Portable phytopathogen detection devices integrate actuators and sensors initially developed for consumer electronics, including smartphones and smartwatches, to enable on-site, real-time diagnostics. Actuator components, such as miniaturized light-emitting diodes (LEDs), are capable of emitting wavelengths spanning the visible range (approximately 400–700 nm) and have been employed to stimulate fluorescence or other optical responses in biochemical assays, particularly in the detection of pathogen-associated molecular markers. LEDs for portable biosensing, when coupled with smartphone imaging, have been well documented, offering a promising route toward rapid, field-based diagnostics of plant pathogens (Gudkov et al., 2023). Similarly, near-field communication (NFC) modules operating at 13.56 MHz allow short-range interactions with magnetic or resistive sensing elements, thereby enabling wireless activation of biosensors and facilitating assessments of soil moisture or other environmental parameters that influence pathogen spread (Gawade, 2022).

Display screens in smartphones and smartwatches, with resolutions often exceeding 720 × 1,280 px, can emit controlled wavelength outputs—such as red (628 nm), green (536 nm), and blue (453 nm)—to serve as dynamic light sources for colorimetric analyses of plant extracts. In addition, vibration motors providing haptic feedback in the range of 130–180 Hz can be leveraged to enhance assay kinetics by mixing reagents directly in the field, while integrated speakers emitting acoustic signals are being explored to disrupt sample matrices or stimulate particular biochemical reactions (Fan et al., 2021). The integration of thermal actuators is also critical, as these elements enable precise temperature control essential for nucleic acid amplification tests (NATs), including polymerase chain reaction (PCR) assays, thus facilitating on-the-spot genomic detection of pathogens without the need for a fully equipped laboratory.

Sensor components are equally transformative. Imaging detectors, including high-resolution smartphone cameras equipped for UV–Vis spectrometry, provide powerful means to capture fluorescence, color changes, or other optical indicators of infection. These imaging strategies have proven helpful for plant disease phenotyping and early stress detection (Zubler and Yoon, 2020). Environmental light sensors enhance sensitivity by accounting for ambient conditions and improving the reliability of fluorescence or colorimetric quantifications. NFC readers serve dual roles as communication modules and near-field sensors, enabling rapid, localized measurements vital for wearable and in-field biosensors.

Initially designed for human-device interaction, capacitive touchscreen sensors are increasingly recognized for their ability to detect subtle changes in pressure, moisture, or conductivity when brought into contact with plant tissues, thereby providing indirect indications of infection or physiological stress. Wearable photodetectors integrated into smartwatches further expand the scope for continuous, mobile monitoring of plant health markers, and microphones sensitive to acoustic signals emitted during microbial metabolism or pathogen-vector interactions offer unique, non-invasive diagnostic approaches (Xu et al., 2024). Coupling these sensory capabilities with geolocation data from GPS modules enables spatial mapping of disease outbreaks and supports the implementation of data-driven management strategies (Xin et al., 2009).

The convergence of these actuators and sensors within portable platforms is revolutionizing how researchers and field professionals monitor phytopathogens. Smartphones and smartwatches have evolved into versatile biosensing tools, providing real-time imaging, thermal regulation, optical stimulation, wireless connectivity, and acoustic or tactile feedback (Buja et al., 2021). These devices facilitate on-site nucleic acid amplification and rapid pathogen detection and enable continuous environmental and plant health monitoring. These integrated technologies can guide timely interventions by tracking chlorophyll fluorescence fluctuations indicative of early disease onset, mapping pathogen prevalence across agricultural landscapes, and detecting subtle changes in leaf temperature profiles. As a result, mobile devices equipped with tailored actuators and sensors are poised to become indispensable in modern precision agriculture, improving our capacity to detect, map, and manage phytopathogens and safeguarding global crop productivity and food security.

Figure 1 shows that portable phytopathogen detection devices integrate sensor components (such as imaging detectors, light sensors, and GPS modules) and actuator components (like LEDs and thermal actuators) to enable various applications, including phytopathogen detection, agricultural monitoring, and remote sensing for enhanced crop management.

