Abstract
Early detection of pests and diseases can significantly increase crop yield in agricultural fields. Farmers normally use visible characteristics to identify and characterize different pests and diseases. In some cases, human experts are hired to identify the resulting anomalies. All these measures are time-consuming, expensive, and impractical in some cases. The objective of this paper was to review the previous research related to pests and diseases detection in fruit and vegetable plants using machine vision techniques, focusing on visible RGB images. . Based on captured images of infected leaves or pests, machine vision techniques were applied to give a general idea about disease/pest infestation. Machine vision applications in agriculture have been encouraging for several decades, and the steady development of useful vision algorithms has been well matched by modern computers' ability to implement them at sufficiently high speeds to make them viable. The development of these vision systems has involved moving from academic research to agricultural application, which presents more problems to solve to ensure mature application in the real natural environment.
Figures & Tables
Fig. 1. Common diseases and pest infestation to tomato plants. a) Leaf mold b) Gray mold c) Canker d)Canker e) Miners f) Whitefly (Modified from Fuentes et al., 2017).
