Deep Learning based Orange Crop Disease Detection Using Image based Intelligent Framework for Precision Monitoring

Abstract

Orange crop production is highly vulnerable to fungal, bacterial, and nutrient-related diseases that significantly reduce yield quality and economic productivity. Traditional manual inspection methods are time-consuming, subjective, and often ineffective for early disease diagnosis, creating a need for automated and intelligent monitoring solutions. This study proposes a deep learning–based image-driven framework designed to accurately detect major orange crop diseases using high-resolution leaf and fruit images captured in real field conditions. The methodology integrates image enhancement, segmentation using K-means clustering and Canny edge detection, and preprocessing steps such as resizing, normalization, augmentation, and class balancing. A curated dataset of 3,000 images across six classes—including canker, greening, melanose, black spot, nutrient deficiency, and healthy samples—was used to train multiple CNN architectures (AlexNet, VGG19, and Xception) and a fuzzy rank-based ensemble model. Experimental results demonstrate that the proposed enhanced framework outperforms conventional methods, achieving 96.51% accuracy with the ensemble model, while individual models such as Xception and VGG19 achieve 92.25% and 90.34% accuracy, respectively, confirming its effectiveness for precision disease monitoring in orange orchards.