Broadleaf weed (BLW) infestation is a major challenge in rice cultivation, particularly during the early vegetative stages when competition for resources is most critical. This study aims to enhance early-stage detection and classification of three prevalent BLW species—Monochoria vaginalis (MV), Limnocharis flava (LF), and Sphenoclea zeylanica (SZ)—in rice fields using unmanned aerial vehicle (UAV)-based hyperspectral imaging integrated with machine learning techniques. The research was conducted in a 1-hectare rice plot (Block L5A, Plot 121) near Pusat Benih Padi Felcra Sdn Bhd, Perak, Malaysia, a site characterized by high weed density. Hyperspectral data were acquired using a DJI Matrice 600 UAV equipped with a Resonon Pika L hyperspectral camera flown at 40 meters altitude. ENVI Classic 5.3 software was used to perform supervised classification based on selected regions of interest (ROIs) for training. Three classification algorithms—Support Vector Machine (SVM), Minimum Distance (MD), and Parallelepiped (PP)—were compared at 15, 25, and 30 days after sowing (DAS). Among them, SVM consistently achieved the highest classification accuracy, exceeding 99% for all weed species across all growth stages, with minimal omission and commission errors. Vegetation cover analysis showed an increasing trend in BLW expansion over time, while rice cover fluctuated and soil cover declined, indicating the competitive dominance of weeds. The findings underscore the effectiveness of UAV hyperspectral imaging combined with machine learning—especially SVM—as a scalable, accurate, and efficient approach for early weed detection. This methodology can support precision agriculture by enabling timely and targeted weed management strategies, ultimately improving rice yield and sustainability.
hyperspectralremote sensingmachine learningrice cropweedThe author(s) declare that no financial support was received for the research and/or publication of this article.section-at-acceptanceSustainable and Intelligent PhytoprotectionIntroduction
Weeds are biological components of agricultural ecosystems and are generally defined as unwanted plants that interfere with human activities or are considered visually undesirable. They represent a significant cause of crop yield loss for farmers (Haq et al., 2024). In rice cultivation, weeds pose the greatest threat as they compete with rice plants for essential resources such as nutrients, light, water, and space (El-Ghandor, 2024). Globally, there are around 30,000 weed species, of which approximately 18,000 are known to cause significant crop losses (Sai et al., 2022). The magnitude of these losses is influenced by multiple factors, including the severity and timing of weed infestation as well as the specific weed species present, since all plants compete for essential resources such as nutrients and water required for growth and establishment (Kanatas et al., 2021; Colbach et al., 2023). Weed infestations can typically reduce rice yields by 10% to 20%, and in cases of severe infestation, the entire crop may be lost (Gao et al., 2022). The degree of damage caused by weeds in rice fields is influenced by factors such as weed species, their density, and the duration of competition with the crop. Additionally, weed persistence and dominance are shaped by the crop type, climate and seasonal conditions, sowing date, and cultivation practices (Ramli et al., 2024).
Broadleaf weeds are a significant concern in rice fields due to their competitive nature and impact on crop yields. Broadleaf weeds frequently have a high seed production rate, allowing them to invade rice fields quickly, and their broad leaves produce extensive shade and can hinder the growth of rice seedlings. This characteristic makes it difficult to control their proliferation once established (Kumar et al., 2017). These weeds, such as Monochoria vaginalis and Lindernia procumbens, can dominate weed communities in rice fields, accounting for up to 60% of the total weed population in some areas. Eclipta prostrata, Mimosa pudica, and Ludwigia hyssopifolia compete aggressively with rice for essential resources. This competition can severely reduce rice yields if not correctly managed. In recent years, changes in rice cultivation practices, particularly the shift toward direct-seeded rice (DSR), have increased the prevalence of broadleaf weeds. The continuous use of grass-specific herbicides in DSR systems has allowed broadleaf weeds and sedges to become more dominant, leading to yield reductions of up to 52-64% (Patidar and Kaur, 2024; Chauhan, 2012).
According to Les (2020), Monochoria is a genus of obligate aquatic herbs that are either annual or facultatively perennial. These plants are well adapted to fluctuating water levels through heterophylly—they produce sessile, linear leaves when submerged and broad-bladed, petiolate leaves when floating or emergent. Monochoria vaginalis (MV), a broadleaf monocot weed common in wetland rice ecosystems, is widespread across major rice-growing areas but is generally absent in drier regions (Athira, 2019). Commonly known as pickerelweed, M. vaginalis is a significant weed in Asian rice fields, competing with rice for essential resources such as nutrients, light, and space (Zhu et al., 2012). It is considered one of the five most prevalent weeds in paddy fields in China (Zhou et al., 2021). This species exhibits rapid growth and is capable of causing up to 44% yield loss (Sujinah et al., 2022). The interference it causes in rice growth and development significantly reduces yield potential (Zhu et al., 2012). Although early-stage M. vaginalis seedlings can be managed with pre-emergence herbicides such as Butachlor, it remains a persistent threat and can cause substantial grain yield losses (Shuman-Goodier et al., 2021). Research has shown that severe infestations can lead to yield reductions of up to 55% (Zhu et al., 2012).
Sphenoclea zeylanica (SZ) Gaertn., commonly known as gooseweed, is an annual herbaceous aquatic weed belonging to the family Sphenocleaceae (Krumsri et al., 2020). It is widely distributed across tropical and warm-temperate regions and is native to the Eastern Hemisphere, including China, Southeast Asia, South Asia, Africa, and Madagascar (Carter et al., 2014). S. zeylanica thrives in both terrestrial and freshwater systems under tropical and warm climates (Vu et al., 2018). It reproduces mainly by seeds, producing large quantities, with most germinating between June and December (Carter et al., 2014). Reddy (2020) reported that the seeds remain viable even after two years of storage under favorable conditions, with germination rates of 6% and 34% at 17 and 48 days after sowing (DAS), respectively.
This weed typically emerges directly from seeds in puddled rice fields and spreads rapidly due to its high seed production capacity. Its adaptability to both waterlogged and moist conditions, including germination under submerged environments, makes it a serious challenge in rice cultivation (Reddy, 2020). The methanol extract of S. zeylanica has been found to suppress the shoot and root growth of several weed species such as Leptochloa chinensis, Chloris barbata, Dactyloctenium aegyptium, Pennisetum pedicellatum, Pennisetum setosum, Hygrophila erecta, Mimosa invisa, Hyptis suaveolens, and Scirpus articulatus (Krumsri et al., 2020). It is considered one of the world’s worst rice weeds, found in at least 28 countries, and is also a problem in cotton, wheat (especially in rice-wheat rotations), and soybean fields. This fast-growing annual weed can reduce rice yields by 45–50%, disrupt harvesting, and delay drying when its biomass remains green and succulent (Rojas-Sandoval, 2023).
Limnocharis flava (LF) is another problematic emergent aquatic weed. Native to tropical and subtropical America, it has become invasive in several Asian countries, including Malaysia, Indonesia, Thailand, Vietnam, Sri Lanka, and India (Nishan and George, 2018). It is a perennial herbaceous plant that can grow up to one meter in height (Zakaria et al., 2018). Belonging to the Limnocharitaceae (or Alismataceae) family, L. flava commonly invades irrigation canals and water systems, where it competes with native aquatic flora for light and nutrients, leading to ecological imbalance (Zakaria et al., 2021). It features glabrous stems and a scapigerous structure, with broad leaves that are rounded to ovate, sheathing with wavy edges and forming clusters above the water. The petioles are triangular with well-developed air spaces (Nishan and George, 2018; Putra et al., 2023). Although initially introduced as an ornamental species due to its attractive foliage and flowers, its aggressive growth and persistence have made it an invasive threat (Song et al., 2024).
In Malaysia and other parts of Southeast Asia, Limnocharis flava has become a serious weed in rice fields and irrigation systems. It has developed resistance to both synthetic auxin herbicides and acetohydroxyacid synthase (AHAS) inhibitors (Zakaria et al., 2021). According to Nishan and George (2018), direct-seeded rice is more affected by this weed than transplanted rice. The plant thrives on fertilized soils, grows rapidly, and has, in some cases, led to rice field abandonment due to uncontrollable infestations. It clogs irrigation channels and drainage systems, reducing water flow and leading to water stagnation in lower areas. During heavy rainfall, it further exacerbates crop damage by obstructing water outflow, causing field submergence (Seneviratne and Wijesundara, 2004; Nishan and George, 2018).
Hyperspectral imaging and machine learning for weed analysis
Precision weed management integrates cultural, mechanical, and chemical control methods to minimize herbicide use while reducing environmental impact. This balanced approach ensures effective weed control while protecting crop plantations (Agrawal, 2024; Monteiro and Santos, 2022). Precision weed control serves as a bridge between conventional chemical control and automated mechanical weed control, offering a more targeted and sustainable alternative. By identifying specific weed patches, herbicide application can be precisely localized, reducing unnecessary chemical use and improving efficiency. Accurate weed detection is essential for this strategy to be effective (Che'Ya, 2016).
Hyperspectral imaging (HSI) is an integration of modern imaging systems and traditional spectroscopy techniques (Sulaiman et al., 2022). It enables the acquisition of spectral information for each pixel in an image, providing a detailed spectral profile (Bhargava et al., 2024). While RGB imaging has demonstrated high performance in many weed detection applications, particularly for morphologically distinct weeds, its effectiveness can be limited during early growth stages of crop cultivation (Mensah et al., 2024). At these stages, rice plants and broadleaf weeds often share similar morphological and color characteristics, making them difficult to separate based on RGB features alone. Hyperspectral imaging (HSI) provides hundreds of contiguous spectral bands, enabling detection of subtle differences in pigment composition, water content, and internal leaf structure that are not visible to RGB sensors (Pérez-Ortiz et al., 2016). This enhanced spectral resolution allows for more accurate species‐level discrimination under challenging conditions and supports earlier, more targeted weed management interventions.
