Apples are one of the most economically significant and widely cultivated fruit crops worldwide, contributing substantially to food security and the horticultural economy. However, their production is frequently compromised by diseases such as Alternaria leaf spot, Apple Mosaic, Powdery Mildew, and Apple Scab, leading to significant yield losses and increased dependence on chemical control. Timely and accurate disease diagnosis is critical to minimize crop damage, reduce pesticide usage, and promote sustainable horticultural practices. This study proposes the Multilayer Transformer-based Apple Disease Classification (MTADC) model, an advanced deep learning framework designed for early and robust identification of apple leaf diseases. MTADC employs a two-stage learning approach: global feature extraction using a transformer encoder, followed by class-specific mapping through a refined classification head. By incorporating a self-attention mechanism, the model effectively suppresses background noise and enhances feature discrimination, even under variable field conditions. Unlike existing models that require well-constrained, high-quality images captured under ideal lighting and angles, MTADC is designed for deployment in real-world field conditions, allowing disease detection from diverse and unconstrained field images commonly captured by farmers. Experiments on a curated dataset comprising publicly available and field-acquired images demonstrate that MTADC achieves a classification accuracy of 96.3%, outperforming conventional convolutional models. These results highlight the model’s robustness, scalability, and potential to be an accessible tool for digital plant health monitoring and precision horticulture.
Rajput et al. (Sun,) studied this question.