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GamaNNet: A Novel Plant Pathologist-Level CNN Architecture for Intelligent Diagnosis

Authors :
Marcio Oliveira
Adunias Teixeira
Guilherme Barreto
Cristiano Lima
Source :
AgriEngineering, Vol 6, Iss 3, Pp 2623-2639 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Plant pathologies significantly jeopardise global food security, necessitating the development of prompt and precise diagnostic methods. This study employs advanced deep learning techniques to evaluate the performance of nine convolutional neural networks (CNNs) in identifying a spectrum of phytosanitary issues affecting the foliage of Solanum lycopersicum (tomato). Ten thousand RGB images of leaf tissue were subsampled in training (64%), validation (16%), and test (20%) sets to rank the most suitable CNNs in expediting the diagnosis of plant disease. The study assessed the performance of eight well-known networks under identical hyperparameter conditions. Additionally, it introduced the GamaNNet architecture, a custom-designed model optimised for superior performance on this specific type of dataset. The investigational results were most promising for the innovative GamaNNet and ResNet-152, which both exhibited a 91% accuracy rate, as evidenced by their confusion matrices, ROC curves, and AUC metrics. In comparison, LeNet-5 and ResNet-50 demonstrated lower assertiveness, attaining accuracies of 74% and 69%, respectively. GoogLeNet and Inception-v3 emerged as the frontrunners, displaying diagnostic preeminence, achieving an average F1-score of 97%. Identifying such pathologies as Early Blight, Late Blight, Corynespora Leaf Spot, and Septoria Leaf Spot posed the most significant challenge for this class of problem.

Details

Language :
English
ISSN :
26247402
Volume :
6
Issue :
3
Database :
Directory of Open Access Journals
Journal :
AgriEngineering
Publication Type :
Academic Journal
Accession number :
edsdoj.7fa2223520a84e3d9eb40c2a6e614baa
Document Type :
article
Full Text :
https://doi.org/10.3390/agriengineering6030153