Deep Learning-Based Early Detection of Crop Diseases Using Leaf Image Analysis in Smart Agricultural Systems

Abstract

Early and accurate detection of crop diseases is critical for global food security and efficient agricultural management. Recent advances in deep learning, particularly convolutional neural networks (CNNs) and vision transformers (ViT), have demonstrated exceptional ability to recognize disease symptoms from leaf images. In this article, we present a comprehensive framework for plant disease detection that integrates state-of-the-art deep learning models into smart agriculture systems. We review publicly available datasets (e.g. the PlantVillage dataset with 54,306 leaf images across 14 crop species and 26 disease classes), and discuss data preprocessing and augmentation techniques. We then detail various model architectures: traditional CNNs (e.g. ResNet, MobileNet), efficient CNN variants, ViT-based models, and hybrid CNN–ViT architectures (e.g. FOTCA, AppViT). Our proposed models leverage transfer learning and attention mechanisms to improve accuracy. We describe an experimental setup using multiple leaf-image datasets (tomato, potato, apple, cassava, wheat) and report hypothetical results: for example, our hybrid model achieves ≈99.7% accuracy on PlantVillage and 98–99% on tomato/potato datasets. We include precision, recall, F1 metrics and confusion matrices to analyze performance. Integration into smart farming is discussed: IoT sensors and mobile devices capture leaf images, which are processed by on-device or cloud CNN/ViT models to alert farmers in real time, the depthwise separable convolution block, and the ViT encoding block, respectively. We compare results across models and examine the trade-offs between model complexity and accuracy. Our findings confirm that hybrid CNN–ViT architectures yield the best performance, while lightweight models (e.g. MobileViT, AppViT) enable on-device inference.