Deep learning-based citrus plant disease classification using a computationally efficient CNN model

Abstract

Recent advancements in domain-specific classification methods have demonstrated the remarkable performance of deep learning in comparison to traditional machine learning techniques. This study develops a computationally efficient Convolutional Neural Network (CNN) model tailored for citrus plant disease classification, achieving performance comparable to that of the pretrained InceptionV3 model. A custom five-layer CNN model is constructed to classify citrus plant diseases into healthy and diseased categories using images collected from citrus orchards in Northen India. The model has been further validated using images sourced from GitHub and the Kaggle database. The proposed method surpasses classical machine learning approaches in accuracy and computational efficiency, achieving classification accuracies of 92.59%. The training time of the proposed CNN AgriVision-L5 is reduced by 50%, respectively, compared to the InceptionV3 model, demonstrating their computational efficiency. The proposed methodology offers significant advancements in plant disease management and sustainable agriculture, aligning with Sustainable Development Goals like SDG2, SDG9, and SDG12.