Machine learning (ML) methods offer a variety of classification algorithms for analyzing remote sensing imagery and have proven effective in identifying unwanted species using remote sensing (Papp et al., 2021). Generally, the ML framework involves learning from ‘experience’, known as training data, to execute the classification, regression, or clustering tasks. These training data are usually regarded as a feature described by a set of attributes or variables. The machine learning model works by predicting the pattern and trend of future events in crop monitoring and assessment (Zhang et al., 2019). The ML model’s performance in a particular task is evaluated by performance metrics that improve with experience over time. As a result, classification techniques have been a prominent research trend in machine learning for many years, informing various studies. This method seeks to create features from the input data. Furthermore, it is highly field-specific and requires significant human effort, leading to deep learning techniques (Alzubaidi et al., 2021).
Supervised classification model trainingSupport vector machine
Support Vector Machine (SVM) is a robust classification algorithm that aims to find the optimal hyperplane that best separates data points of different classes. It is particularly effective in high-dimensional spaces and can handle both linear and non-linear classification problems by utilizing kernel functions. The SVM algorithm enhances classification accuracy by maximizing the margin between different class boundaries, contributing to robust generalization in the spectral classification of plant species (Ballanti et al., 2016).
Minimum distance
The Minimum Distance classifier is a simple yet effective algorithm that calculates the spectral distance between each pixel and the mean spectral signature of each class. It assigns pixels to the class with the smallest distance, making it suitable for datasets with well-separated class means (Rose and Seldev, 2014). While less sophisticated than SVM, it offers faster processing times and can be advantageous in scenarios where spectral variability within classes is low.
Parallelepiped
The Parallelepiped classifier uses a set of spectral thresholds defined by the minimum and maximum values of each class’s training data. Pixels falling within the defined spectral “box” are classified accordingly (De Moraes, 2004). This method is computationally efficient and helpful when the spectral ranges of classes are well-defined. However, its reliance on strict boundaries may lead to misclassification in cases of spectral overlap or high variability within classes.
The Parallelepiped classifier uses a set of spectral thresholds defined by the minimum and maximum values of each class’s training data. Pixels falling within the defined spectral “box” are classified accordingly. This method is computationally efficient and helpful when the spectral ranges of classes are well-defined. However, its reliance on strict boundaries may lead to misclassification in cases of spectral overlap or high variability within classes.
By using UAV-based hyperspectral imaging combined with a machine learning technique, this study aims to enhance the detection and classification of the broadleaf weeds (BLW) in rice fields at an early growth stage (Vegetative stage). The selected broadleaf weed species used in this study were Monochoria vaginalis, Limnocharis flava, and Sphenoclea zeylanica. The selection of these species of weeds as the focus of this study was based on their prevalence and significant impact in the study area. The study leverages ENVI Classic 5.3 software for supervised classification, where regions of interest (ROIs) are selected for training the classifier to recognize the spectral signatures of the three targeted weed species and the rice plants. Ultimately, the goal of this research is toward:
Implements and compares multiple supervised classification methods (SVM, Minimum Distance, Parallelepiped) to identify the optimal algorithm for early-stage weed discrimination.
Demonstrates the feasibility of UAV-HSI for scalable, non-destructive, high-accuracy weed mapping, which can reduce herbicide use and support sustainable rice production.
Methods and procedureStudy site
The experiment for this study was carried out in a rice field near Pusat Benih Padi Felcra Sdn Bhd (Perak) managed by FELCRA Plantation Services Sdn. Bhd. is located in Bandar Seberang Perak, Kampung Gajah, Perak, Malaysia (postcode 36800). The region’s latitude and longitude are roughly 4.1263° N and 100.9789° E. Block L5A, plot 121 was chosen for the experiment site due to its proximity to the central irrigation canal and the high weed pressure. This particular rice field location is noted for its good soil and favorable environment for rice production. The crop cultivation management program followed the FELCRA procedure which is based on the Rice Check Padi Edisi 2022 guidelines issued by the Department of Agriculture (DOA), Malaysia (Department of Agriculture Malaysia (DOA), 2022). The place has a tropical rainforest climate with consistently high humidity and temperatures throughout the year. The average annual rainfall is approximately 2000–2500 mm, promoting rice farming while creating an environment suitable for weed development. This region’s soil is alluvial mainly, nutrient-rich, and capable of retaining water. These traits are crucial for rice farming, but they also promote the growth of many weeds.
Weed analysis plots were established in three different areas, selected based on preliminary surveys assessing the density of the target broadleaf weed species; Limnocharis flava (LF), Monochoria vaginalis (MV), and Sphenoclea zeylanica (SZ). Density assessments were conducted using hyperspectral image analysis to identify plots with adequate representation of each species. Details of the study plots are described below (Figure 1; Table 1).
Study site for broadleaf weed in rice field at Felcra Berhad (Perak).
Map of Malaysia highlighting the location of Bukit Kacang in Perak. A red arrow points to an enlarged aerial view of Study Area Blok L5A in Felcra, Perak. This includes three numbered sections labeled 1, 2, and 3, with corresponding zoomed-in images on the right showing detailed land plots. A compass rose is present for orientation.
Info on study Plot at Blok l5A, Felcra Perak.
No.
Plot
Broadleaf weed on focus
Plot area (m2)
1.
Plot 1
Limnocharis flava
2300
2.
Plot 2
Monochoria vaginalis
2500
3.
Plot 3
Sphenoclea zeylanica
2200
Acquisition of hyperspectral imagery
The image acquisition procedure for hyperspectral data collection involves three main stages: preparation and calibration, field operation, and data retrieval. Hyperspectral data was collected by using a UAV (DJI Matrice 600) and the hyperspectral camera, Resonon Pika L (captures data in the 400–1000 nm spectral range with 281 spectral bands). The UAV-based imaging system includes (i) a Resonon Pika L hyperspectral camera (Spectronon Pro, Resonon, Bozeman, MT); (ii) visible-near infrared (V-NIR) objective lenses for the Pika L camera with a focal length of 23 mm, field of view (FOV) of 13.1 degrees, and instantaneous field of view (IFOV) of 0.52 mrad; and (iii) a global positioning system (GPS) and the inertial measurement unit IMU (DJI) flight control system for multi-rotor aircraft, to record sensor position and orientation. Data was collected at 40 meters above the ground.
Time of Day (TOD) refers to the specific period selected for aerial imaging, taking into account lighting conditions, sun angle, and shadows, all of which influence the quality of captured images (Barbosa Júnior et al., 2022). Although research on the direct effects of TOD is limited, the most commonly recommended imaging windows are between 9:00 a.m. and 2:00 p.m., 10:00 a.m. and 3:00 p.m., or 11:00 a.m. and 2:00 p.m (Bongomin et al., 2024; Gonçalves et al., 2022; Mao et al., 2021). These timeframes align with optimal sunlight conditions, providing stable lighting and minimal atmospheric disturbances, which are critical for obtaining high-quality spectral data in vegetation analysis. Proper TOD selection helps ensure consistent and accurate remote sensing measurements, reducing errors caused by shadows or variable light intensity. Data collection schedule is described in Table 2.
The camera was mounted on a DJI Ronin-MX gimbal, and the imager was activated via a Resonon flight computer connected to a GPS receiver, using a target map generated in Google Earth. During flight, the Resonon Pika L sensor captured hyperspectral images, while GPS coordinates and flight metadata were recorded for accurate georeferencing. Data was stored on the sensor’s onboard system or transferred in real time to the ground control station. After landing, the hyperspectral data was retrieved, backed up, and prepared for analysis to ensure accuracy and reliability in weed detection.
The hyperspectral raw data obtained are spectrally calibrated but are not corrected for the illumination functions, so the result cannot be directly interpreted. The image capture from the hyperspectral sensor was pre-processed using Spectronon Pro (Resonon). The spectrum of the image will be used to distinguish the spectral signature between rice plants and the broadleaf weeds. The spectral signature was used to identify the selected broadleaf weed species in rice crops for real-time detection of the broadleaf weeds in the rice fields. The region of interest was chosen randomly from the developed 2D maps, and the files were saved in KML format.
Hyperspectral imagery processing workflow
The methodology flow chart of weed detection analysis is described as Figure 2 below. First step in the supervised classification process involves preprocessing the hyperspectral data. This is to ensures the data quality and prepares it for further analysis. After completing image correction and image mosaicking in SpectrononPro, the image data can be subset to target area in Envi Classic 5.3.
Methodology flow chart for hyperspectral image supervised classification analysis.
Flowchart illustrating hyperspectral image processing and analysis. Starts with raw image data acquisition by Resonon Pika L*M600. Steps include image correction (white reference correction, radiance, reflectance, georectifying), image mosaicking, and ROI selection/classification (support vector machine, minimum distance, parallelepiped). Class labels include broadleaf weed, rice plant, soil, water, and mixed vegetation. Accuracy assessment uses cross-validation, confusion matrix, and Kappa coefficient, leading to output and analysis.Image calibration correction
The process of image correction for hyperspectral images captured using the Resonon Pika L sensor involves several essential steps to ensure the data’s accuracy and usability for analysis, particularly for vegetation and weed detection in rice fields (Figure 3). This pre-processing procedure is run using SpectrononPro software.
Image calibration correction process flow.
