A CNN-based image detector for plant leaf diseases classification. (October 2022)

• Record Type: Journal Article
• Authors: Falaschetti, Laura; Manoni, Lorenzo; Di Leo, Denis; Pau, Danilo; Tomaselli, Valeria; Turchetti, Claudio
• Is Part Of: HardwareX. Volume 12(2022)
• Publication Date: 2022-10
• Subjects: Image detector -- Esca disease -- convolutional neural network -- embedded systems -- plant diseases recognition; Scientific apparatus and instruments -- Periodicals; Laboratories -- Equipment and supplies -- Periodicals; Laboratories -- Equipment and supplies; Scientific apparatus and instruments; Periodicals; Electronic journals; 681
• DOI: 10.1016/j.ohx.2022.e00363
• Languages: English
• ISSNs: 2468-0672

Abstract

Graphical abstract: Our article proposes an image detector embedding a resource constrained convolutional neural network (CNN) implemented in a low cost, low power platform, named OpenMV Cam H7 Plus, to perform a real-time classification of plant disease. The CNN network so obtained has been trained on two specific datasets for plant diseases detection, the ESCA-dataset and the PlantVillage-augmented dataset, and implemented in a low-power, low-cost Python programmable machine vision camera for real-time 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 embedding a resource constrained convolutional neural network (CNN) implemented in a low cost, low power platform, named OpenMV Cam H7 Plus, to perform a real-time classification of plant disease. The CNN network so obtained has been trained on two specific datasets for plant diseases detection, the ESCA-dataset and the PlantVillage-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 (122.969 ms/125.630 ms) tested on board for the ESCA and the PlantVillage-augmented datasets respectively, allowing the design of a portable embedded system for plant leaf diseases classification. Source files are available at https://doi.org/10.17605/OSF.IO/UCM8D .