Application of multispectral reflectance for early detection of tomato disease

Automatic diagnosis of plant disease is important for plant management and environmental preservation in the future. The objective of this study is to use multispectral reflectance measurements to make an early discrimination between the healthy and infected plants by the strain of tobacco mosaic virus (TMV-U1) infection. There were reflectance changes in the visible (VIS) and near infrared spectroscopy (NIR) between the healthy and infected plants. Discriminant models were developed using discriminant partial least squares (DPLS) and Mahalanobis distance (MD). The DPLS models had a root mean square error of calibration (RMSEC) of 0.397 and correlation coefficient (r) of 0.59 and the MD model correctly classified 86.7% healthy plants and up to 91.7% infected plants.

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