In India, maize (Zea Mays L.) is the third most important cereal grain crop. The maize crop is being grown in Kharif and rabi seasons across the country (Kaur et al., 2020). It is considered as the ‘Queen of Cereals’ due to its multiple use cases, such as staple food for human beings, feed-fodder for livestock animals, raw materials for several processed foods, industrial products, a rich source of starch and so on. As per the reports, around 31.65 mt of maize was produced across the country during 2020-2021 (ICAR-IIMR, 2021). Every year, around 13.2% of the total crop yield is damaged due to the attack of several disease-causing pathogens (Aggarwal et al., 2021). Among several diseases, Maydis leaf blight or MLB (aka Southern corn leaf Blight) is a serious fungal disease across maize-growing regions of India. Generally, the country’s warm and humid climatic condition is extremely favorable for the disease development (Malik et al., 2018). The MLB disease is caused by Bipolaris maydis (Nisik. & Miyake) Shoemaker 1959 fungus. In the early stages, its symptoms appear as small and oval to diamond-shaped, necrotic to brown-colored lesions on the leaf surfaces. These lesions get elongated as the disease progresses (Aggarwal et al., 2021). It is reported that this disease alone is capable of causing damage approx. 70% of the total crop yield in severe conditions (Hooda et al., 2018). The severity of diseases is an important parameter that measures the intensity level of disease symptoms in the affected portion of the crop and is crucial for disease management too (Hooda et al., 2018). Therefore, our first and foremost aim must be to identify and control the disease at the earliest possible stage of severity to minimize the risk of potential yield loss of maize crop. However, the conventional approach for identifying the severity stages involves visual observations and laboratory analysis. But the fact is, these approaches require highly trained and experienced personnel, which makes them practically infeasible many times. Hence, there is a much need for a precise, quick, cost-effective and automated approach to identify the disease severity stages in the field conditions.

In recent years, several computer vision techniques have been applied to several challenging agricultural problems (Kamilaris and Prenafeta-Boldú, 2018). In this connection, the convolutional neural networks (aka CNNs) are considered as the benchmark for different image-based problem identification in the agriculture domain. The CNN approaches have eased the image recognition process by automatically extracting the features from the images as compared to the hand-engineered feature extractions in the traditional machine learning approaches (LeCun et al., 2015). In case of diagnosis of diseases as well as their severity stages, CNNs have shown significantly better results than the traditional image processing and machine learning techniques. In this context, a very limited number of works have been reported to diagnose disease severity stages in maize crop using in-field images. Therefore, we proposed a novel lightweight CNN network for identifying the severity stages of MLB disease in maize crop. This network would be a practical and viable solution for the farm community of the country. The main contributions of this study are provided below:

This article is organized into six sections. Section 1 (present section) highlights the importance of maize crop, the devastating effect of MLB diseases, constraints of the conventional approaches of disease recognition and management, importance of computer vision-based technologies etc.: Section 2 explores and briefly discusses the related works relevant to the present study, Section 3 explains the materials and methodologies used to carry out the current study; Section 4 reports and discusses the experimental results and finding of the study; Section 5 presents the ablation studies; and Section 6 concludes the whole study highlighting the impact and crucial finding and aligns the future perspective of this study.

In this section, we will briefly discuss the methodologies proposed by research works from across the globe for recognizing diseases as well their severity stages. In recent years, deep learning-based techniques are gaining momentum for identifying diseases of several crops. Several authors like Mohanty et al. (2016); Sladojevic et al. (2016); Ferentinos (2018); Barbedo (2019) and Atila et al. (2021) focused on identifying the diseases of crops at once by applying variety of deep learning models such as state-of-the-art networks, transfer learning models, custom defined models, hybrid CNN models and many more. These works targeted identifying diseases of multiple crops by a single deep learning model. Whereas most of the reported works aimed at crop-specific disease identification problems such as for Rice crop (Lu et al., 2017; Chen et al., 2020; Rahman et al., 2020), Wheat crop (Lu et al., 2017; Picon et al., 2019; Nigam et al., 2021), Tomato crop (Fuentes et al., 2018; Zhang et al., 2018), Maize crop (DeChant et al., 2017; Priyadharshini et al., 2019; Lv et al., 2020; Haque et al., 2021; Haque et al., 2022a; Haque et al., 2022b), etc. The experimental findings of these research works reported significant results by employing several types of CNN-based networks to identify the diseases using color images. Some of these works used lab-based images of crop-diseases such as plant village for their model development, while some has used in-field images

