Multi-component image analysis for citrus disease detection using convolutional neural networks

Abstract

Citrus crops are susceptible to diseases such as Black Spot, Canker, and Greening, which significantly harm both the leaves and the fruits, ultimately reducing overall yield. Traditional visual inspection methods for identifying these diseases are labour-intensive and prone to inaccuracies. The present research proposes a deep learning approach utilizing Convolutional Neural Networks (CNNs) to overcome the limitations of manual inspection. Moreover, it introduces the utilization of combined visual features of citrus leaves and fruits for enhanced disease classification. The proposed multi-component approach demonstrates superior classification performance, achieving more accurate results than single-component-based classifications.

A dataset comprising 12,000 images, distributed across leaves, fruits, and their merged forms, was used for training, validation, and testing. Three CNN models were developed and evaluated: Leaf-Trained, Fruit-Trained, and Multiple Component-Trained CNNs. Performance was assessed using metrics such as accuracy, precision, recall, and F1-score, including their macro values, focusing on model generalization across different input types. The Multiple Component-Trained CNN outperformed the other models, achieving a validation accuracy of 97.75%, followed by the Leaf-Trained CNN at 95.50%. During testing, it also demonstrated superior performance across all input types, with accuracies of 94.75% on the leaf dataset, 92.87% on the fruit dataset, and 96.62% on the merged dataset. The results indicate that Black Spot is the most accurately classified disease, while Canker and Greening are less accurately classified. These findings highlight the potential of integrating various components of plants for enhanced disease classifications.