HEIGHTENING CITRUS HUANGLONGBING DETECTION: INNOVATIONS AND STRATEGIC APPROACHES

Аннотация

Citrus Huanglongbing (HLB) poses a significant threat to citrus production worldwide, necessitating efficient detection methods for early disease identification. In this study, we propose a novel approach for enhancing HLB detection utilizing image feature extraction coupled with a two-stage Backpropagation Neural Network (BPNN) modeling framework. The method integrates advanced image processing techniques to extract relevant features from citrus leaf images, capturing subtle symptoms indicative of HLB infection. Subsequently, a two-stage BPNN model is employed to classify the extracted features, enabling accurate identification of HLB-infected citrus trees. Experimental results demonstrate the effectiveness of the proposed approach in achieving high detection accuracy and robustness across diverse citrus varieties and environmental conditions. The integration of image feature extraction and BPNN modeling represents a promising strategy for advancing citrus HLB detection and facilitating timely disease management practices. KEYWORDS Citrus Huanglongbing, Disease detection, Image processing, Feature extraction, Backpropagation Neural Network (BPNN), Machine learning, Plant pathology.