Figure 2 illustrates various advanced biosensing technologies categorized based on their unique functionalities. Immunoaffinity devices provide sensitive detection using fluorescence techniques, while imaging biosensors enable label-free, multiplexed biomarker detection. Plasmonic sensors facilitate high-throughput screening of biomolecular interactions, and lab-on-a-chip systems integrate microfluidics for rapid analyte detection. Additionally, optofluidic devices offer real-time monitoring of molecular interactions, and smartphone integration enables biosensing in resource-limited settings by leveraging portable and accessible technologies. Together, these innovations highlight the convergence of biosensing technologies’ precision, portability, and scalability.

Portable point-of-care (POC) devices for phytopathogen detection are essential in agricultural and environmental management, enabling on-site diagnostics and immediate decision-making to curb plant diseases. These devices’ portability and user-friendliness are designed for field deployment, allowing use by individuals without specialized training. Lightweight and compact, POC devices are optimized for rapid, on-site testing, crucial in areas lacking lab access. Their ability to deliver quick results in a few minutes to hours compared to lab tests that may take days—supports timely intervention to prevent further pathogen spread, which is especially critical in large-scale agricultural settings where delays can lead to economic losses (Buja et al., 2021; Safavieh et al., 2014). Table 1 lists the core technologies used in portable diagnostic devices.

The latest developments integrate nucleic acid amplification and optical detection, allowing these devices to differentiate pathogenic from non-pathogenic organisms—critical for reliable diagnostics (Hsieh et al., 2015). Durability is equally important, as these devices often endure harsh conditions. Features like ruggedized casings, moisture resistance, and self-contained power sources ensure reliability and minimize maintenance in varied agricultural environments (Kou et al., 2020).

The integration of IoT in agriculture has fundamentally improved the ability to monitor and manage agricultural practices with real-time, actionable data. This technology enables portable devices like PCR, LAMP, and ELISA to detect phytopathogens directly in the field, transmitting data to cloud systems for analysis and alerts. By facilitating immediate intervention through mobile alerts, IoT-connected devices help farmers take proactive steps to contain diseases such as Fusarium wilt, as demonstrated in successful applications across global agricultural settings (Dholu and Ghodinde, 2018; Kumar et al., 2023). Figure 14 describes the application of IoT in plant pathogen detection.

IoT-enabled devices have optimized pathogen detection in vineyards, reducing pesticide use while maintaining crop quality (Trilles et al., 2020). In Southeast Asia, IoT-based LAMP systems detected bacterial blight in rice, providing timely data in regions with limited lab access and enhancing regional pathogen tracking (Uddin et al., 2017). Similarly, U.S. farmers managing soybean rust have benefited from IoT kits that enabled targeted fungicide applications (Ayaz et al., 2019).

Beyond pathogen detection, IoT’s role in agriculture includes granular data collection supporting predictive models for disease prevention. However, challenges such as costs, connectivity issues in rural areas, and technical expertise remain significant obstacles to its broader adoption (Khuwaja et al., 2023; Siskandar et al., 2020). Nevertheless, IoT is pivotal in shaping resilient and data-driven farming practices, optimizing resource use, and ensuring sustainable agricultural operations.

Applying AI and Machine Learning (ML) in agriculture, particularly in portable diagnostic tools, has transformed how diseases are managed in real-time. AI-driven platforms, including PlantVillage Nuru and, ViT-SmartAgri harness the potential of frameworks like TensorFlow and PyTorch to provide farmers with rapid and precise disease diagnostics via smartphones. These tools empower smallholder farmers worldwide, reducing dependency on costly lab tests and enhancing disease management.

For example, PlantVillage Nuru identifies plant diseases with over 90% accuracy by analyzing smartphone images of leaves, enabling farmers in East Africa to mitigate crop losses effectively (Mrisho et al., 2020). ViT-SmartAgri leverages advanced vision transformer models to detect subtle symptoms in crops like tomatoes, further supporting precision agriculture with highly accurate disease identification (Boukabouya et al., 2022).

In addition to image-based diagnostics, AI algorithms such as Support Vector Machines (SVMs) and k-NN classifiers are being used in multiplex detection systems to classify pathogens like Ralstonia solanacearum, improving the speed and accuracy of field-based diagnostics (Ryo et al., 2009). Predictive modeling through platforms integrating IoT and AI further supports preemptive disease management. These models, such as Random Forest and RNNs, predict outbreaks of diseases like potato late blight, enabling farmers to implement preventive strategies before infections spread (Meno et al., 2023).

