Entropy-Guided Feature Fusion Deep Learning Framework for Orange Fruit Disease Detection

Abstract

Automated detection and classification of orange diseases will greatly help to save money on fruit deterioration, improve fruit quality, and provide for long-term orange sustainability. Orange diseases usually appear as surface lesions or discoloration and can decrease the market price of the fruit and the likelihood of post-harvest decay. Since current inspection techniques depend heavily on the manual observations of trained inspectors, they are inefficient, biased, and cannot be applied to commercial-scale operations. Therefore, to address these issues, an automatic disease detection and classification framework using a Deep Convolutional Recurrent Neural Network (DCRNN) enabled by the optimization process of an Enhanced Sea Horse Optimization (ESHO) algorithm is developed. Pre-processing images with a Wiener filter to remove noise, CLAHE to amplify image contrast, and color-based segmentation to distinguish areas affected by disease via RGB thresholding is implemented. Next, deep feature extraction is achieved utilizing multiple pretrained convolutional models (i.e., ResNet50, VGG-16, and NasNet), which have different properties and are combined into one model using an entropy-based fusion technique. To achieve better performance by adjusting the hyperparameters of the DCRNN model, an Enhanced Sea Horse Optimization (ESHO) algorithm is utilized. Finally, classification of the disease is achieved using sophisticated machine learning algorithms such as SSAE, MHA-LSTM, and DCRNN. Additionally, Grad-CAM visualization was employed to enhance model interpretability by highlighting the disease-affected regions that influenced the classification decisions. Experiments were conducted using an open-source dataset for orange fruits, demonstrating that proposed ESHO-DCRNN framework produces better results than the traditional deep neural network approaches for detecting orange diseases, reaching 99.50% accuracy.