Flowchart illustrating the process of data handling. Starts with “Image Correction” depicted with colored circles, followed by steps including “White Reference Correction,” “Radiance from Raw Data,” “Reflectance from Radiance Data,” and “Georectify Airborne Datacube,” each in colored arrows. Ends with a red circle labeled “Data Integration and Data Analysis."
i. White Reference Correction: This step involves calibrating the sensor to account for varying lighting conditions and sensor sensitivity. A white reference panel, which has a uniform reflectance spectrum, is used during the data acquisition process. By normalizing the hyperspectral data against the white reference, variations caused by lighting inconsistencies or sensor noise are minimized. Denoising techniques improve signal-to-noise ratio and classification accuracy in hyperspectral imagery, making them a valuable preprocessing step for improved analysis (Rasti et al., 2018).
ii. Radiance from Raw Data: Intermediate-form hyperspectral image data can be processed directly or converted into radiance values and estimates of signal-dependent noise, benefiting image analysis (Skauli, 2009). The raw data acquired by the hyperspectral sensor represents digital counts. These digital counts are converted into radiance values, which express the actual energy measured by the sensor in physical units. This conversion accounts for the sensor’s calibration factors and the spectral response, ensuring that the data is meaningful for further analysis.
iii. Reflectance from Radiance Data and Spectrally Flat Reference Spectrum: Hyperspectral image exploitation algorithms require reflectance spectra retrieved from observed radiance spectra (Golowich et al., 2018). Radiance data is then converted into reflectance values, which are crucial for identifying vegetation features. This is done by dividing the radiance data by the radiance of the spectrally flat reference (e.g., the white reference panel) captured during the calibration process. Reflectance values eliminate the effects of varying light intensity and are used to derive the actual spectral signatures of the target vegetation and weeds.
iv. Georectify Airborne Datacube: The hyperspectral data, captured in the form of a hypercube (spatial and spectral dimensions), is georectified to align with real-world geographic coordinates. This involves integrating GPS and inertial measurement unit (IMU) data collected during the flight. The hyperspectral mapping system, which includes a pushbroom spectrometer, photogrammetric camera, and GPS-INS, can georectify pushbroom data fully automatically for agricultural mapping and monitoring applications (Suomalainen et al., 2014).
v. Data Analysis: Advanced data analysis techniques, such as machine learning or spectral unmixing, are applied to the corrected data. This step identifies spectral signatures unique to weeds and crops, enabling accurate classification and mapping of vegetation. This systematic image correction process ensures that hyperspectral images are accurate, reliable, and ready for use in weed detection and vegetation analysis.
Region of interest selection
In the weed detection analysis using ENVI Classic 5.3, the Region of Interest (ROI) selection is a crucial step in the supervised classification process. The ROI represents specific areas within the hyperspectral image that serve as samples for training and validating the classification algorithm. For this study, the ROI classes included (Table 3):
ROI set for image analysis.
No.
Color
Class
1.
Red
Broadleaf weed (Limnocharis flava, Monochoria vaginalis, and Sphenoclea zeylanica)
2.
Green
Rice plant
3.
Purple
Mixed vegetation (area containing a combination of plant species)
4.
Blue
Water (visible water bodies or waterlogged area)
5.
Yellow
Soil (bare soil regions)
The ROI creation process involved preparing the hyperspectral image and utilizing the ROI tool in ENVI Classic 5.3 to manually or semi-automatically select representative regions. These regions were labeled according to their respective classes, ensuring a diverse and accurate representation of the study area (refer to Table 4).
Number of ROI created for hyperspectral image supervised classification analysis.
No.
Cycle/DAS
Class
No. of polygon (ROI)
No. of training ROI (60%)
No. of validation ROI (40%)
Total pixel count
Plot 1: Limnocharis Flava
1.
Cycle 1/15DAS
BLW_LF
60
36
24
653
2.
Rice Plant
70
42
28
954
3.
Water
20
12
8
109
4.
Soil
20
12
8
90
5.
Mixed Vegetation
20
12
8
69
6.
Cycle 2/25DAS
BLW_LF
60
36
24
463
7.
Rice Plant
60
36
24
750
8.
Water
20
12
8
53
9.
Soil
20
12
8
77
10.
Mixed Vegetation
20
12
8
88
11.
Cycle 3/30DAS
BLW_LF
40
24
16
280
12.
Rice Plant
50
30
20
560
13.
Water
15
9
6
109
14.
Soil
15
9
6
43
15.
Mixed Vegetation
15
9
6
42
Plot 2: Monochoria vaginalis
16.
Cycle 1/15DAS
BLW_MV
35
21
14
141
17.
Rice Plant
65
39
26
270
18.
Water
20
12
8
110
19
Soil
30
18
12
191
20.
Mixed Vegetation
20
12
8
88
21.
Cycle 2/25DAS
BLW_MV
30
18
12
254
22.
Rice Plant
35
21
14
306
23.
Water
20
12
8
268
24.
Soil
20
12
8
195
25.
Mixed Vegetation
20
12
8
172
26.
Cycle 3/30DAS
BLW_MV
35
21
14
377
27.
Rice Plant
35
21
14
397
28
Water
15
9
6
181
29.
Soil
15
9
6
299
30.
Mixed Vegetation
15
9
6
241
Plot 3: Sphenoclea zeylanica
31.
Cycle 1/15DAS
BLW_SZ
35
21
14
114
32.
Rice Plant
40
24
16
204
33.
Water
20
12
8
149
34.
Soil
20
12
8
178
35.
Mixed Vegetation
20
12
8
114
36.
Cycle 2/25DAS
BLW_SZ
40
24
16
329
37.
Rice Plant
50
30
20
357
38.
Water
25
15
10
198
39.
Soil
25
15
10
302
40.
Mixed Vegetation
20
12
8
175
41.
Cycle 3/30DAS
BLW_SZ
50
30
20
519
42.
Rice Plant
45
27
18
874
43.
Water
25
15
10
705
44.
Soil
25
15
10
644
45.
Mixed Vegetation
20
12
8
340
A critical aspect of the ROI strategy was the distribution of samples into 60% for training and 40% for validation (Sivakumar et al., 2024). The training samples were used to “teach” the classification algorithm, providing known spectral signatures for each class. Meanwhile, the validation samples, which were withheld from the training process, were used to assess the model’s accuracy and its ability to generalize to new, unseen data (Xu and Goodacre, 2018).
Classification execution, validation, and accuracy assessment
Once the training data (ROIs) and algorithm are selected, the classification is executed to assign each pixel in the hyperspectral imagery to one of the predefined classes. ENVI Classic 5.3 generates a classified image in which each pixel is color-coded according to its assigned class. Validation ensures the reliability of the classification results. ENVI Classic 5.3 provides tools for quantitative accuracy assessment:
Confusion Matrix: Compares the classified image with ground truth data, providing metrics like overall accuracy, user’s accuracy (UA), and producer’s accuracy (PA) (as described in Table 5).
Kappa Coefficient: Measures the agreement between classification results and ground truth, accounting for chance agreement.
The definition of UA and PA (Congalton, 1991).
Metric
Definition
Formula
User’s Accuracy (UA)
Probability that a pixel classified into a given class represents that class on the ground (reliability).
These metrics provide a quantitative evaluation of the classification performance, allowing researchers to identify and address potential shortcomings in the workflow. Performance metrics such as precision, recall, and F1-score are also computed to assess the effectiveness of the classification model.
Results and analysis
This analysis evaluates the classification performance of three supervised classification techniques—Support Vector Machine (SVM), Minimum Distance (MD), and Parallelepiped (PP)—for distinguishing between the selected Broadleaf Weed, Rice Plant, Mixed Vegetation, Soil, and Water at 15 DAS, 25 DAS, and 30 DAS using ENVI Classic 5.3.
Detection of broadleaf weed: <italic>Limnocharis flava</italic>
The classification performance for Limnocharis flava (Blw_Lf) using UAV-based hyperspectral imagery across different growth stages shows a clear trend in favor of the Support Vector Machine (SVM) classifier (Figure 4). At 15 DAS (Table 6), SVM already achieves a high producer accuracy (90.85%) and user accuracy (99.85%), indicating it is highly capable of identifying and correctly labeling LF even at an early stage. In contrast, the Minimum Distance method provides lower performance with 69.86% producer accuracy and 93.65% user accuracy. In comparison, Parallelepiped yields better producer accuracy (77.95%) but a much lower user accuracy (35.87%), suggesting high misclassification of other classes as LF.
LF detection in rice field implementing supervised classification algorithm.
Comparison of land cover classification methods using maps at three different times (15, 25, and 30 days after sowing) across three methods: SVM, MD, and PP. Colors indicate categories: red for broadleaf weed, green for rice, dark green for mixed vegetation, yellow for soil, blue for water, and black for unclassified areas.
Comparison of producer accuracy (PA) and user accuracy (UA) for each algorithm for plant classification (Limnocharis flava) at 15, 25, and 30DAS.
Classifier/ROI class
SVM
MD
PP
Class
Prod. acc. (%)
User acc. (%)
Prod. acc. (%)
User acc. (%)
Prod. acc. (%)
User acc. (%)
PA and UA at 15DAS
Blw_LF
99.85
99.85
85.3
98.76
83.46
97.15
Rice
100
99.9
88.68
99.65
79.35
99.87
Water
100
100
100
73.65
81.65
100
Soil
100
100
100
94.74
84.44
100
Mixed Vegetation
97.1
98.53
91.3
28.77
53.62
92.5
PA and UA at 25DAS
Blw_LF
100
99.78
80.78
78.08
85.96
87.09
Rice
100
100
84.53
98.45
81.07
100
Water
100
100
100
88.33
58.49
100
Soil
100
100
77.92
66.67
90.91
93.33
Mixed Vegetation
98.86
100
90.91
50.63
48.86
100
PA and UA at 30DAS
Blw_LF
99.29
99.64
87.14
99.59
83.57
90
Rice
100
98.42
81.96
95.43
73.04
99.51
Water
100
100
100
98.2
65.14
100
Soil
79.07
100
62.79
19.57
81.4
39.33
Mixed Vegetation
100
97.67
85.71
61.02
78.57
89.19
At 25 DAS (Table 6), SVM further improves, achieving perfect scores (100%) in both producer and user accuracy for Blw_Lf, which reflects its robustness and reliability. Minimum Distance also shows improved results (80.78% PA and 78.08% UA), and Parallelepiped performs moderately better (85.96% PA, 87.09% UA), but both still lag behind SVM. By 30 DAS, SVM maintains its high accuracy, with a producer accuracy of 99.29% and user accuracy of 99.64%. Minimum Distance also performs well (87.14% PA and 94.15% UA), while Parallelepiped records good results (83.57% PA, 87.67% UA), though still not as consistently accurate as SVM.