With millions of global users, apps like Plantix, which diagnose plant diseases and nutrient deficiencies, have significantly contributed to sustainable farming practices (Zhao et al., 2024).

Moreover, portable spectrometers combined with AI algorithms, such as Decision Trees and Support Vector Machines, analyze plant nutrient deficiencies by interpreting leaf reflectance spectra, providing farmers with immediate insights to optimize plant health (Li D. et al., 2020). AI models, including LSTM networks, are also used for forecasting pest outbreaks based on climatic data, allowing for targeted interventions (Chen et al., 2022). Drone-based systems equipped with AI offer large-scale crop monitoring, enabling efficient management of disease hotspots in expansive agricultural fields (Bawa et al., 2024).

These portable diagnostic devices generate diverse, real-time data streams—from diagnostic results to geospatial coordinates and temporal disease trends—and rely on robust pipelines for harmonization, analysis, and dissemination. Wireless connectivity through Wi-Fi, LPWANs, and 4G/5G networks ensures seamless data transfer to cloud servers and decision-support tools, enabling systems to integrate with nanopore sequencing for real-time pathogen analysis, machine learning-driven outbreak predictions and targeted fungicide recommendations (Fountas et al., 2020). Interoperable data formats and standardized protocols promoted by organizations like ISO facilitate large-scale disease surveillance, informed by spatial data on virulence races of pathogens such as wheat rust (Gilligan, 2024). Embedded algorithms enhance data quality control, while smartphone-integrated microfluidic chips ensure accurate detection by adjusting for environmental variables (Chen et al., 2017). Connectivity also supports collaborative diagnostics, allowing remote consultation with experts and global data-sharing for emerging pathogens (Lau and Botella, 2016), and CRISPR-based devices coupled with geospatial data can predict disease spread to optimize management strategies (Morcia et al., 2023). Data security measures, including encryption and authentication, safeguard agronomic information, while AI models and multi-modal analyses combining external data sources, like satellite imagery and soil profiles, support holistic disease management (Mahlein, 2016).

Flexible connectivity frameworks integrate evolving diagnostic technologies—CRISPR-based tools, synthetic biology biosensors—into existing IoT and cloud platforms, benefiting large-scale and smallholder agriculture. For instance, China’s IoT-based system for crop disease prevention uses ozone sterilization and light-trap technology, transmitting environmental and pathogen data to a central system for real-time alerts and reducing chemical inputs (Wang et al., 2024). Cloud-based analytics platforms like Arable and Semios combine sensor data on plant health, pests, and weather conditions, enabling immediate, data-driven insights and collaboration among farmers, extension services, and researchers. Large-scale epidemiological analysis and predictive modeling help manage infections like Xylella fastidiosa in olive orchards (Hornero et al., 2020) and predict Phytophthora infestans outbreaks in potatoes using machine learning models (Duarte-Carvajalino et al., 2018). This convergence of wireless sensor networks, cloud analytics, and AI-driven modeling fundamentally enhances real-time decision-making, resilience, and sustainable disease control, empowering modern agriculture to confront climate change and evolving pathogen challenges. Figure 15 illustrates the resolution of various plant stress detection techniques.

Smartphone applications for phytopathogen detection have revolutionized agricultural diagnostics, leveraging AI and machine learning to provide real-time disease management solutions. Apps like Plantix are widely used in South Asia and Africa, enabling farmers to diagnose diseases like powdery mildew and rust through image analysis. Plantix’s extensive database covers over 30 crops and 400 diseases, and it also offers a community feature for farmers to share insights, making it a valuable resource in regions with limited access to traditional agricultural support (Samal et al., 2023).

AgroAI targets staple crops such as rice and maize, using CNNs to identify bacterial blight and maize rust while also forecasting disease outbreaks based on environmental data like humidity and temperature (Terentev and Dolzhenko, 2023). AgroAI’s predictive capabilities allow farmers to implement preventive measures, reducing crop losses and improving crop health. In East Africa, iCassava addresses cassava diseases like the Cassava Brown Streak Virus (Enkvetchakul and Surinta, 2022). Developed with TensorFlow’s mobile framework, iCassava, a computer vision dataset, operates offline, essential in remote areas of Uganda and Tanzania. Field tests have demonstrated high accuracy, bolstering efforts to protect this staple crop from devastating diseases (Mwebaze et al., 2019).

