Abstract
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.
Highlights
The major objective of this paper is to provide a holistic explanation of how Unmanned Aerial Vehicles (UAVs) can be used to automatically monitor the health status of the crop in the field
K-means clustering is a common clustering algorithm used in various application domains, such as image segmentation [130], which divides a dataset into k groups [131]
The process of automatic identification of plant diseases using UAVs and deep learning can be improved by choosing a high-quality image capturing camera, appropriate sensors (RGB, multispectral or hyperspectral) depending upon the purpose of study, enough datasets to accurately train the model, and selecting appropriate architecture for the deep learning model
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. PlantVillage Nuru has been integrated with the West African viral epidemiology platform (WAVE 2) to track the spread of cassava brown streak disease Out of these applications, the most extensive use of UAVs in agriculture has been for the detection of stress in plants and quantification. This paper tries to explore different UAV platforms and their limitations and advantages, cameras, and sensors with their spectral specifications to capture images and acquire data for monitoring and detecting plant diseases. It reflects different methods of processing acquired data, the challenges in the process and the prospects of autonomous identification of plant diseases using Unmanned Aerial Vehicles. The total field coverage per flight duration [54]
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