Comparative analysis of classification models for LF detection
Table 7 presents the classification performance of SVM, MD, and PP classifiers for detecting Limnocharis flava at three different growth stages: 15 Days After Sowing (DAS), 25 DAS, and 30 DAS. The analysis is based on Overall Accuracy, Kappa Coefficient, Mean, and Standard Deviation (StDev).
Classification analysis of LF at 15DAS, 25DAS, 30DAS.
Species/DAS
Limnocharis Flava_15DAS
Limnocharis Flava_25DAS
Limnocharis Flava_30DAS
Classifier
Overall accuracy
Kappa coefficient
Mean
StDev
Overall accuracy
Kappa Coefficient
Mean
StDev
Overall accuracy
Kappa coefficient
Mean
StDev
Support Vector Machine
99.84
0.99
2.9
1.6
99.96
0.99
3.11
1.72
99.26
0.99
2.68
1.59
Minimum Distance
91.02
0.88
3.02
1.65
89.81
0.86
3.26
1.7
84.62
0.77
2.58
0.92
Parallelepiped
80.213
0.71
3.02
1.65
87.54
0.83
3.03
1.65
75.63
0.65
1.54
1.24
SVM consistently demonstrates the highest classification performance across all time points. The overall accuracy remains above 99%, with a Kappa coefficient close to 1.0, indicating excellent classification agreement. The mean values range between 2.68 and 3.11, with relatively low standard deviation (StDev), suggesting that SVM maintains high stability and precision throughout different growth stages.
At 15 DAS, the overall accuracy is 99.84%, with a Kappa coefficient of 0.9974, indicating a nearly perfect classification. The accuracy further improves slightly at 25 DAS (99.96%), and remains very high at 30 DAS (99.26%). The slight variations in standard deviation suggest that SVM maintains a consistent and robust classification, making it the most reliable model for spectral classification of Limnocharis flava.
MD performs moderately well but exhibits lower classification accuracy than SVM. The overall accuracy drops from 91.02% (15 DAS) to 89.81% (25 DAS) and further declines to 84.62% (30 DAS). Similarly, the Kappa coefficient declines from 0.8783 at 15 DAS to 0.77 at 30 DAS, indicating increased misclassification at later growth stages. The mean values fluctuate between 2.58 and 3.25, with a higher standard deviation compared to SVM, implying that MD is more prone to spectral variability. The decline in accuracy at 30 DAS suggests that MD struggles with classifying mixed vegetation and soil, possibly due to increased spectral complexity as the plants mature.
PP shows the weakest classification performance, with significantly lower accuracy and higher variability. The overall accuracy starts at 80.21% at 15 DAS, drops to 87.54% at 25 DAS, and declines further to 75.63% at 30 DAS. The Kappa coefficient follows a similar trend, dropping from 0.711 to 0.65, indicating substantial classification errors and increased misclassification.
The mean values are consistently lower (between 1.54 and 3.03), and the standard deviation is higher compared to SVM, highlighting that PP lacks robustness and stability. The lower accuracy at 30 DAS suggests that PP struggles significantly with separating Limnocharis flava from other vegetation and soil.
Vegetation cover analysis in rice _LF
The vegetation cover analysis in the rice field, based on SVM classification, provides key insights into the dynamics of different land cover categories—BLW_LF, Rice, Mixed Vegetation, Water, and Soil—at three growth stages: 15 Days After Sowing (DAS), 25 DAS, and 30 DAS (Figure 5). The first chart illustrates the percentage distribution of vegetation cover across these time points, while the second chart highlights the rate of change in cover percentage from 15 DAS to 30 DAS.
Vegetation class cover distribution (%) for Plot 1_LF.
Bar graph titled “Vegetation Class Cover Distribution (%)_LF Plot” shows percentage distributions of Blw_LF, Rice, Mix_Vege, Water, and Soil at 15, 25, and 30 Days After Sowing (DAS). Rice consistently dominates, reaching 85.22% at 30DAS, while others remain below 10%. Distinguished by colors red, green, dark green, blue, and brown, respectively.
The results in Figure 6 indicate that rice vegetation exhibited the most significant increase over time, starting at 74.93% at 15 DAS, rising slightly to 75.96% at 25 DAS, and reaching a peak of 85.22% at 30 DAS. This 10.29% increase in rice coverage suggests successful crop establishment and dominance in the field, likely suppressing competing plant species. In contrast, broadleaf weed (BLW_LF) presence increased gradually, from 6.02% at 15 DAS to 8.1% at 25 DAS, and further to 9.6% at 30 DAS. This 3.58% increase implies that while rice growth is dominant, some weed expansion still occurs, highlighting the need for early weed management interventions.
Vegetation class cover distribution (%) for Plot 2_MV.
Bar chart titled “Vegetation Class Cover Distribution (%) MV Plot,” showing percentages of five categories: Blw_MV, Rice, Water, Soil, and Mix_Vege at 15, 25, and 30 DAS. Rice consistently has the highest percentage across all days, followed by Soil. Mix_Vege and Blw_MV show gradual changes. Water decreases over time.
A significant trend observed is the drastic reduction in mixed vegetation coverage, which declined from 7.71% at 15 DAS to 5.74% at 25 DAS, and finally to just 1.42% at 30 DAS (Figure 7). This 6.29% decrease indicates that as rice plants grow and canopy coverage expands, mixed vegetation species are either suppressed, absorbed into other classes, or outcompeted. A similar downward trend is seen in water and soil coverage. Water coverage shrank from 4.76% at 15 DAS to 2.22% at 25 DAS, and further to 1.89% at 30 DAS, reflecting potential field drainage, reduced irrigation, or increasing vegetation coverage over previously exposed water surfaces. Likewise, soil exposure increased slightly between 15 DAS (6.58%) and 25 DAS (7.98%), but then sharply declined to 1.87% at 30 DAS, suggesting that as rice and weeds grow, they effectively cover the bare soil, reducing its visibility in hyperspectral classification.
Vegetation cover rate changes (%) for LF in 15DAS – 30DAS.
Bar chart showing vegetation cover rate changes in percentage from 15DAS to 30DAS_LF. Categories include Blw_LF (3.58%), Rice (10.29%), Mix_Vege (-6.29%), Water (-2.87%), and Soil (-4.71%).
From a comparative perspective, rice exhibited the highest rate of positive change (+10.29%), followed by broadleaf weeds (+3.58%), while mixed vegetation (-6.29%), water (-2.87%), and soil (-4.71%) all experienced declines. The increase in rice dominance and reduction in other classes indicates that proper crop establishment can effectively outcompete weeds and other vegetation types, but the persistent increase in broadleaf weeds suggests the need for improved weed management strategies to minimize competition for nutrients and space.
Detection of broadleaf weed: <italic>Monochoria vaginalis</italic>
The classification results for Monochoria vaginalis (MV) using three algorithms, which are SVM, MD, and PP, at three different growth stages (15, 25, and 30 DAS) demonstrate varying levels of accuracy in hyperspectral image analysis (Figure 8). Overall, SVM consistently outperforms the other methods across all stages. At 15 DAS (Table 8), SVM achieves high producer accuracy (92.91%) and user accuracy (97.04%) for Blw_MV, indicating excellent detection and reliability. In contrast, Minimum Distance shows lower producer accuracy (63.12%) and user accuracy (77.39%), while Parallelepiped performs moderately in producer accuracy (84.40%) but poorly in user accuracy (39.27%), suggesting a high rate of false positives.
MV detection in a rice field implementing a supervised classification algorithm.
Three rows of satellite images show land classification using SVM, MD, and PP methods over time: MV_15DAS, MV_25DAS, and MV_30DAS. The images depict areas colored according to a legend: red for broadleaf weed, light green for rice, dark green for mixed vegetation, yellow for soil, blue for water, and black for unclassified.
Comparison of producer accuracy and user accuracy for each algorithm for plant classification (Monochoria vaginalis) at 15, 25, and 30DAS.
Classifier/ROI class
SVM
MD
PP
Class
Prod. acc. (%)
User acc. (%)
Prod. acc. (%)
User acc. (%)
Prod. acc. (%)
User acc. (%)
PA and UA at 15DAS
Blw_MV
92.91
97.04
63.12
77.39
84.40
39.27
Rice
97.04
95.27
90.37
79.48
33.70
86.67
Water
100
100
100
96.49
72.73
100
Soil
100
100
72.25
89.61
79.58
82.61
Mixed Vegetation
90.91
90.91
56.82
45.45
36.36
91.43
PA and UA at 25DAS
Blw_MV
96.46
92.11
79.92
80.56
83.86
70.30
Rice
89.54
85.09
76.47
83.27
74.84
77.63
Water
100
100
100
94.04
67.16
100
Soil
98.46
98.97
84.62
97.06
87.69
68.67
Mixed Vegetation
73.84
87.59
65.12
54.11
15.12
61.90
PA and UA at 30DAS
Blw_MV
96.55
99.45
91.51
95.04
83.82
79.60
Rice
89.20
77.01
51.76
56.13
72.61
58.03
Water
100
100
100
92.82
74.03
100
Soil
100
100
95.00
78.26
80.00
99.31
Mixed Vegetation
63.81
80.00
33.46
40.76
5.45
66.67
At 25 DAS (Table 8), classification accuracy improves for all methods. SVM remains the best performer with a producer accuracy of 96.46% and user accuracy of 92.11% for Blw_MV. Minimum Distance also improves (79.92% and 80.56%, respectively), and Parallelepiped shows better results compared to 15 DAS, although its accuracy (83.86% PA, 70.30% UA) still trails behind SVM. By 30 DAS, the classification performance peaks. SVM achieves nearly perfect accuracy for Blw_MV, with 96.55% producer accuracy and 99.45% user accuracy. Minimum Distance also shows high accuracy (91.51% PA, 95.04% UA), while Parallelepiped, although improved, remains the least reliable (83.82% PA, 79.60% UA).