DetectPlant uses the EfficientNetV2B2 model to detect diseases in tomatoes and grapes, achieving high accuracy through transfer learning, which adapts pre-trained models to specific crops (Debnath et al., 2023). Meanwhile, CropDoctor is widely adopted in India to identify diseases and nutrient deficiencies across crops like wheat and soybeans. Its AI-powered analysis provides farmers with actionable recommendations, and studies have shown that its guidance can increase crop yields by up to 15% (Siddiqua et al., 1869). LeafDoctor serves researchers and extension officers by quantifying disease severity in crops like wheat and corn. By estimating the percentage of leaf area affected, LeafDoctor helps monitor the effectiveness of treatment strategies, facilitating more precise disease management decisions over time (Maginga et al., 2024). Table 3 summarizes various smartphone applications used in phytopathogen detection.

Portable diagnostic devices have revolutionized disease surveillance by enabling real-time monitoring directly within agricultural settings. These devices integrated into routine farming practices allow for continuous crop monitoring, crucial for early detection and rapid intervention against fast-spreading pathogens.

The data collected by these portable diagnostics is key to data-driven decision-making, enabling precise, targeted disease management. Field-based diagnostic data help farmers and agricultural managers make informed decisions about interventions, such as targeted fungicide application, removal of infected plants, or effective quarantine measures. For example, information on Xylella fastidiosa infections in olive groves informs selective pruning and vector control strategies (Amoia et al., 2023). Additionally, geotagged diagnostic data can be integrated into regional systems, allowing agricultural networks to track and respond to disease trends more effectively. By mapping infection patterns, stakeholders can anticipate future outbreaks and implement preemptive measures.

Accurate and timely diagnosis of plant pathogens is critical to minimizing agricultural losses and safeguarding global food security. While reliable, traditional diagnostic methods often require extensive laboratory infrastructure, technical expertise, and significant time, limiting their utility in the field. Advances in portable, rapid, and user-friendly diagnostic tools are revolutionizing the management of major pathogen groups, enabling farmers and agronomists to take swift action at the point of need. These novel approaches leverage cutting-edge technologies such as microfluidic LOC platforms, LFIAs, and smartphone-integrated detection systems to provide accessible and precise solutions for pathogen detection.

Table 4 provides a comprehensive list of significant plant pathogens, categorized by their type and primary target host plants. This summary highlights the diverse pathogens affecting key crops and underscores the need for targeted diagnostic and management strategies.

Field-based phytopathogen diagnostics provide essential advantages over traditional centralized laboratory testing, mainly by reducing reliance on labs, which often have long processing times and require specialized equipment and personnel. On-site diagnostic devices allow rapid pathogen detection at the infection site, enabling swift responses to prevent disease spreading. This capability is precious in agricultural regions with limited lab access, empowering farmers to make informed, timely decisions that positively impact crop health and yield.

Successful implementations of field diagnostics illustrate their effectiveness in managing crop diseases. For example, tomato crops face threats from pathogens like Xylella fastidiosa and TYLCV, which can cause severe yield losses if not caught early. Handheld LAMP-based assays for TYLCV have proven effective for rapid, accurate detection in the field, enabling farmers to quickly identify and remove infected plants, thereby reducing the spread of disease. In lettuce cultivation, lab-on-a-chip technologies have been instrumental in managing infections caused by Sclerotinia sclerotiorum, a pathogen that can devastate crops. These portable devices provide results within minutes, allowing targeted fungicide application to protect yields, a clear improvement over traditional lab methods that require days for results (Grabicoski et al., 2020).

Field diagnostics also significantly impact cereal crops, such as wheat, affected by rust caused by Puccinia striiformis. Wheat rust can substantially reduce yields if not promptly controlled. Smartphone-integrated devices allow farmers to detect rust early by capturing and analyzing images of infected leaves via an app, which can also share data with agricultural services for coordinated responses. Real-time analysis and data sharing enhance the effectiveness of interventions like fungicide application and crop rotation, ultimately safeguarding yields. Table 5 presents a performance comparison of various diagnostic devices for plant pathogen detection, detailing their target pathogen types, limits of detection (LOD), response times, and selectivity. This comparison highlights the strengths and applications of different technologies, ranging from rapid detection with high specificity to portable and cost-effective solutions for diverse pathogen types.