Nowadays, the identification of severity stages of diseases has also attracted the attention of researchers. Significant works have been carried out to identify the disease severity stages using digital images. Wang et al. (2017) applied transfer learning of popular deep CNN models to diagnose disease severity in apple plants and obtained more than 94% classification accuracy on the test dataset. They used publicly available images and assessed them into 4 categories of severity stages for their experiment. Liang et al. (2019) proposed a robust approach for disease diagnosis and disease severity estimation of several crops using deep learning models. Verma et al. (2020) worked on tomato late blight disease and Prabhakar et al. (2020) worked on estimating the severity stages of tomato early blight disease using Deep CNN models. Recently, Sibiya and Sumbwanyambe (2021) used Deep CNN models to classify images of common rust disease of maize crop into four classes of severity levels. They applied fuzzy logic-based techniques to automatically categorize the diseased images into severity categories. Nigam et al. (2021) classified the stem rust disease of wheat crop into four severity categories using deep convolutional neural networks. Chen et al. (2021) worked on estimating the severity of the rice bacterial leaf streak disease using a segmentation-based approach. Wang et al. (2021) proposed an image-segmentation-based approach by integrating a deep CNN model to recognize severity stages of downy mildew, powdery mildew and cucumber viral diseases of cucumber crops. Ji and Wu (2022) proposed fuzzy logic integrated deep learning model for detecting the severity levels of grape black measles disease. Liu et al. (2022) developed a two-stage CNN model for diagnosing the severity of Alternaria leaf blotch disease of the Apple plant. A summary of the previous works is provided in Table 1 .

In this study, 1,760 images of MLB disease of maize were collected under the MDSD database from agricultural fields which were then augmented to 14,389 images. The MDSD image database is categorized into 4 groups viz. healthy, low severity, medium severity and high severity based on the intensity levels of the disease symptoms on leaves. We randomly split the whole dataset into two sets viz. training and testing sets in the ratio of 80:20. Here, the proposed convolutional neural network (CNN) was trained and tested with the MDSD dataset for automated diagnosis of severity stages of MLB disease. In this approach, several combinations of CRB and inception modules were attempted. However, CNN network with 10 CRB and 2 modified inception modules gave the optimal classification performance. Furthermore, to inspect the effectiveness of the proposed model, we also employed a few state-of-the-art pre-trained models viz: VGG16, VGG19, InceptionV3, ResNet50, Xception, MobileNetV2, DenseNet121 and NASNetMobile networks in this study. All the models were trained and tested with similar hyperparameters and configurations as shown in Table 4 . All the model architectures were implemented in python using the tensorflow environment, an open-source deep learning framework. We performed all the experimental analyses by utilizing the high computation power of the Tesla V100 GPUs in the NVIDIA DGX servers.

In the present study, we trained and validated our proposed CNN model 500 times (epochs) using a batch size of 128 (according to the hardware system feasibility) on the MDSD database. Our proposed model achieved the training accuracy of 99.78% with loss of 0.046, whereas the testing accuracy achieved so far was 99.13% with loss of 0.0317. We presented the epoch-wise training and testing behavior (for both classification accuracy and loss) of the proposed model in Figures 6A, B to showcase the model’s efficiency on images of MDSD database.

The experimental findings on the testing set of the MDSD image database reported that our proposed model achieved the overall classification accuracy (99.13%) which is far better than the employed pre-trained networks as shown in Figures 7A, B . However, among the state-of-the-art pre-trained models, the DenseNet121 model achieves the highest accuracy of 95.65% on the test dataset (shown in Figure 7A ). The rest of the models achieve accuracy within 85 to 92%. The proposed model also obtained the lowest (0.0317) of all, while the DenseNet121 model reaches 0.1063 (can be seen in Figure 7B ). These experimental results cater the superiority and effectiveness of the proposed model over the popular pre-trained models.

