Rice leaf diseases is a threat to sustainable rice production. Many methods via machine learning or artificial intelligence have been used to detect rice leaf diseases. However, these methods may fail to identify them or are slow in recognition. Therefore, a YOLO V5-IMPROVEMENT model is proposed to overcome these issues. Based on YOLO V5s, the improved K-Means is utilized to generate initial anchor sizes which more fit in the experimental dataset. An attention mechanism is added to let the feature extraction layer focus more on Regions of Interest (ROIs). This improvement enhances the efficiency of feature information extraction from objects of different scales. To better detect occluded targets, the loss function is improved to strengthen the regression effect of the predicted bounding boxes. Main branch gradient flow BottleNeck Block module of Cross Stage Partial (CSP) in the Neck is improved. The Spatial Pyramid Pooling-Fast (SPPF) is replaced with the S-SPPF module to optimize the model structure and ensure real-time performance. An ablation study was conducted and the proposed YOLO V5-EFFICIENT was compared with other models. Compared with YOLO V5, the detection result of YOLO V5-EFFICIENT increased by 9.90% in terms of mAP. Compared with other approaches, the improved model can better balance detection accuracy and speed when recognizing fine features of lesions. And it is capable of handling rice leaf disease detection tasks with numerous small targets. It is suggested that YOLO V5-EFFICIENT is of high accuracy and robustness.

中文翻译：

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基于YOLO V5-EFFICIENT的水稻叶部病害智能识别


水稻叶部病害对可持续水稻生产构成威胁。许多通过机器学习或人工智能的方法已被用于检测水稻叶部疾病。然而，这些方法可能无法识别它们或者识别速度很慢。因此，提出了YOLO V5-IMPROVMENT模型来克服这些问题。基于YOLO V5s，利用改进的K-Means生成更适合实验数据集的初始锚点大小。添加了注意力机制，让特征提取层更多地关注感兴趣区域（ROI）。这一改进提高了从不同尺度的对象中提取特征信息的效率。为了更好地检测被遮挡的目标，改进了损失函数以增强预测边界框的回归效果。对Neck中的Cross Stage Partial (CSP)主分支梯度流BottleNeck Block模块进行了改进。 Spatial Pyramid Pooling-Fast（SPPF）替换为S-SPPF模块，优化模型结构并保证实时性。进行了消融研究，并将所提出的 YOLO V5-EFFICIENT 与其他模型进行了比较。与YOLO V5相比，YOLO V5-EFFICIENT的检测结果在mAP方面提高了9.90%。与其他方法相比，改进的模型在识别病变的精细特征时可以更好地平衡检测精度和速度。能够处理大量小目标的水稻叶部病害检测任务。表明YOLO V5-EFFICIENT具有高精度和鲁棒性。