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.

A CNN-based Image Detector for Plant Leaf Diseases Classification

Laura Falaschetti
Primo
;
Lorenzo Manoni
Secondo
;
Claudio Turchetti
Ultimo
2022

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.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/306724
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact