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
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-019-09703-4

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, initial disease symptoms may be similar even if caused by different pathogens, for example early lesions of target spot (TS) caused by the fungus Corynespora cassicola and bacterial spot (BS) caused by Xanthomonas perforans . In this study, hyperspectral imaging (380–1020 nm) was utilized in laboratory and field (collected by an unmanned aerial vehicle; UAV) settings to detect both diseases. Tomato leaves were classified into four categories: healthy, asymptomatic, early and late disease development stages. Thirty-five spectral vegetation indices (VIs) were calculated to select an optimum set of indices for disease detection and identification. Two classification methods were utilized: (i) multilayer perceptron neural network (MLP), and (ii) stepwise discriminant analysis (STDA). Best wavebands selection was considered in blue (408–420 nm), red (630–650 nm) and red edge (730–750 nm). The most significant VIs that could distinguish between healthy leaves and diseased leaves were the photochemical reflectance index (PRI) for both diseases, the normalized difference vegetation index (NDVI850) for BS in all stages, and the triangular vegetation index (TVI), NDVI850 and chlorophyll index green (Chl green) for TS asymptomatic, TS early and TS late disease stage respectively. The MLP classification method had an accuracy of 99%, for both BS and TS, under field (UAV-based) and laboratory conditions.