Deep Learning-Based Improved ResNet Model for Accurate Detection of Jackfruit Leaf Diseases
Year: 2025
Type: article
Abstract: Jackfruit leaf diseases caused by bacteria, fungi, or environmental stress significantly affect tree health and fruit yield. This study presents a Deep Learning (DL)-based approach for accurately identifying and classifying jackfruit leaf diseases. The proposed method includes a preprocessing stage with techniques—flipping, rotation, noise reduction, contract adjustment and color enhancement—to improve dataset diversity, followed by a classification stage using an improved ResNet architecture is employed to perform disease classification. Experiments were conducted on a publicly available Kaggle dataset of jackfruit leaf images (three classes: Healthy Leaf, Black Spot, and Algal Leaf Spot). Implemented in MATLAB, the proposed model achieved a classification accuracy of 99.25%, outperforming existing conventional approaches across precision, recall, F1-score, sensitivity, and specificity. The results demonstrate the reliability and robustness of the proposed model in detecting jackfruit leaf diseases.
Authors: Radhika Gunasekaran, Mahendran Thambusamy
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4414217698