An Integrated Deep Learning and Sparse Representation Framework for Apple Leaf Disease Recognition

Proper identification of apple leaf diseases is an important issue towards safeguarding crop production as well as management sustainability of orchards. Manual diagnosis is widely used, but not efficient, subjective, and cannot be easily used to diagnose large-scale monitoring. This paper presents a Hybrid Deep Learning and Sparse Classification Framework, which combines the use of YOLOv8 to extract deep features with L1-regularized machine learning models to do the efficient and interpretable disease recognition. A dataset of 4,006 high-resolution images of apple leaf which had a total of 9 classes which included Alternaria leaf spot, Brown spot, Frogeye leaf spot, gray spot, Healthy, Mosaic, Powdery mildew, Rust, and Scab, was used. After 60 epochs, YOLOv8 attained a mean Average Precision (MAP) of 98.10% at 0.5, a precision value of 95.16%, and a recall of 95.97, which is quite good in terms of detection. The 3,648 resulting dimension feature space was then narrowed down to 319 dimensions using Lasso feature selection and 13.48 percent of sparsity and without losing important discriminatory data. The Lasso model achieved the best test accuracy of 97.50 percent and highest cross-validation was 98.78 percent, which is better than the Logistic Regression (L1) and random forest baselines among the compared classifiers. Sparse regularization integration did not only reduce overfitting but also led to an improved model interpretability, by forcing the visual patterns to focus on diseases. The suggested model is a computationally efficient, scalable, and interpretable AI model to real-time agricultural diagnostics. It offers a bright future to the precision farming systems that can not only detect diseases early but also protect their yields and make better decisions based on interpretable hybrid intelligence.