Validation of phytopathogen diagnostic devices in resource-poor settings requires robust assessment of specificity, sensitivity, reproducibility, and fitness-for-purpose under field conditions. Resource limitations such as inadequate infrastructure and reagent stability issues necessitate adopting techniques like LAMP, which allows on-site detection without advanced equipment (De Jonghe et al., 2016). Validation begins with stakeholder input to identify pathogens and agroecological constraints, using local sample panels to ensure relevance. For example, validating portable diagnostic devices using local sample panels to detect Ralstonia solanacearum in Kenyan potato fields enhances their relevance and farmer confidence (Okiro et al., 2019). Comparative validation against “gold-standard” methods approved by organizations like IPPC or EPPO is crucial (Conraths and Schares, 2006). A study investigated a nested PCR method against gold-standard techniques, demonstrating its utility for detecting Xanthomonas axonopodis pv. dieffenbachiae, and emphasizing the role of EPPO-endorsed protocols (Chabirand et al., 2013).

A lateral flow device was developed, and PCR-based diagnostic methods were evaluated for Xanthomonas campestris pv. musacearum under field conditions in East Africa, showing high sensitivity and specificity for early disease detection (Hodgetts et al., 2015).

Ruggedness testing is equally vital, ensuring performance under harsh conditions such as fluctuating temperatures or dust. Devices like solar-powered portable PCR kits validated for maize lethal necrosis disease maintained reliability in extreme environments (Snodgrass et al., 2016). Usability testing ensures operational simplicity for users with minimal training, as demonstrated by lateral flow devices (LFDs) for plant pathogen detection, with local technicians in field conditions achieving reliable results through simple operational protocols (Danks and Barker, 2000).

Periodic reassessment of diagnostic tools is crucial to capture pathogen evolution and maintain their robustness. Adaptive frameworks with iterative calibration and field survey feedback ensure diagnostics remain relevant. For example, integrating evolutionary dynamics with immune responses provides insights into pathogen adaptation and highlights iterative calibration’s role in robust management (Restif and Grenfell, 2006). Advances like adaptive sequencing enhance real-time detection and resistance profiling, demonstrating the significance of continuously updated methods (Cheng et al., 2022). Together, these approaches emphasize dynamic, flexible systems essential for addressing evolving pathogen diagnostics and management challenges.

For instance, validated kits for Cassava Brown Streak Virus in Eastern Africa reduced pesticide sprays, improving profitability and reducing environmental harm (Patil et al., 2015). Scalability and sustainability are assessed by evaluating whether diagnostics can be produced locally or maintained with minimal external inputs. Gender-inclusive approaches ensure equitable benefits, particularly for women who play a significant agricultural role (Jost et al., 2015). Integrated validation frameworks that combine diagnostics with data analytics show promise. Handheld LAMP devices paired with smartphone imaging provide real-time geo-referenced disease data for community-wide surveillance, enhancing decision-making and agricultural resilience (Paul et al., 2021). Validated diagnostics in resource-poor settings catalyze sustainable agrarian intensification, resilience to emerging pests, and improved livelihoods.

Furthermore, portable diagnostic devices contribute to environmental sustainability by supporting precision agriculture practices. Accurate, on-site detection allows for targeted treatment of affected plants, reducing the reliance on broad-spectrum pesticide applications. This precision minimizes unnecessary chemical usage, thereby lessening the environmental impact of agricultural activities. Potable diagnostics are vital in advancing sustainable farming methodologies by promoting efficient use of resources and decreasing the potential for harmful side effects on ecosystems. Table 6 compares the three most commonly used portable phytopathogen diagnostic technologies based on their attributes.

While offering rapid, sensitive, and specific diagnostics, portable diagnostic tools for detecting phytopathogens face significant hurdles that limit their effectiveness and adoption. Despite advancements like LFIA and LAMP assays, these devices struggle with sensitivity and specificity under environmental stressors such as temperature and humidity, leading to false positives and negatives. Detecting pathogens at low concentrations remains challenging, hindering their application in early disease detection. Overcoming these technical limitations through enhanced sensitivity and specificity across diverse environments is crucial (Ali et al., 2021; Dey et al., 2023; Archana et al., 2024).