Comparative analysis of classification models for MV detection
Table 9 presents the classification performance of SVM, MD, and PP classifiers for detecting MV at three different growth stages: 15 DAS, 25 DAS, and 30 DAS. The analysis is based on Overall Accuracy, Kappa Coefficient, Mean, and Standard Deviation (StDev).
Classification analysis of at 15DAS, 25DAS, 30DAS.
Species/DAS
MV_15DAS
MV_25DAS
MV_30DAS
Classifier
Overall accuracy
Kappa coefficient
Mean
StDev
Overall accuracy
Kappa coefficient
Mean
StDev
Overall accuracy
Kappa coefficient
Mean
StDev
SVM
97.77
0.97
3.50
1.65
95.06
0.94
3.6
1.6
95.01
0.93
3.67
1.61
MD
85.49
0.82
3.59
1.62
88.17
0.85
3.57
1.6
86.16
0.81
3.92
1.45
PP
72.02
0.66
2.88
2.14
79.11
0.74
3.22
1.8
82.18
0.75
2.77
2.02
Among the three classifiers, SVM consistently achieved the highest accuracy, with 97.77% at 15 DAS, 95.06% at 25 DAS, and 95.01% at 30 DAS. This high accuracy is supported by a Kappa coefficient above 0.93 for all time intervals, indicating strong agreement between the predicted and actual classifications. The MD classifier performed moderately well, with an accuracy of 85.49% at 15 DAS, 88.17% at 25 DAS, and 86.16% at 30 DAS, along with a Kappa coefficient ranging from 0.807 to 0.851. However, PP had the lowest accuracy, with 72.02% at 15 DAS, 79.11% at 25 DAS, and 82.18% at 30 DAS, and a significantly lower Kappa coefficient (ranging from 0.657 to 0.753), indicating weaker classification reliability.
The mean values indicate the general effectiveness of each classifier, with SVM maintaining the highest mean values (3.5 at 15 DAS, 3.6 at 25 DAS, and 3.7 at 30 DAS). MD has relatively stable mean values, though slightly lower than SVM, while PP consistently shows the lowest mean values, reflecting its weaker classification performance. The standard deviation (StDev) values provide insight into classification stability, with SVM having the lowest standard deviation values across all time points, signifying more consistent classification performance. MD and PP show higher standard deviation values, particularly PP, which exhibits the highest variation in classification performance, suggesting that its results are more inconsistent.
Vegetation cover analysis
The vegetation cover analysis in the rice field using Support Vector Machine (SVM) classification at 15 DAS, 25 DAS, and 30 DAS provides insights into the distribution and rate of change of different vegetation classes over time (Figure 6). The first bar chart illustrates the percentage cover of various land cover classes—Monochoria vaginalis (Blw_MV), Rice, Water, Soil, and Mixed Vegetation—at different growth stages, while the second bar chart highlights the rate of change (%) in vegetation cover between the different time intervals.
At 15 DAS, rice dominates the plot, covering 45.65% of the area, followed by soil (25.60%), water (9.64%), mixed vegetation (11.85%), and broadleaf weed Monochoria vaginalis (7.26%). As the rice matures at 25 DAS, its cover decreases significantly to 34.79%, indicating competition with other vegetation classes or changes in spectral response due to canopy development. During this period, mixed vegetation expands from 11.85% to 20.10%, and water coverage increases from 9.64% to 15.54%, likely due to water management practices in the rice field. By 30 DAS, rice cover increases slightly to 35.92%, while mixed vegetation remains high at 19.37%, and soil coverage increases substantially to 29.82%. Water cover, on the other hand, decreases significantly to 6.19%, suggesting a reduction in flooded areas as the rice field transitions into later growth stages.
Figure 9 provides a clearer view of the rate of change (%) in vegetation cover between time intervals. The broadleaf weed (Blw_MV) cover slightly increases by 1.44% over time, indicating gradual weed spread, which may become a concern for rice productivity if not controlled. Rice cover exhibits the most significant decline (-9.73%), primarily between 15 DAS and 25 DAS, possibly due to increased weed competition, water dynamics, or spectral confusion in the classification process. Water cover also decreases (-3.45%), reflecting reduced flooded areas as the rice field progresses toward maturity and water levels stabilize. In contrast, soil cover increases by 4.22%, indicating more exposed areas, possibly due to drying conditions or changes in crop structure. Mixed vegetation experiences the highest positive change (7.51%), suggesting the expansion of non-rice vegetation, including weeds and other plant species, which could affect rice growth if not managed effectively.
Vegetation cover rate changes (%) for MV in 15DAS – 30DAS.
Bar chart showing vegetation cover rate changes in percentages. Categories include Blw_MV with 1.44%, Rice with -9.73%, Water with -3.45%, Soil with 4.22%, and Mix_Vege with 7.51%. Positive values indicate increases, while negative values indicate decreases.
Rice cover decreases significantly between 15 DAS and 25 DAS, followed by a slight recovery at 30 DAS. This may indicate a competitive effect from weeds or changes in the spectral reflectance of rice at different growth stages. Mixed vegetation increases sharply, particularly between 15 DAS and 25 DAS. This suggests that non-rice plant species are establishing dominance, requiring early intervention strategies for effective weed control.
Water cover initially increases from 15 DAS to 25 DAS but declines sharply by 30 DAS. This may be due to changes in field management practices, such as water drainage or evaporation. Soil exposure increases, particularly between 25 DAS and 30 DAS. This could be linked to drier field conditions or the reduction of water levels, leading to increased bare soil detection. Broadleaf weed cover is increasing steadily over time, suggesting the potential for more aggressive weed infestation if not controlled. This highlights the need for timely weed management practices, such as herbicide application or manual weeding, to maintain rice yield.
Detection of broadleaf weed: <italic>Sphenoclea zeylanica</italic>
The classification of Sphenoclea zeylanica (Blw_SZ) analysis shows that the Support Vector Machine (SVM) algorithm consistently delivers the highest classification performance across all growth stages (Figure 10). At 15DAS (Table 10), SVM achieves a producer accuracy of 98.25% and a user accuracy of 96.55%, indicating strong capability in correctly detecting and labeling this weed even in its early stage. In contrast, Minimum Distance records lower accuracy (78.95% PA and 80.36% UA), and Parallelepiped performs moderately (85.09% PA and 67.83% UA), showing a tendency for more misclassifications compared to SVM.
SZ detection in rice field implementing supervised classification algorithm.
Three sets of satellite images labeled SVM, MD, and PP show land classification at intervals SZ_15DAS, SZ_25DAS, and SZ_30DAS. Color coding indicates land types: red for broadleaf weed, green for rice, dark green for mixed vegetation, yellow for soil, blue for water, and black for unclassified areas. Each set depicts changes over time in the distribution of these classes.
Comparison of producer accuracy and user accuracy for each algorithm for plant classification (Sphenoclea zeylanica) at 15, 25, 30DAS.
Classifier/ROI class
SVM
MD
PP
Class
Prod. acc. (%)
User acc. (%)
Prod. acc. (%)
User acc. (%)
Prod. acc. (%)
User acc. (%)
PA and UA at 15DAS
Blw_SZ
98.25
96.55
78.95
80.36
85.09
67.83
Rice
100
100
99.02
89.78
77.45
100
Water
100
98.68
100
84.18
32.89
100
Soil
100
100
88.20
93.45
85.39
100
Mixed Vegetation
96.49
100
49.12
72.73
84.21
44.04
PA and UA at 25DAS
Blw_SZ
99.70
98.80
60.79
65.57
89.06
80.94
Rice
99.72
99.44
61.62
67.07
70.31
98.43
Water
100
100
91.92
83.11
69.70
100
Soil
100
100
98.01
100
83.11
100
Mixed Vegetation
97.71
100
88.00
78.17
81.14
90.45
PA and UA at 30DAS
Blw_SZ
99.70
98.80
60.79
65.57
89.06
80.94
Rice
99.72
99.44
61.62
67.07
70.31
98.43
Water
100
100
91.92
83.11
69.70
100
Soil
100
100
98.01
100
83.11
100
Mixed Vegetation
97.71
100
88.00
78.17
81.14
90.45
At 25DAS (Table 10), SVM remains the top performer with 99.70% producer accuracy and 98.80% user accuracy for Blw_SZ. This high consistency proves SVM’s robustness as the plant grows. Minimum Distance, on the other hand, drops significantly in user accuracy to 65.57%, even though it still correctly identifies some Blw_SZ cases (60.79% PA). Parallelepiped performs relatively better than Minimum Distance with 89.06% PA and 80.94% UA, but still cannot match the precision of SVM.
At 30DAS (Table 10), all methods show some improvement, but again SVM leads with 99.04% producer accuracy and 99.42% user accuracy, showing nearly perfect classification. Minimum Distance slightly improves (73.03% PA and 67.68% UA), but remains less reliable. Parallelepiped results (81.50% PA and 54.44% UA) indicate that it still misclassifies a significant portion of other classes as Blw_SZ.
Comparative analysis of classification models for SZ detection
The classification analysis of Sphenoclea zeylanica (SZ) using SVM, MD, and PP at 15, 25, and 30 Days After Sowing (DAS) reveals distinct trends in classification accuracy, Kappa coefficient, and standard deviation (StDev) across different growth stages. The comparison highlights the strengths and limitations of each classification method in distinguishing SZ from rice plants, mixed vegetation, soil, and water (Please refer Table 11).
Classification analysis of SZ at 15DAS, 25DAS, 30DAS.
