基于分水岭算法结合卷积神经网络的玉米种子质量检测

为实现玉米种子快速、准确地优选，以不同质量的玉米种子为研究对象，提出一种分水岭算法结合卷积神经网络对玉米种子进行质量检测的方法。首先利用分水岭算法分割出单粒玉米种子，然后通过卷积神经网络模型对每粒种子进行质量分类，根据分水岭算法得到的单粒种子的位置，将结果在图像中进行标注，实现种子质量检测。使用改进型的InceptionV3模型进行试验，模型测试结果表明：质量良好和带有缺陷的两类种子的平均准确率为94.18%，平均召回率为94.61%，F1值（调和平均评价）为94.39%。同时为突出卷积神经网络模型的性能，将结果与传统的机器学习方法进行比较，其F1值高出LBP+SVM模型20.39%。

Corn seed quality detection based on watershed algorithm and convolutional neural network

In order to realize the fast and accurate optimization of corn seeds, a watershed algorithm combined with convolutional neural network was proposed to detect the quality of corn seeds with different quality as the research object. Firstly, the watershed algorithm was used to divide the single corn seed, and then the quality of each seed was classified by the convolutional neural network model. According to the position of the single seed obtained by the watershed algorithm, the results were labeled in the image to realize the quality detection of seeds. The improved InceptionV3 model was used to test the seeds. The test results showed that the average accuracy rate, the average recall rate and the F1 value (harmonic average evaluation) of the two kinds of seeds with good quality and defects were 94.18%, 94.61% and 94.39%. Meanwhile, in order to highlight the performance of the convolutional neural network model, the results were compared with the traditional machine learning method, and the F1 value of the convolutional neural network model was 20.39% higher than that of the LBP+SVM model.