Standardization and quality assurance are equally critical for ensuring the reliability of these tools. A lack of universal testing protocols and calibration procedures undermines result consistency, complicating regulatory approvals and limiting scalability. This inconsistency reduces user confidence, especially among smallholder farmers and agricultural workers, who rely on these devices for on-site diagnostics (Ali et al., 2021). Advances in nanotechnology, molecular diagnostics, and IoT-enabled systems have begun addressing these gaps. For instance, nanotechnology enhances sensitivity and specificity, enabling reliable on-site detection via accessible devices such as smartphones (Li Z. et al., 2020). Molecular diagnostics like real-time PCR and microchip PCR systems integrate built-in quality assurance features such as temperature regulation and amplification monitoring, ensuring dependable pathogen detection in diverse settings (Koo et al., 2013). IoT-enabled tools provide real-time data collection and cloud-based quality assurance, promoting consistent accuracy in field conditions (Buja et al., 2021).

Durability and operational challenges further constrain portable device usability. Harsh field conditions degrade performance, and maintaining devices, including calibration and component replacement, is often infeasible in remote areas. Standardized molecular protocols, like isothermal amplification and next-generation sequencing, reduce variability through uniform sample preparation and device calibration, while user-friendly tools such as biosensors and lateral flow assays improve accessibility for growers and field workers without requiring extensive training (Hariharan and Prasannath, 2021).

Economic barriers compound these challenges, with high upfront costs, recurring consumable expenses, and training requirements making these devices less accessible to smallholder farmers. Inadequate training exacerbates the risk of data misinterpretation, potentially worsening pathogen spread. Additionally, regulatory and ethical considerations, including compliance with regional agricultural laws and data privacy issues linked to cloud-based and smartphone technologies, create further obstacles to widespread adoption. Table 7 illustrates the advantages and limitations of various portable phytopathogen diagnostic devices.

Portable phytopathogen diagnostic tools hold immense commercial potential by mitigating economic losses, enhancing market stability, and driving innovation in agriculture. Early detection of plant diseases minimizes yield losses, stabilizes market supplies, and ensures product quality, addressing challenges from asymptomatic infections and emerging diseases. These tools streamline field applications by eliminating the reliance on centralized laboratories, with real-time PCR systems and nanotechnology-based devices offering rapid and sensitive detection. Their affordability, particularly with smartphone-integrated systems, makes them viable for resource-limited settings, fostering inclusivity in agricultural markets (Paul et al., 2021).

Integration with IoT amplifies their value by linking diagnostics to real-time data management systems, improving disease monitoring and decision-making. Additionally, their applicability extends beyond agriculture to industries like water quality testing, food safety, and environmental monitoring, enabling manufacturers to diversify revenue streams (Islam et al., 2023). These tools also support sustainable agriculture by reducing agrochemical use through precise interventions and meeting consumer demand for eco-friendly, residue-free produce.

Current portable phytopathogen diagnostic devices face significant technical limitations despite miniaturization and field usability advances. A key constraint is throughput; while laboratory methods like high-throughput qPCR or NGS can process large sample volumes efficiently, portable devices are restricted to a few targets per run, limiting their utility for large-scale surveillance. Handheld molecular assays like LAMP improve speed and convenience but are constrained by their inability to handle large sample loads in field conditions, as seen in their limited use for detecting pathogens like Phytophthora infestans and Botrytis cinerea in agricultural settings (Ristaino et al., 2020).

Multiplexing remains another significant hurdle. Detecting multiple pathogens simultaneously is essential for cost-effectiveness but is limited by constraints such as the cross-reactivity of primers and uneven assay performance. Attempts at multiplexing for pathogens like Pseudomonas syringae, Xanthomonas campestris, and Ralstonia solanacearum often result in reduced accuracy, while detecting phylogenetically diverse pathogens (bacteria, fungi, oomycetes, viruses) poses additional biochemical and logistical challenges (Umesha and Avinash, 2015).

Stability of reagents and devices is critical for field deployment. Reagents must withstand environmental fluctuations, but real-world use often shows degradation in sensitivity, as seen in LAMP-based assays for plant viruses requiring cold-chain logistics. Immunoassays also face stability issues, with antibodies losing efficacy under field conditions (Thekisoe et al., 2009).

