The precise use of specific types of herbicides according to the weed species and density in fields can effectively reduce chemical contamination. A weed species and density evaluation method based on an image semantic segmentation neural network was proposed in this paper. A combination of pre-training and fine-tuning training methods was used to train the network. The pre-training data were images that only contain one species of weeds in one image. The weeds were automatically labeled by an image segmentation method based on the Excess Green (ExG) and the minimum error threshold. The fine-tuning dataset was real images containing multiple weeds and crops and manually labeled. Due to the limitation of computational resources, larger images were difficult to segment at one time. Therefore, this paper proposed a method of cutting images into sub-images. The relationship between sub-image size and segment accuracy was studied. The results showed that the training method reduced the workload of labeling training data while effectively avoiding overfitting. The accuracy of image segmentation decreases as the sub-image size decreases. Considering the limitation of computational resources, a subgraph of \(256 \times 256\) size was selected. The semantic segmentation network achieved 97% overall accuracy. The coefficient of determination (\(R^2\)) of weed density calculated by the algorithm and manually assessed was 0.90, and the root means square error (\(\sigma \)) was 0.05. The method could effectively assess the density of each species of weeds in complex environments. It could provide a reference for accurate spraying herbicides based on weed density and species.

根据田间杂草种类和密度，精准使用特定种类的除草剂，可有效减少化学污染。提出一种基于图像语义分割神经网络的杂草种类和密度评价方法。使用预训练和微调训练方法相结合的方法来训练网络。预训练数据是在一张图像中只包含一种杂草的图像。杂草通过基于过量绿色（ExG）和最小误差阈值的图像分割方法自动标记。微调数据集是包含多种杂草和农作物并手动标记的真实图像。由于计算资源的限制，较大的图像难以一次分割。所以，本文提出了一种将图像切割成子图像的方法。研究了子图像大小与分割精度的关系。结果表明，该训练方法在有效避免过拟合的同时，减少了标注训练数据的工作量。图像分割的准确性随着子图像尺寸的减小而降低。考虑到计算资源的限制，子图选择了\(256 \times 256\)大小。语义分割网络的整体准确率达到了 97%。算法计算并人工评估的杂草密度决定系数（\(R^2\)）为0.90，均方根误差（\(\sigma\)）为0.05。该方法可以有效评估复杂环境中每种杂草的密度。可为基于杂草密度和种类的除草剂精准喷洒提供参考。