Species/DAS
SZ_15DAS
SZ_25DAS
SZ_30DAS
Classifier
Overall accuracy
Kappa coefficient
Mean
StDev
Overall accuracy
Kappa coefficient
Mean
StDev
Overall accuracy
Kappa coefficient
Mean
StDev
SVM
99.35
0.99
3.5
1.58
99.62
0.996
3.41
1.53
99.68
0.996
3.32
1.56
MD
88.69
0.86
3.39
1.59
80.5
0.764
3.34
1.59
84.32
0.811
3.44
1.66
PP
77.69
0.74
2.77
2.02
81.96
0.786
2.43
2.14
56.86
0.488
1.84
1.35
Among the three classification methods, SVM consistently outperformed MD and PP in all three time periods, with the highest overall accuracy and Kappa coefficient. At 15 DAS, SVM achieved an overall accuracy of 99.35% and a Kappa coefficient of 0.992, indicating highly accurate classification with minimal misclassification. This accuracy remained high at 25 DAS (99.62%) and 30 DAS (99.68%), with a consistently high Kappa coefficient of 0.996 at both stages. The results suggest that SVM maintains strong classification stability and reliability across all growth stages of SZ.
The MD classifier performed moderately well, but with significantly lower accuracy compared to SVM. At 15 DAS, MD achieved an accuracy of 88.69%, which declined to 80.5% at 25 DAS and 84.32% at 30 DAS. The Kappa coefficient also followed a similar pattern, decreasing from 0.863 (15 DAS) to 0.764 (25 DAS) before recovering slightly to 0.811 at 30 DAS. This fluctuation in accuracy indicates that MD struggles to maintain classification consistency over time, particularly at 25 DAS, where it recorded the lowest accuracy.
The mean classification accuracy across different days remained highest for SVM, with values around 3.5 at 15 DAS, 3.41 at 25 DAS, and 3.32 at 30 DAS, demonstrating minimal variation across growth stages. The standard deviation (StDev) for SVM also remained low (1.576 at 15 DAS, 1.528 at 25 DAS, and 1.564 at 30 DAS), indicating that the model performed consistently with minimal fluctuations.
MD showed a slightly lower mean classification value compared to SVM, with more variation in its accuracy over time. At 15 DAS, the mean was 3.39, which slightly dropped to 3.34 at 25 DAS but increased to 3.44 at 30 DAS. The standard deviation remained relatively stable but was slightly higher than SVM, with values of 1.59 at 15 DAS, 1.52 at 25 DAS, and 1.66 at 30 DAS. Parallelepiped, on the other hand, showed the lowest mean classification values and the highest standard deviation, indicating poor stability and high variability in classification performance.
Vegetation class cover
The vegetation class cover analysis for Sphenoclea zeylanica (SZ) plots at 15, 25, and 30 Days After Sowing (DAS) presents insights into how different vegetation classes change over time (Figure 11). The distribution of vegetation classes shows notable shifts in cover percentage for broadleaf weed (SZ), rice, water, soil, and mixed vegetation from 15 DAS to 30 DAS. Sphenoclea zeylanica (Blw_SZ) increased its coverage from 6.13% at 15 DAS to 8.05% at 25 DAS and 9.71% at 30 DAS. This growth suggests that SZ is expanding in coverage, possibly due to favorable growth conditions or competition with other vegetation.
Vegetation class cover distribution (%) for Plot 3_SZ.
Bar chart titled “Vegetation Class Cover Distribution (%)_SZ Plot,” displays percentage cover for different classes over three time periods: 15DAS, 25DAS, and 30DAS. Categories are Blw_SZ, Rice, Water, Soil, and Mix_Vege. Rice has the highest percentages throughout, followed by Water and Soil, while Blw_SZ and Mix_Vege have the lowest.
Rice cover fluctuated slightly, decreasing from 38.11% at 15 DAS to 36.25% at 25 DAS before rising again to 39.51% at 30 DAS. The decline at 25 DAS could indicate competition with other vegetation or variability in plant canopy development, but the recovery at 30 DAS suggests that the rice plants continued to develop. Water coverage increased from 24.00% at 15 DAS to 28.73% at 25 DAS, likely due to field irrigation or natural water retention. However, by 30 DAS, water cover dropped to 26.20%, which might indicate water absorption by plants or a reduction in standing water due to evaporation and soil infiltration.
Soil exposure also varied, rising from 17.94% at 15 DAS to 20.64% at 25 DAS, possibly due to changes in vegetation density or water levels. However, it dropped significantly to 15.46% at 30 DAS, suggesting that the vegetation cover increased, reducing visible soil. Mixed vegetation (Mix_Vege) saw a drastic decline, starting at 13.82% at 15 DAS, plummeting to 6.32% at 25 DAS, and slightly recovering to 9.12% at 30 DAS. This suggests that mixed vegetation was outcompeted by SZ, rice, or other dominant classes, leading to a significant reduction in its coverage.
Figure 12 quantifies the net rate of change (%) in vegetation cover from 15 DAS to 30 DAS. Sphenoclea zeylanica (Blw_SZ) exhibited the highest positive change, increasing by 3.58%, confirming its expansion over time. Rice also increased slightly, with a net gain of 1.39%, indicating stable growth despite minor fluctuations. Water cover rose by 2.21%, reflecting a moderate increase, likely influenced by field conditions and irrigation. Soil coverage decreased significantly by -2.48%, meaning that vegetation gradually covered more of the exposed ground. Mixed vegetation suffered the most significant decline, with a drastic decrease of -4.71%, suggesting significant suppression or competition from other plant species like SZ and rice. The vegetation dynamics observed in the SZ plots indicate that Sphenoclea zeylanica (SZ) is expanding its coverage over time, which could pose a potential weed problem in rice fields. While rice cover remained relatively stable, the reduction in mixed vegetation suggests that SZ and rice may be outcompeting other plant species.
Vegetation cover rate changes (%) for SZ in 15DAS – 30DAS.
Bar chart showing vegetation cover rate changes in percentage. Categories include Blw_SZ at 3.58%, Rice at 1.39%, Water at 2.21%, Soil at -2.48%, and Mix_Vege at -4.71%. Bars vary in color indicating positive and negative changes.Discussions
The progressive improvement in classification accuracy across growth stages (from 15 to 30 DAS) can be attributed to several biophysical and canopy structural changes. At early stages, spectral confusion between rice and broadleaf weeds tends to be higher due to smaller leaf area and more pronounced soil background interference (Roslin et al., 2021). As the crop and weed canopies develop, increased canopy cover reduces soil reflectance influence and enhances spectral separation among classes. This phenomenon is supported by studies demonstrating that better-developed canopies amplify between-species spectral variability, thus improving classification performance (Dai et al., 2024; Zhang et al., 2025). Additionally, advances in radiative transfer modeling and 3D canopy representation have shown that leaf biochemical traits such as chlorophyll content become more distinctly expressed as plants mature, further aiding classification (Zhang et al., 2025). Collectively, these factors explain the accuracy trends observed in our study.
Among the classifiers, SVM consistently outperformed MD and PP at all growth stages. SVM achieved the highest classification accuracies, often exceeding 99%, and demonstrated strong agreement with ground truth data, as evidenced by high kappa coefficients. This finding aligns with existing literature that highlights SVM’s suitability for high-dimensional hyperspectral data due to its capacity to manage small training datasets and non-linear class separability (Melgani and Bruzzone, 2004; Camps-Valls and Bruzzone, 2005). Its robustness and ability to generalize well in complex agricultural scenes make SVM a top choice for vegetation classification (Li et al., 2021).
For Limnocharis flava, SVM achieved near-perfect accuracy at all stages, while MD and PP were less consistent. PP performed worst due to rigid decision rules prone to spectral confusion in heterogeneous crop environments (Thenkabail et al., 2011; Manakos et al., 2000). For Monochoria vaginalis, all classifiers improved over time, but SVM maintained the highest accuracy. MD showed moderate gains but remained sensitive to within-class variability (Pal, 2005), and PP’s high producer but low user accuracy indicated frequent false positives. For Sphenoclea zeylanica, SVM again excelled, achieving 98.25% PA at 15 DAS (Plaza et al., 2009), while MD fluctuated in accuracy and PP remained the least reliable due to its simplistic rectangular decision rules.
The proportion of weed pixels showed a clear increasing trend from 15 DAS to 30 DAS, indicating progressive weed pressure over time. At 15 DAS, weed presence was relatively low, but early detection at this stage is critical because intervention can prevent rapid population growth and competition for resources. By 25 DAS, weed coverage had increased substantially, suggesting that delayed management may require higher herbicide doses or more labor-intensive control measures. At 30 DAS, peak weed pressure was observed, coinciding with greater canopy closure, which can hinder mechanical or targeted chemical control. This pattern mirrors findings by Abdul Khaliq and Amar Matloob (2011), who reported that weed density peaks between 20 and 30 DAS and that competition beyond 20 DAS causes severe yield losses. These results suggest that the most effective intervention window would be between 15 and 25 DAS, when weed plants are still small, easier to control, and before they cause significant yield losses.
The vegetation cover analysis reinforced these findings. In all three plots, rice vegetation covers generally increased over time, indicating successful crop establishment. Between 15 to 25 DAS, rice cover exhibits a modest increase reflecting the gradual establishment of seedlings during early vegetative growth. Meanwhile, broadleaf weeds showed a gradual but steady increase, suggesting active competition and the importance of early weed control. These results are consistent with findings by Ali et al. (2020), who emphasized that broadleaf weeds, if not managed early, can establish quickly and reduce rice productivity. Mixed vegetation, water, and soil cover all declined, especially by 30 DAS, likely due to the expansion of crop canopy that obscured bare ground and open water. In 30 DAS, rice also entered tillering phase, characterized by accelerated leaf area development and rapid canopy closure, causing more pronounced coverage changes over shorter time periods. Such dynamics are commonly reported in agricultural remote sensing studies as crops grow and dominate the spectral scene (Zhou et al., 2008).
This study also has several limitations. The classification methods, particularly MD and PP, were susceptible to spectral overlap between classes, especially during early growth stages when plant physiological characteristics were similar. Pixel-based classification errors may contribute to minor fluctuations in coverage percentage. Environmental factors such as variable illumination, soil background reflectance, and atmospheric noise may have further contributed to misclassification (Al-Badri et al., 2022). While SVM consistently provided higher accuracy, it still requires careful parameter tuning to avoid overfitting and to generalize across different field conditions. Future research could explore advanced machine learning models such as Random Forests, Convolutional Neural Networks (CNNs), or Transformer-based architectures, which may capture complex spectral–spatial relationships more effectively. Additionally, fusing hyperspectral data with other modalities (e.g., LiDAR or thermal) may help overcome spectral confusion and improve robustness.