In West Africa, molecular assays for detecting cassava pathogens like CBSV struggled with reagent stability under high temperatures (Abarshi et al., 2012). Similarly, vineyard surveillance for pathogens like Plasmopara viticola and Erysiphe necator revealed inefficiencies in multiplexed assays and difficulties in managing plant inhibitors in field settings (Possamai and Wiedemann-Merdinoglu, 2022).

Engineering refinements in microfluidics, robust reagent formulations, advanced bioinformatics tools, and integrated digital platforms are essential to address these issues. Emerging methods like CRISPR-based diagnostics show promise but require further validation. Collaboration across disciplines, investment in infrastructure, and regulatory harmonization are necessary to bridge the gap between laboratory capabilities and field requirements, paving the way for more effective integrated pest management systems.

Traditional diagnostic methods, though reliable, are impractical for on-site use, prompting the development of portable systems that integrate molecular biology, engineering, and data analytics. Advances in isothermal amplification techniques like LAMP and recombinase polymerase amplification (RPA) have enabled quick and efficient pathogen detection without requiring complex thermal cycling equipment. These methods have been successfully applied to detect pathogens such as Phytophthora sojae in soybean fields and Ralstonia solanacearum in bacterial wilt cases, providing actionable insights for immediate field interventions (Miles et al., 2015).

CRISPR-Cas systems have further revolutionized diagnostics, offering unparalleled specificity through programmable Cas12 and Cas13 proteins, enabling rapid pathogen identification from simple samples. Such technologies are adaptable and promise applications for various plant diseases (Li et al., 2022; Prasad et al., 2022). Rapid and efficient insulated isothermal PCR method for detecting Fusarium oxysporum directly from banana crop samples, supporting early diagnosis and containment efforts (Chang et al., 2022).

Emerging technologies such as nanopore sequencing are also transforming field diagnostics. Handheld devices like the Oxford Nanopore MinION deliver real-time pathogen identification, offering strain-level precision even in remote locations, although they currently require technical expertise (Player et al., 2020). Smartphones have further enhanced accessibility by integrating high-resolution imaging and cloud-based data sharing for real-time pathogen detection and geotagged disease monitoring. Smartphone-based fluorescence detectors exemplify the potential for user-friendly diagnostic tools that empower farmers with timely insights (Zhang et al., 2023).

The future of portable diagnostics lies in integrating multiplexed detection, machine learning, and user-friendly interfaces to democratize agricultural disease management. Innovations in synthetic biology, biosensors, and nanotechnology are expected to enhance diagnostic accuracy and accessibility further, empowering farmers to mitigate threats with minimal delay and ensuring sustainable agricultural practices in a changing climate. Figure 15 visualizes various phytodiagnostic techniques used in agriculture.

Scalable manufacturing and cost-reduction strategies for portable phytopathogen detection devices focus on optimizing materials, modular designs, and streamlined production processes. Paper-based lateral flow assays are cost-effective due to their simplicity, compatibility with point-of-need applications, and reliance on mass-produced cellulose membranes (Sena-Torralba et al., 2022). Polymers like cyclic olefin copolymer (COC) and poly(methyl methacrylate) (PMMA), used in microfluidic chip fabrication, offer scalable manufacturing via injection molding, while low-cost sensors, such as LEDs and smartphone cameras, reduce reliance on specialized hardware (Liu et al., 2024). Modular designs enable standardization of components, such as interchangeable reagent cartridges, enhancing scalability and reducing per-unit costs.

Reagent stabilization and simplified sample preparation are critical for scalability. Lyophilized primers and isothermal amplification methods like LAMP and RPA enable ambient storage and reduce cold-chain dependence (Carter et al., 2017). Integrated sample-to-answer systems employing miniaturized nucleic acid extraction modules have been successfully used to detect pathogens like Phytophthora infestans in potato fields, empowering smallholder farmers with rapid diagnostic capabilities (Paul et al., 2021). Bulk purchasing of reagents, automated assembly lines, and robotic manufacturing streamline production while maintaining reliability (Ortiz et al., 2017).

Collaborative approaches play a vital role in cost reduction. Partnerships between public and private sectors, supported by agencies like the International Plant Protection Convention (IPPC), facilitate technology transfer, standardization, and large-scale production (Boyer et al., 2016). Domestic manufacturing reduces import costs and enhances local availability in low- and middle-income countries, while philanthropic and governmental support ensures affordability. Automated quality control measures using standardized reference strains, assuring product reliability across regions (Adiga et al., 2018).

