Search papers, labs, and topics across Lattice.
This review paper synthesizes deep learning methodologies for plant disease detection, focusing on CNN architectures like VGG, ResNet, and EfficientNet. It categorizes classification strategies based on XAI integration and examines object detection models (YOLO, SSD, Faster R-CNN). The review highlights challenges like environmental variability and data imbalance, suggesting solutions such as transfer learning, GANs, and edge computing.
Navigating the jungle of deep learning models for plant disease detection just got easier with this review that distills best practices for dataset selection, model optimization, and deployment on mobile platforms.
Background/aim The rapid advancement of deep learning (DL) has revolutionized plant disease detection by enabling highly accurate, image-based diagnostic solutions. This review provides a comprehensive synthesis of DL-based methodologies for plant disease detection, systematically structured around the key stages of the modeling pipeline, encompassing data acquisition, preprocessing, augmentation, classification, detection, segmentation, and deployment. Materials and methods The review focuses on evaluating convolutional neural network (CNN) architectures such as VGG, ResNet, EfficientNet, and DenseNet across diverse experimental contexts. Classification strategies are categorized according to their integration of visualization techniques (e.g., saliency maps, Grad-CAM) to enhance model interpretability, emphasizing the pivotal role of explainable artificial intelligence (XAI) in plant pathology. Object detection models are systematically examined within both one-stage (YOLO, SSD) and two-stage (Faster R-CNN) paradigms. Furthermore, critical challenges—such as environmental variability, data imbalance, and computational constraints—along with potential solutions including transfer learning, synthetic data generation using generative adversarial networks (GANs) and diffusion models, and edge computing for real-time deployment, are comprehensively discussed. Results This review summarizes best practices for dataset selection and model optimization for mobile platforms, emphasizing their role in improving the efficiency and accuracy of plant disease detection systems. Conclusion Deep learning-based methods show strong potential to enhance precision and resilience in real-world plant disease detection and monitoring.
