He Yuelong, Peng Yunfeng, Wei Chuyong, Zheng Yuda, Yang Changcai, Zou Tengyue
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Fujian Key Laboratory of Agricultural Information Sensoring Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Plants (Basel). 2024 Sep 11;13(18):2556. doi: 10.3390/plants13182556.
Strawberries are susceptible to various diseases during their growth, and leaves may show signs of diseases as a response. Given that these diseases generate yield loss and compromise the quality of strawberries, timely detection is imperative. To automatically identify diseases in strawberry leaves, a KTD-YOLOv8 model is introduced to enhance both accuracy and speed. The KernelWarehouse convolution is employed to replace the traditional component in the backbone of the YOLOv8 to reduce the computational complexity. In addition, the Triplet Attention mechanism is added to fully extract and fuse multi-scale features. Furthermore, a parameter-sharing diverse branch block (DBB) sharing head is constructed to improve the model's target processing ability at different spatial scales and increase its accuracy without adding too much calculation. The experimental results show that, compared with the original YOLOv8, the proposed KTD-YOLOv8 increases the average accuracy by 2.8% and reduces the floating-point calculation by 38.5%. It provides a new option to guide the intelligent plant monitoring system and precision pesticide spraying system during the growth of strawberry plants.
草莓在生长过程中易受多种病害影响，叶片可能会出现病害症状作为反应。鉴于这些病害会导致产量损失并影响草莓品质，及时检测至关重要。为了自动识别草莓叶片上的病害，引入了KTD-YOLOv8模型以提高准确性和速度。采用内核仓库卷积来替换YOLOv8主干中的传统组件，以降低计算复杂度。此外，添加了三重注意力机制以充分提取和融合多尺度特征。此外，构建了一个参数共享的多样分支块（DBB）共享头，以提高模型在不同空间尺度上的目标处理能力，并在不增加过多计算量的情况下提高其准确性。实验结果表明，与原始的YOLOv8相比，所提出的KTD-YOLOv8平均准确率提高了2.8%，浮点计算减少了38.5%。它为草莓植株生长期间的智能植物监测系统和精准农药喷洒系统提供了新的选择。
