Plant Disease Diagnosis Using Deep Learning Based on Aerial Hyperspectral Images: A Review

Abstract

Plant diseases cause considerable economic loss in the global agricultural industry. A current challenge in the agricultural industry is the development of reliable methods for detecting plant diseases and plant stress. Existing disease detection methods mainly involve manually and visually assessing crops for visible disease indicators. The rapid development of unmanned aerial vehicles (UAVs) and hyperspectral imaging technology has created a vast potential for plant disease detection. UAV-borne hyperspectral remote sensing (HRS) systems with high spectral, spatial, and temporal resolutions have replaced conventional manual inspection methods because they allow for more accurate cost-effective crop analyses and vegetation characteristics. This paper aims to provide an overview of the literature on HRS for disease detection based on deep learning algorithms. Prior articles were collected using the keywords “hyperspectral”, “deep learning”, “UAV”, and “plant disease”. This paper presents basic knowledge of hyperspectral imaging, using UAVs for aerial surveys, and deep learning-based classifiers. Generalizations about workflow and methods were derived from existing studies to explore the feasibility of conducting such research. Results from existing studies demonstrate that deep learning models are more accurate than traditional machine learning algorithms. Finally, further challenges and limitations regarding this topic are addressed.

Study on the automatic detection of pepper TSWV disease based on ResNet

“…Furthermore, hyperspectral cameras often sacrifice spatial resolution to achieve superior spectral resolution. Considering these factors, the practical advantages of hyperspectral systems are limited; in many current studies, the observations toward plant disease was reflected through morphological changes, which can also be identified under visible wavelengths [17][18][19][20].…”

A Survey on UAV Applications in Smart City Management: Challenges, Advances, and Opportunities

“…In recent years, with the improvement of UAV load capacity and the miniaturization of hyperspectral cameras, UAVs equipped with light hyperspectral cameras have been widely used, such as in water quality parameter inversion and vegetation index extraction [54], [55]. Compared with satellite hyperspectral remote-sensing technology, mini-UAV-borne hyperspectral remote-sensing (HRS) systems have the advantages of high spatial resolution, high temporal resolution and low cost, which makes the observation more flexible [56]. The hyperspectral instruments that can be carried by UAVs can be divided into four categories.…”

Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection

“…In this paper ( Kuswidiyanto et al., 2022 ), the researchers have developed a deep learning-based system for the accurate identification of plant diseases using photographs of plant symptoms. Leveraging a convolutional neural network (CNN), they successfully extracted features from the images.…”

“…( Kuswidiyanto et al., 2022 ) devised a plant disease identification system based on CNN technology. Their efforts yielded a commendable accuracy rate of 98.34% when applied to a collection of 1,625 images representing four distinct plant diseases.…”