Real-world examples highlight these strategies. Mass-produced PCR-based lateral flow kits for Cassava Brown Streak Virus in East Africa demonstrated reduced costs through local manufacturing and smartphone-based detection (Munguti et al., 2024). Similarly, a low-cost lateral flow device for field detection of Xanthomonas campestris pv. musacearum in banana plantations aids in rapid outbreak mitigation and reduces losses (Hodgetts et al., 2015). Such efforts create a sustainable ecosystem for innovation, enabling widespread deployment of portable phytopathogen detection devices and bolstering global food security.

Linking portable phytopathogen diagnostics to global surveillance and early warning systems integrates advanced molecular detection, sensor technologies, and data-driven networks for rapid on-site disease assessment. Portable tools, such as isothermal amplification techniques like LAMP, enable quick and reliable detection without laboratory infrastructure, producing results within 30–60 min (Parida et al., 2008). CRISPR-based detection platforms add specificity and sensitivity, making pathogen identification highly precise and accessible. Smartphone imaging and biosensor systems enhance portability by integrating high-resolution cameras, image recognition, and cloud-based analysis, enabling real-time data sharing with global networks. These advancements align with global initiatives like the IPPC and FAO’s surveillance frameworks, ensuring that diagnostic data feed into platforms like the International Phytosanitary Portal and GPPIS for immediate access by plant health authorities (International Plant Protection Convention (IPPC), 2016). For example, LAMP kits reduce instances of Phytophthora spp. and Phytophthora cactorum in strawberry fields (Siegieda et al., 2021) and rapid diagnostics curtailing downy mildew in vineyards through timely fungicide application (Radhey et al., 2024). As climate shifts alter pathogen distributions, portable diagnostics ensure real-time disease mapping and swift interventions, fortifying global capacity to mitigate threats (Buja et al., 2021).

Empowering local networks through training in assay interpretation, data logging, and sample handling builds decentralized surveillance systems capable of early pathogen detection. Public-private partnerships can improve kit affordability and innovation, while international agencies ensure cross-regional data accuracy through proficiency testing (Markell et al., 2020). Emerging technologies, such as drones and satellites equipped with diagnostic tools, offer the potential for real-time infection mapping and outbreak prediction through integrated data systems. Such innovations advance global preparedness, fostering resilient and collaborative responses to safeguard food security and biodiversity (Abbas et al., 2023).

The development of portable phytopathogen detection devices offers significant potential for modern agriculture by enabling rapid and accurate disease diagnosis to reduce crop losses. To ensure their successful adoption, robust policies, and stakeholder engagement are crucial. Governments should incentivize research and development through grants and tax benefits while fostering public-private partnerships to accelerate prototyping and testing. Clear regulatory guidelines are essential to guarantee safety, reliability, and data security, while privacy policies should balance farmer confidentiality with the benefits of aggregated data for disease monitoring and early warning systems. Market accessibility can be enhanced by subsidies, reduced tariffs, and microcredit systems, particularly for smallholder farmers in low-income regions.

Active stakeholder involvement is critical throughout the development and deployment processes. Farmers, as primary users, must be included in co-design efforts to ensure the devices meet real-world needs. Research institutions and agricultural organizations are instrumental in tailoring detection mechanisms to region-specific pathogens and validating device performance. Collaboration with agrarian technology companies supports mass production and ensures alignment with market demands, while engagement with policymakers facilitates streamlined regulatory approvals and integration with agricultural health priorities. International organizations like the FAO can provide technical assistance, advocate for standardization, and support deployment in underserved areas. Additionally, NGOs can enhance access in remote farming communities.

Building capacity among agricultural extension workers ensures that the devices are correctly utilized, maximizing their effectiveness. Cost barriers and adoption resistance can be addressed through subsidized manufacturing, NGO partnerships, training programs, and demonstration projects to build trust and demonstrate value. Interoperability issues can be resolved with open data standards and modular designs, enhancing device usability across different systems and regions. A cohesive approach integrating strong policies with comprehensive stakeholder engagement is vital for the widespread adoption of these diagnostic technologies. Such efforts will strengthen agricultural productivity and resilience, ensuring these innovations fulfill their potential to combat crop diseases and enhance global food security.