ECNNA: Enhanced Convolutional Neural Network with Attention Mechanism for Plant Leaf Disease Classification

Abstract

Recently, the classification of plant leaf diseases has become a critical research area to improve the agricultural productivity.Early and accurate identification of diseases is needed to prevent the disease transmission and reduce the crop losses.Deep learning approaches enables to learn complex meaningful patterns within the various leaves.In the paper, enhanced convolutional neural network with attention mechanism (ECNNA) model is proposed to extract the discriminative features and classify the types of leaf diseases.The ECNNA comprises of prior feature extraction with convolution layers and maximum pooling layers, feature enhancement with attention mechanism, classification using SoftMax classifier.Additionally, the model utilizes data augmentation techniques to increase dataset diversity and improve generalization ability of model.The attention layer is incorporated in convolutional neural network to improve the performance of system that increase crop yield and quality.The system classifies the various diseases categories.Experimental results demonstrate that the proposed ECNNA model achieves the classification accuracy of 99.99% on Potato dataset, 97.27% on Corn dataset, 97.95% on Apple dataset, 99.14% on Grape dataset, 99.62% on Peach dataset, 99.31% on six classes dataset, and 98.90% on eight classes dataset.The classification results are also compared with previous studies, indicating the higher classification rate.This research contributes to the early detection and diagnosis of plant leaf disease for supporting sustainable agriculture and food security.

Code and data availability

The paper uses the public PlantVillage and CCMT datasets, but these are cited third-party datasets rather than paper-specific released assets. No author code, trained model, or data deposit with a public URL is mentioned anywhere in the supplied blocks.

No evidence-backed public reproduction asset is currently recorded.