CNN-BASED IMAGE DETECTOR FOR PLANT LEAF DISEASES CLASSIFICATION

Abstract: Identifying diseases from images of plant leaves is one of the most important research areas in precision agriculture. The aim of this paper is to propose an image detector embed ding a resource constrained convolutional neural network (CNN) implemented in a low cost, low power platform, named Open MV Cam H7 Plus, to perform a real- time classification of plant disease. The CNN network so obtained has been trained on two specific data sets for plant diseases detection, the ESCA-dataset and the Plant Village-augmented dataset, and implemented in a low-power, low-cost Python programmable machine vision camera for real-time image acquisition and classification, equipped with a LCD display showing to the user the classification response in real-time. Experimental results show that this CNN based image detector can be effectively implemented on the chosen constrained-resource system, achieving an accuracy of about 98.10%/95.24% with a very low memory cost (718. 961 KB/735.727 KB) and inference time (122.969ms/125.630ms) tested on board for the ESCA and the Plant Village-augmented datasets respectively, allowing the design of a portable embedded system for plant leaf diseases classification.

Keywords: Image detector Esca disease convolutional neural network embedded systems plant diseases recognition