YR2S: Efficient Deep Learning Technique for Detecting and Classifying Plant Leaf Diseases

Most plant diseases have observable symptoms, and the widely used approach to detect plant leaf disease is by visually examining the affected plant leaves. A model which might carry out the feature extraction without any errors will process the classification task successfully. The technology currently faces certain limitations such as a large parameter count, slow detection speed, and inadequate performance in detecting small dense spots. These factors restrict the practical applications of the technology in the field of agriculture. Hence, this work is focused on devising an optimized framework that works based on YOLOv7 that encompasses the preprocessing and hybrid optimization techniques. This proposed YR2S(YOLO-Enhanced Rat Swarm Optimizer - Red Fox Optimization(RFO-ShuffleNetv2) has been devised. After the preprocessing feature maps are generated using PCFAN. Later, these feature maps are used for the detection of leaves. ShuffleNet with ERSO is used to optimize the classification process. Segmentation of the area prone to disease could be identified through the FCN-RFO. This framework is deployed on the customized dataset which comprehends images of various plant leaves. The leaf disease dataset is used for simulating and assessing the model. The experimental analysis reveals that the proposed method can effectively classify and detect leaf disease with high accuracy i.e., 99.69%, which outperforms the state-of-the-art approaches in the literature. Practical implication shows that the proposed deep learning classifiers are efficient and highly accurate.

大多数植物疾病均具有可观测的症状，而检测植物叶片病害的常用方法即为通过视觉检查受影响的植物叶片。一个能够在特征提取过程中毫无误差地执行任务的模型，将能够成功地完成分类任务。当前技术面临一定的局限性，如参数数量庞大、检测速度缓慢以及在检测细小密集斑点方面性能不足。这些因素限制了该技术在农业领域的实际应用。因此，本研究致力于设计一个基于YOLOv7的优化框架，该框架融合了预处理与混合优化技术。所提出的YR2S（YOLO-Enhanced Rat Swarm Optimizer - Red Fox Optimization(RFO-ShuffleNetv2)）模型得以构建。通过PCFAN生成预处理特征图后，这些特征图被用于叶片的检测。采用带有ERSO的ShuffleNet对分类过程进行优化。通过FCN-RFO可以识别出易患病的区域分割。该框架在自定义数据集上得以部署，该数据集包含了多种植物叶片的图像。叶片病害数据集被用于模型的模拟与评估。实验分析表明，所提出的方法能够有效地进行叶片病害的分类与检测，准确率高达99.69%，在文献中的现有方法中表现卓越。实际应用表明，所提出的深度学习分类器既高效又极为精确。