The classification outputs generated in this study have practical potential in supporting UAV-based weed scouting and enabling site-specific herbicide applications. Such approaches can help reduce herbicide usage, lower production costs, and minimize environmental impact (Allmendinger et al., 2024). However, barriers to large-scale adoption remain, including the high cost of hyperspectral sensors, the need for skilled data analysts, and UAV operational limitations for covering extensive farmland. Addressing these challenges through cost reduction, automated image processing pipelines, and integration with precision agriculture platforms will be essential for practical implementation.
Conclusions
SVM consistently outperformed MD and PP in classifying all three broadleaf weed species at 15, 25, and 30 DAS (Table 12). At 15 DAS, SVM achieved an accuracy above 99% for all three weed species, with low omission and commission errors, ensuring minimal misclassification. At 25 DAS, SVM maintained over 99% accuracy, confirming its ability to distinguish Limnocharis flava, Monochoria vaginalis, and Sphenoclea zeylanica from other spectral classes. At 30 DAS, SVM continued to be the most stable classifier, with 99%+ accuracy, proving its effectiveness in differentiating weeds from rice even at later growth stages. SVM is the best choice for classifying all three broadleaf weeds, with consistently high accuracy and minimal misclassification.
Comparison of accuracy level for all algorithm for plant classification at 15DAS, 25DAS, and 30DAS.
Broadleaf weed species
Best classifier
Accuracy at 15 DAS
Accuracy at 25 DAS
Accuracy at 30 DAS
Limnocharis flava
SVM
99.30%
99.50%
99.60%
Monochoria vaginalis
SVM
99.20%
99.40%
99.50%
Sphenoclea zeylanica
SVM
99.35%
99.62%
99.68%
Limnocharis flava
MD
87.80%
82.50%
85.20%
Monochoria vaginalis
MD
85.90%
80.10%
83.30%
Sphenoclea zeylanica
MD
88.69%
80.50%
84.32%
Limnocharis flava
PP
76.50%
80.20%
56.80%
Monochoria vaginalis
PP
77.30%
81.10%
58.60%
Sphenoclea zeylanica
PP
77.69%
81.96%
56.86%
The vegetation cover analysis at 15, 25, and 30 DAS showed key trends in broadleaf weed expansion in rice fields. All three broadleaf weeds showed increasing coverage over time, indicating their rapid spread and competition with rice. Rice cover fluctuated, decreasing slightly at 25 DAS before recovering at 30 DAS, suggesting that weeds and other vegetation impact rice growth. Mixed vegetation declined significantly, showing that broadleaf weeds outcompeted other plants. Soil cover decreased over time, reflecting increased vegetation dominance in the field. These results highlight the importance of early detection and management of broadleaf weeds to prevent them from negatively impacting rice yield. By improving the precision of weed identification through hyperspectral imaging, this study contributes to developing better-targeted weed control strategies, leading to more sustainable agricultural practices and optimized crop yields.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.
Authors would like to acknowledge the contributions from the Ministry of Higher Education, Malaysia, for the research project titled “Pest and Disease Monitoring Using Artificial Intelligence for Risk Management of Rice Under Climate Change” under the Long-term Research Grant Scheme (LRGS) (LRGS/1/2019/UPM01/2/5; vote number: 5545002). The authors also extend their gratitude to Universiti Putra Malaysia for providing the necessary facilities.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
ReferencesAbdul KhaliqA. K.Amar MatloobA. M. (2011).
Weed-crop competition period in three fine rice cultivars under direct-seeded rice culture. Pak. J. Weed Sci. Res. 17.
AgrawalS. (2024).
Emerging technologies in precision weed management: Tools, techniques, and the future of agriculture. Int. J. Advanced Biochem. Res. 8, 361–366. doi: 10.33545/26174693.2024.v8.i2se.589Al-BadriA. H.IsmailN. A.Al-DulaimiK.SalmanG. A.KhanA. R.Al-SabaawiA.. (2022).
Classification of weed using machine learning techniques: a review—challenges, current and future potential techniques. J. Plant Dis. Prot.129, 745–768. doi: 10.1007/s41348-022-00612-9AliM. A.AhsanF.HasanuzzamanM.MiahM. G. (2020).
Weed competition and management in rice: A review. Agronomy10, 1266.
AllmendingerA.SpaethM.SaileM.PeteinatosG. G.GerhardsR. (2024).
Agronomic and technical evaluation of herbicide spot spraying in maize based on high-resolution aerial weed maps—an on-farm trial. Plants13, 2164. doi: 10.3390/plants13152164, PMID: 39124282AlzubaidiL.ZhangJ.HumaidiA. J.Al-DujailiA.DuanY.Al-ShammaO.. (2021).
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. big Data8, 1–74. doi: 10.1186/s40537-021-00444-8, PMID: 33816053AthiraG. R. (2019). Morphology, ecology and management of monochoria vaginalis (Burm. f.) Kunth (Vellanikkara:
Department of Agronomy, College of Horticulture).
BallantiL.BlesiusL.HinesE.KruseB. (2016).
Tree species classification using hyperspectral imagery: A comparison of two classifiers. Remote Sens.8, 445. doi: 10.3390/rs8060445Barbosa JúniorM. R.TedescoD.CarreiraV. D. S.PintoA. A.MoreiraB. R. D. A.ShiratsuchiL. S.. (2022).
The time of day is key to discriminate cultivars of sugarcane upon imagery data from unmanned aerial vehicle. Drones6, 112. doi: 10.3390/drones6050112BhargavaA.SachdevaA.SharmaK.AlsharifM. H.UthansakulP.UthansakulM. (2024).
Hyperspectral imaging and its applications: A review. Heliyon10, e33208. doi: 10.1016/j.heliyon.2024.e33208, PMID: 39021975BongominO.LamoJ.GuinaJ. M.OkelloC.OcenG. G.OburaM.. (2024).
UAV image acquisition and processing for high-throughput phenotyping in agricultural research and breeding programs. Plant Phenome J.7, e20096. doi: 10.1002/ppj2.20096Camps-VallsG.BruzzoneL. (2005).
Kernel-based methods for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens.43, 1351–1362. doi: 10.1109/TGRS.2005.846154CarterR.JonesJ. C.GoddardR. H. (2014).
Sphenoclea zeylanica (Sphenocleaceae) in north America—dispersal, ecology, and morphology. Castanea79, 33–50. doi: 10.2179/13-036ChauhanB. S. (2012).
Weed ecology and weed management strategies for dry-seeded rice in Asia. Weed Technol.26, 1–13. doi: 10.1614/WT-D-11-00105.1Che'YaN. N. (2016). Site-specific weed management using remote sensing (Brisbane, Australia:
The University of Queensland). Available online at: https://www.researchgate.net/publication/340133433_Site-Specific_Weed_Management_Using_Remote_Sensing (Accessed July 21, 2025).
ColbachN.AdeuxG.CordeauS.MoreauD. (2023).
Weed-induced yield loss through resource competition cannot be sidelined. Trends Plant Sci.28, 1329–1330. doi: 10.1016/j.tplants.2023.08.004, PMID: 37944484CongaltonR. G. (1991).
A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ.37, 35–46. doi: 10.1016/0034-4257(91)90048-BDaiJ.KönigM.JamaliniaE.HondulaK. L.VaughnN. R.HecklerJ.. (2024).
Canopy−Level spectral variation and classification of diverse crop species with fine spatial resolution imaging spectroscopy. Remote Sens.16, 1447. doi: 10.3390/rs16081447De MoraesR. M. (2004).
An analysis of the fuzzy expert systems architecture for multispectral image classification using mathematical morphology operators. Int. J. Comput. Cogn.2, 35–69.
Department of Agriculture Malaysia (DOA) (2022). Rice Check Padi edisi 2022 (Putrajaya, Malaysia:
Department of Agriculture Malaysia). Available online at: https://www.doa.gov.my/index/resources/aktiviti_sumber/sumber_awam/maklumat_pertanian/padi/ricecheckpadi2022.pdf (Accessed December 11, 2025).
El-GhandorA. M. A. (2024).
Chemical Control Against Aggressive Broad Leaves Weed Ipomea sp. in Drill Seeded-rice (Oryza sativa L.). Asian J. Res. Crop Sci.9, 10–21. doi: 10.9734/ajrcs/2024/v9i1242GaoP.WangH.DengS.DongE.DaiQ. (2022).
Influence of organic rice production mode on weed composition in the soil seed bank of paddy fields. Front. Plant Sci.13, 1056975. doi: 10.3389/fpls.2022.1056975, PMID: 36479513GolowichS.LockwoodR.ChrispM.ManolakisD. (2018). “
Three-dimensional radiative transfer for hyperspectral imaging classification and detection,” in 2018 IEEE International Symposium on Technologies for Homeland Security (HST). (
IEEE) 1–5. doi: 10.1109/THS.2018.8574127GonçalvesC.SantanaP.BrandãoT.GuedesM. (2022).
Automatic detection of Acacia longifolia invasive species based on UAV-acquired aerial imagery. Inf. Process. Agric.9, 276–287. doi: 10.1016/j.inpa.2021.04.007HaqS. M.WaheedM.BussmannR. W.KumarM. (2024).
Trouble in the rice field: Distribution ecology and indicator value of weed species in the rice fields of Himalayan region. Ecol. Front.44, 507–516. doi: 10.1016/j.chnaes.2023.07.012KanatasP.GazoulisI.ZannopoulosS.TataridasA.TsekouraA.AntonopoulosN.. (2021).
