Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques

• Published in: Precision agriculture Vol. 21; no. 5; pp. 955 - 978
• Main Authors: Abdulridha, Jaafar, Ampatzidis, Yiannis, Kakarla, Sri Charan, Roberts, Pamela
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2020; Springer Nature B.V
• Subjects: Agriculture; Asymptomatic; Atmospheric Sciences; Biomedical and Life Sciences; Chemistry and Earth Sciences; Chlorophyll; Classification; Computer Science; Corynespora; Diagnosis; Discriminant analysis; Disease detection; Fungi; Hyperspectral imaging; Identification methods; Imaging techniques; Laboratories; Leaves; Life Sciences; Medical imaging; Multilayer perceptrons; Neural networks; normalized difference vegetation index; Normalized difference vegetative index; Photochemicals; photochemistry; Physics; Plant diseases; precision agriculture; reflectance; Remote Sensing/Photogrammetry; Signs and symptoms; Soil Science & Conservation; Spot; Statistics for Engineering; Target detection; Target spot; Tomatoes; Unmanned aerial vehicles; Vegetation; Vegetation index; Xanthomonas perforans; Spectral vegetation indices; UAV; Classification methods; Disease detection; Hyperspectral; Remote sensing

Early and accurate diagnosis is a critical first step in mitigating losses caused by plant diseases. An incorrect diagnosis can lead to improper management decisions, such as selection of the wrong chemical application that could potentially result in further reduced crop health and yield. In tomato...