An Improved Crop Scouting Technique Incorporating Unmanned Aerial Vehicle-Assisted Multispectral Crop Imaging into Conventional Scouting Practice for Gummy Stem Blight in Watermelon

From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management

In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations.

Catching Spores: Linking Epidemiology, Pathogen Biology, and Physics to Ground-Based Airborne Inoculum Monitoring

Monitoring airborne inoculum is gaining interest as a potential means of giving growers an earlier warning of disease risk in a management unit or region. This information is sought by growers to aid in adapting to changes in the management tools at their disposal and the market-driven need to reduce the use of fungicides and cost of production. To effectively use inoculum monitoring as a decision aid, there is an increasing need to understand the physics of particle transport in managed and natural plant canopies to effectively deploy and use near-ground aerial inoculum data. This understanding, combined with the nuances of pathogen-specific biology and disease epidemiology, can serve as a guide to designing improved monitoring approaches. The complexity of any pathosystem and local environment are such that there is not a generalized approach to near-ground air sampler placement, but there is a conceptual framework to arrive at a "semi-optimal" solution based on available resources. This review is intended as a brief synopsis of the linkages among pathogen biology, disease epidemiology, and the physics of the aerial dispersion of pathogen inoculum and what to consider when deciding where to locate ground-based air samplers. We leverage prior work in developing airborne monitoring tools for hops, grapes, spinach, and turf, and research into the fluid mechanics governing particle transport in sparse canopies and urban and forest environments. We present simulation studies to demonstrate how particles move in the complex environments of agricultural fields and to illustrate the limited sampling area of common air samplers.

Identification and Classification of Downy Mildew Severity Stages in Watermelon Utilizing Aerial and Ground Remote Sensing and Machine Learning

Remote sensing and machine learning (ML) could assist and support growers, stakeholders, and plant pathologists determine plant diseases resulting from viral, bacterial, and fungal infections. Spectral vegetation indices (VIs) have shown to be helpful for the indirect detection of plant diseases. The purpose of this study was to utilize ML models and identify VIs for the detection of downy mildew (DM) disease in watermelon in several disease severity (DS) stages, including low, medium (levels 1 and 2), high, and very high. Hyperspectral images of leaves were collected in the laboratory by a benchtop system (380-1,000 nm) and in the field by a UAV-based imaging system (380-1,000 nm). Two classification methods, multilayer perceptron (MLP) and decision tree (DT), were implemented to distinguish between healthy and DM-affected plants. The best classification rates were recorded by the MLP method; however, only 62.3% accuracy was observed at low disease severity. The classification accuracy increased when the disease severity increased (e.g., 86-90% for the laboratory analysis and 69-91% for the field analysis). The best wavelengths to differentiate between the DS stages were selected in the band of 531 nm, and 700-900 nm. The most significant VIs for DS detection were the chlorophyll green (Cl green), photochemical reflectance index (PRI), normalized phaeophytinization index (NPQI) for laboratory analysis, and the ratio analysis of reflectance spectral chlorophyll-a, b, and c (RARSa, RASRb, and RARSc) and the Cl green in the field analysis. Spectral VIs and ML could enhance disease detection and monitoring for precision agriculture applications.

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers.

Reducing Chlorothalonil Use in Fungicide Spray Programs for Powdery Mildew, Anthracnose, and Gummy Stem Blight in Melons

Fungicides are applied to nearly 80% of U.S. melon acreage to manage the numerous foliar and fruit diseases that threaten yield. Chlorothalonil is the most widely used fungicide but has been associated with negative effects on human and bee health. We designed alternative fungicide programs to examine the impact of reducing chlorothalonil use (Bravo Weather Stik) on watermelon, cantaloupe, and honeydew melon in 2016, 2017, and 2018 in Maryland. Chlorothalonil was replaced in the tank mix of weekly sprays of targeted fungicides with either polyoxin D zinc salt (Oso) or an extract of Reynoutria sachalinensis (Regalia). Powdery mildew (PM; Podosphaera xanthii), gummy stem blight (GSB; Stagonosporopsis spp.), and anthracnose (Colletotrichum orbiculare) were the most prevalent diseases to occur in the 3 years. Replacing chlorothalonil with the biopesticides as the tank-mix component of the fungicide spray program was successful in reducing GSB and PM severity in cantaloupe, honeydew melon, and watermelon compared with the untreated control, with the exception of GSB in 2017 in cantaloupe, and similar to the program including chlorothalonil in all cases, except anthracnose in watermelon. Anthracnose disease severity was not significantly reduced compared with the untreated control when chlorothalonil was replaced with the biopesticides and yields were not improved over the chlorothalonil-alone treatment in any of the trials. Therefore, replacement of chlorothalonil may not fully address its loss as a fungicide resistance management tool but efficacy can be maintained when polyoxin D is alternated with R. sachalinensis as a tank mix with targeted fungicides to manage PM and GSB.

A Review on Drone-Based Data Solutions for Cereal Crops

Food security is a longstanding global issue over the last few centuries. Eradicating hunger and all forms of malnutrition by 2030 is still a key challenge. The COVID-19 pandemic has placed additional stress on food production, demand, and supply chain systems; majorly impacting cereal crop producer and importer countries. Short food supply chain based on the production from local farms is less susceptible to travel and export bans and works as a smooth system in the face of these stresses. Local drone-based data solutions can provide an opportunity to address these challenges. This review aims to present a deeper understanding of how the drone-based data solutions can help to combat food insecurity caused due to the pandemic, zoonotic diseases, and other food shocks by enhancing cereal crop productivity of small-scale farming systems in low-income countries. More specifically, the review covers sensing capabilities, promising algorithms, and methods, and added-value of novel machine learning algorithms for local-scale monitoring, biomass and yield estimation, and mapping of them. Finally, we present the opportunities for linking information from citizen science, internet of things (IoT) based on low-cost sensors and drone-based information to satellite data for upscaling crop yield estimation to a larger geographical extent within the Earth Observation umbrella.

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.