Abstract
Tomato crops are susceptible to multiple diseases, several of which may be present during the same season. Therefore, rapid disease identification could enhance crop management consequently increasing the yield. In this study, nondestructive methods were developed to detect diseases that affect tomato crops, such as bacterial spot (BS), target spot (TS), and tomato yellow leaf curl (TYLC) for two varieties of tomato (susceptible and tolerant to TYLC only) by using hyperspectral sensing in two conditions: a) laboratory (benchtop scanning), and b) in field using an unmanned aerial vehicle (UAV-based). The stepwise discriminant analysis (STDA) and the radial basis function were applied to classify the infected plants and distinguish them from noninfected or healthy (H) plants. Multiple vegetation indices (VIs) and the M statistic method were utilized to distinguish and classify the diseased plants. In general, the classification results between healthy and diseased plants were highly accurate for all diseases; for instance, when comparing H vs. BS, TS, and TYLC in the asymptomatic stage and laboratory conditions, the classification rates were 94%, 95%, and 100%, respectively. Similarly, in the symptomatic stage, the classification rates between healthy and infected plants were 98% for BS, and 99–100% for TS and TYLC diseases. The classification results in the field conditions also showed high values of 98%, 96%, and 100%, for BS, TS, and TYLC, respectively. The VIs that could best identify these diseases were the renormalized difference vegetation index (RDVI), and the modified triangular vegetation index 1 (MTVI 1) in both laboratory and field. The results were promising and suggest the possibility to identify these diseases using remote sensing.
Highlights
Disease identification can be a very complicated procedure because it needs experienced personnel and frequent field monitoring
The radial basis function (RBF) method was less accurate than the stepwise discriminate method (STDA) classification method in the major classification categories
This study developed novel techniques that can be used in the laboratory and field for the identification and classification of three critical tomato diseases, bacterial spot (BS), target spot (TS), and tomato yellow leaf curl (TYLC), by utilizing hyperspectral imaging and machine learning
Summary
Disease identification can be a very complicated procedure because it needs experienced personnel and frequent field monitoring. Multispectral and hyperspectral sensing has been utilized for pest, plant disease, and stress detection with promising results [4,5,6] These sensors measure the light reflection from an object (e.g., plant canopy). Any change (e.g., caused by a disease) that might occur and disturb the canopy or the leaf surface would affect the light reflectance and diffuse the light direction By detecting these changes, it is possible to identify abnormalities in plants. The most important benefit of these remote sensing technologies and techniques is that they could detect and distinguish a disease even in an asymptomatic stage (before symptoms become obvious to direct visual observations), and this early detection could help to efficiently control and manage a disease and its spread. Whetton et al [8] utilized hyperspectral imaging to detect yellow rust and Fusarium head blight in cereal crops and determined the best wavelengths (500–700 nm) that were capable to distinguish the diseases in early development stages
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