Machine Learning Review for Early Plant Leaf Disease Detection

Early detection of plant leaf diseases is crucial for ensuring crop health and yield in precision agriculture. Machine learning techniques, particularly convolutional neural networks (CNNs) and transfer learning, have significantly outperformed traditional feature-based methods in accuracy and robustness. Public datasets such as Plant Village, coupled with preprocessing techniques like augmentation, have enabled the development of scalable detection frameworks. Recent studies highlight the effectiveness of lightweight architectures (e.g., MobileNet, FourCropNet) and IncMB-enhanced Inception models, achieving classification accuracies above 99% on benchmark datasets. Furthermore, hybrid models that combine CNNs with classical classifiers such as SVM, along with Vision Transformer (ViT)-based approaches, have improved early symptom recognition under diverse field conditions. Real-time implementations using YOLO variants and attention-augmented MobileNet backbones demonstrate the feasibility of deploying disease detection models on mobile and edge devices. Despite these advances, challenges persist, including dataset imbalance, environmental variability, and limited generalization across crop species. Future directions include model compression, multimodal integration (hyperspectral, thermal imaging), and federated learning to enhance adaptability and large-scale deployment. Overall, machine-learning-driven image analysis represents a promising pathway for early disease detection, supporting sustainable agriculture and global food security.