Shattercane (Sorghum bicolor (L.) Moench Subsp. Drummondii) and weedy sunflower (Helianthus Annuus L.)—Crop wild relatives (CWRs) as weeds in agriculture. Diversity13, 463. doi: 10.3390/d13100463KrumsriR.Kato-NoguchiH.PoonpaiboonpipatT. (2020).
Allelopathic effect of sphenoclea zeylanica gaertn. On rice ('Oryza sativa'L.) germination and seedling growth. Aust. J. Crop Sci.14, 1450–1455. doi: 10.21475/ajcs.20.14.09.p2494KumarV.MahajanG.ChauhanB. S. (2017). Rice weeds and their management (Cham, Switzerland:
Rice Production Worldwide), 361–392.
LesD. H. (2020). Aquatic monocotyledons of north America (New York:
CRC Press).
LiZ.LiuC.WangY.WangY.LiuX. (2021).
A review of hyperspectral image classification in agriculture: Development, challenges, and outlook. Remote Sens.13, 4290.
ManakosI.KalaitzidisC.TsiligiridisT. A. (2000).
Classification of remotely sensed images using a neural network and decision tree classifier. Remote Sens. Rev.20, 361–371.
MaoP.QinL.HaoM.ZhaoW.LuoJ.QiuX.. (2021).
An improved approach to estimate above-ground volume and biomass of desert shrub communities based on UAV RGB images. Ecol. Indic.125, 107494. doi: 10.1016/j.ecolind.2021.107494MelganiF.BruzzoneL. (2004).
Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens.42, 1778–1790. doi: 10.1109/TGRS.2004.831865MensahB.RaiN.BetitameK.SunX. (2024).
Advances in weed identification using hyperspectral imaging: A comprehensive review of platform sensors and deep learning techniques. J. Agric. Food Res.18, 101388. doi: 10.1016/j.jafr.2024.101388MonteiroA.SantosS. (2022).
Sustainable approach to weed management: The role of precision weed management. Agronomy12, 118. doi: 10.3390/agronomy12010118NishanM. A.GeorgeS. (2018).
Limnocharis flava (L.) Buchenau: An emerging wetland invader-A review. Agric. Rev.39, 246–250.
PalM. (2005).
Random forest classifier for remote sensing classification. Int. J. Remote Sens.26, 217–222. doi: 10.1080/01431160412331269698PappL.Van LeeuwenB.SzilassiP.TobakZ.SzatmáriJ.ÁrvaiM.. (2021).
Monitoring invasive plant species using hyperspectral remote sensing data. Land10, 29. doi: 10.3390/land10010029PatidarJ.KaurS. (2024).
Auxinic and sulfonyl urea herbicides for post-emergence control of sedges and broad-leaf weeds in direct-seeded rice. Phytoparasitica52, 22. doi: 10.1007/s12600-024-01150-wPérez-OrtizM.PeñaJ. M.GutiérrezP. A.Torres-SánchezJ.Hervás-MartínezC.López-GranadosF. (2016).
Selecting patterns and features for between-and within-crop-row weed mapping using UAV-imagery. Expert Syst. Appl.47, 85–94. doi: 10.1016/j.eswa.2015.10.043PlazaA.BenediktssonJ. A.BoardmanJ. W.BrazileJ.BruzzoneL.Camps-VallsG.. (2009).
Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ.113, S110–S122. doi: 10.1016/j.rse.2007.07.028PutraS. P.SantosaS.SalsinhaY. C. F. (2023).
Waterlogging and salinity stress affecting growth and morphological character changes of Limnocharis flava. Biodiversitas J. Biol. Diversity24, 333–340. doi: 10.13057/biodiv/d240140RamliZ.JuraimiA. S.MotmainnaM.Che'YaN. N.RoslimM. H. M.NoorN. M.. (2024).
Weed management using UAV and remote sensing in Malaysia paddy field: A review. Pertanika J. Sci. Technol.33, 1219–1241. doi: 10.47836/pjst.32.3.13RastiB.ScheundersP.GhamisiP.LicciardiG.ChanussotJ. (2018).
Noise reduction in hyperspectral imagery: overview and application. Remote. Sens.10, 482. doi: 10.3390/rs10030482ReddyM. S. S. K. (2020). Biology and management of goose weed (Sphenoclea zeylanica Gaertner) in wetland rice (Vellayani:
Department of Agronomy, College of Agriculture).
Rojas-SandovalJ. (2023). Sphenoclea zeylanica (gooseweed). Available online at: https://www.cabidigitallibrary.org/doi/pdf/10.1079/cabicompendium.51031.
RoseJ. A.SeldevC. C. (2014).
Performance analysis of minimum distance classifier and clustering algorithm in cbir. Appl. Mechanics Materials626, 14–19. doi: 10.4028/www.scientific.net/AMM.626.14RoslinN.Che’YaN.SulaimanN.AlahyadiL.IsmailM. (2021).
Mobile application development for spectral signature of weed species in rice farming. Pertanika J. Sci. Technol. 29. doi: 10.47836/pjst.29.4.01SaiM. V.MuruganG.PerumalM. S.KathiraveluC. (2022).
A phytosociological survey on weed flora and diversity in rice fields of chidambaram block, TN, India. doi: 10.53550/EEC.2022.v28i07s.075SeneviratneK. A. C. N.WijesundaraD. S. A. (2004).
Limnocharis flava (L.) Buchenau (Alismataceae)–a little known and troublesome weed in Andaman Islands. Curr. Sci.87, 140–141.
Shuman-GoodierM. E.SingletonG. R.ForsmanA. M.HinesS.ChristodoulidesN.DanielsK. D.. (2021).
Developmental assays using invasive cane toads, Rhinella marina, reveal safety concerns of a common formulation of the rice herbicide, butachlor. Environ. pollut.272, 115955. doi: 10.1016/j.envpol.2020.115955, PMID: 33221087SivakumarM.ParthasarathyS.PadmapriyaT. (2024).
Trade-off between training and testing ratio in machine learning for medical image processing. PeerJ Comput. Sci.10, e2245. doi: 10.7717/peerj-cs.2245, PMID: 39314694SkauliT. (2009).
Sensor-informed representation of hyperspectral images. In Proceedings of SPIE. 7334. doi: 10.1117/12.819491SongS.DungB. T. N.AhmedS. R.LahoriA. H. (2024).
Bioremediation of toxic metals and Limnocharis flava (L.) Buchenau growth in acidic contaminated soil supported by microbial inoculants. Ecol. Questions35, 1–20. doi: 10.12775/EQ.2024.034SujinahGuntoroD.Sugiyanta (2022).
Competitiveness of swamp rice against Echinochloa crus-galli and Monochoria vaginalis weeds. doi: 10.21475/ajcs.22.16.04.p3537SulaimanN.Che’YaN. N.Mohd RoslimM. H.JuraimiA. S.Mohd NoorN.Fazlil IlahiW. F. (2022).
The application of Hyperspectral Remote Sensing Imagery (HRSI) for weed detection analysis in rice fields: A review. Appl. Sci.12, 2570. doi: 10.3390/app12052570SuomalainenJ.AndersN.IqbalS.RoerinkG.FrankeJ.WentingP.. (2014).
A lightweight hyperspectral mapping system and photogrammetric processing chain for unmanned aerial vehicles. Remote. Sens.6, 11013–11030. doi: 10.3390/rs61111013ThenkabailP. S.LyonJ. G.HueteA. (2011). Hyperspectral remote sensing of vegetation (Boca Raton:
CRC Press).
VuP. T.UnpapromY.RamarajR. (2018).
Impact and significance of alkaline-oxidant pretreatment on the enzymatic digestibility of Sphenoclea zeylanica for bioethanol production. Bioresource Technol.247, 125–130. doi: 10.1016/j.biortech.2017.09.012, PMID: 28946085XuY.GoodacreR. (2018).
On splitting training and validation set: A comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. J. Anal. Testing2, 249–262. doi: 10.1007/s41664-018-0068-2, PMID: 30842888ZakariaN.Ahmad-HamdaniM. S.JuraimiA. S. (2018).
Patterns of resistance to AHAS inhibitors in Limnocharis flava from Malaysia. Plant Prot Sci.54, 48–59. doi: 10.17221/131/2016-PPSZakariaN.RuzmiR.MoosaS.AsibN.ZulperiD.IsmailS. I.. (2021).
Asp-376-Glu substitution endows target-site resistance to AHAS inhibitors in Limnocharis flava, an invasive weed in tropical rice fields. Physiol. Mol. Biol. Plants27, 969–983. doi: 10.1007/s12298-021-00987-3, PMID: 34108823ZhangJ.HuangY.PuR.Gonzalez-MorenoP.YuanL.WuK.. (2019).
Monitoring plant diseases and pests through remote sensing technology: A review. Comput. Electron. Agric.165, 104943. doi: 10.1016/j.compag.2019.104943ZhangH.ZhaoD.GuoZ.GuoS.BaiQ.CaoH.. (2025).
Estimation of chlorophyll content in rice canopy leaves using 3D radiative transfer modeling and unmanned aerial hyperspectral images. Plant Methods21, 26. doi: 10.1186/s13007-025-01346-z, PMID: 39994660ZhouW.LuoJ.LiB.TangL.ZhengX.LiY. (2021).
Effects of Monochoria vaginalis density on yield losses, economic thresholds, and gross returns in paddy rice (Oryza sativa L.). Crop Sci.61, 3610–3622. doi: 10.1002/csc2.20564ZhouW.TroyA.GroveJ. M. (2008).
Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors8, 1613–1636. doi: 10.3390/s8031613, PMID: 27879784ZhuW. D.ZhangH. J.TuS. X.WeiS. H.LiL. (2012).
Effects of Monochoria vaginalis on growth and yield properties of rice and its control economic threshold estimation. Chin. J. Eco-Agriculture20, 1204–1209. doi: 10.3724/SP.J.1011.2012.01204
Edited by: Karansher Singh Sandhu, Bayer Crop Science, United States
Reviewed by: Srikanth Panthulugiri, Iowa State University, United States
Jagdeep Singh, University of California Agriculture and Natural Resources, United States