The interpretation of the model’s performance evaluation based on classification accuracy and training loss wouldn’t be sufficient. Hence, we calculated the average f1-scores of all the models to evaluate the models in an unbiased way. We presented the obtained f1-scores of the models (proposed as well as pre-trained) in Figure 8 . It is quite evident from Figure 8 , that our proposed model obtained the highest f1-score than the pre-trained models in the testing dataset of MLB disease. Our proposed model’s prediction performance on the MLB disease dataset was far better than the popular pretrained models. This result implies that our proposed CNN model could identify the unknown images of MDSD database and classify them into respective severity classes.

To better understand the prediction performance of our proposed model, we presented the confusion matrix in Figure 9 . Figure 9 shows that our proposed model was 100% accurate in predicting the healthy samples, 98% accurate for the low severity samples, 99% accurate for both samples of medium severity and high severity. Moreover, we also computed recall, precision and f1-score to present the class-wise prediction performance of the proposed model as shown in Table 5 . Table 5 shows that the proposed model obtained quite high scores (approx. 99%) for all three metrics. It is evident from the confusion matrix and the performance metrics (recall, precision, and f1-score) that our model performed remarkably well for all the classes of the severity of MLB disease in MDSD database. The model’s performance was quite appreciable not only for healthy or high severity images but also for low severity images in which the symptoms of the disease are very mild. This result supports the significance of the proposed CNN model in recognizing severity levels for the unknown images of MLB disease of maize crop.

From the overall analysis of all the employed models, it is apparent that our proposed lightweight CNN model outperforms the popular pre-trained models for identifying the severity stages of MLB disease. However, the most important aspect of this study is that the proposed model can identify the images of the severity of MLB disease even with complex background conditions. This makes the proposed CNN model an effective and cost-effective approach for identifying the appropriate disease severity stages for the researchers, subject matter specialists and farmers in the field condition.

In this section, we presented the ablation studies for selecting the optimum number of inception modules and best optimization function for the proposed model. First, we trained our proposed CNN model by incorporating 0,1,2 and 3 Inception modules. The experimental results reported in Figures 10A, B , depict that the proposed CNN framework achieved around 95% testing accuracy without any inception module. However, the accuracy kept increasing as the number of Inception modules increased as shown in Figure 10A . As a result, the proposed model showed the best prediction performance (classification accuracy of 99.13%) with two Inception modules compared to the others. From Figure 10B , it is apparent that as the number of Inception modules increased, the testing loss decreased and the proposed model achieved the lowest testing loss (i.e. 0.317) with two Inception modules. Hence, the two Inception modules were selected for the proposed CNN model.

We also conducted experiments with different optimization functions, which have a huge role in model convergence and feature learning. We experimented with four types of optimization functions viz. Stochastic gradient descent (SGD), RMSProp, Adam and Nadam in the proposed model and presented the results in Figure 10C . Among the four optimization functions, Nadam function showed the best performance in the MLB disease severity dataset of maize crop.

In this study, we addressed the major issue of crop management i.e., disease severity stages by proposing a deep learning-based diagnosis approach. In this regard, we created an image database known as MDSD containing images of MLB disease with four different severity stages viz. healthy, low severity, medium and high severity. Next, we proposed a novel lightweight CNN model to identify of severity stages of MLB disease using the images of MDSD. The proposed CNN model’s basic framework comprises a stack of computational layers like the CBR layer (Convolution, ReLU and Batch normalization) augmented with two modified Inception modules. On the test dataset, our proposed model reported 99.13% classification accuracy with an f1-score of 98.97% which is quite superior than most of the popular state-of-the-art pretrained models. Furthermore, the overall experimental analysis demonstrated that our proposed CNN model efficiently captures the promising features of the images with complex backgrounds and classifies them into respective severity classes. Therefore, this automated approach for identifying the severity stages of MLB disease using the proposed CNN model would be feasible and cost-effective for the farm community and the subject matter specialists. However, in the present study, the proposed CNN model only applies to the MLB disease of maize crop. In the future, the study can be further expanded to identify severity stages of other major diseases of maize crop and diseases of other crops as per the availability of image dataset.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

MH: Conceptualization, Methodology, Investigation, Writing - Original Draft, Visualization; SM: Conceptualization, Supervision, Investigation; AA: Supervision, Writing - Review and Editing; CD: Conceptualization, Supervision, Writing - Original Draft, Visualization; TM: Writing - Review and Editing; SN: Writing - Original Draft, Visualization; KH: Field data generation; Data curation. All authors contributed to the article and approved the submitted version.