论文标题
基于汽车的作物和杂草分类
Crop and weed classification based on AutoML
论文作者
论文摘要
CNN模型已经在高精度的作物和杂草分类中起着重要作用，如文献报道的那样，超过95％。但是，在大多数传统实践和研究中，手动选择和微调深度学习模型变得艰巨且必不可少。此外，经典的目标功能与农业耕作任务不完全兼容，因为相应的模型遇到了错误分类为杂草的分类，通常比其他深度学习应用领域更有可能。在本文中，我们使用新的目标功能应用了自动驾驶机器学习，以进行农作物和杂草分类，达到更高的准确性和较低的作物杀伤率（将作物识别为杂草的速率）。实验结果表明，我们的方法优于最先进的应用程序，例如Resnet和VGG19。
CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune the deep learning models becomes laborious and indispensable in most traditional practices and research. Moreover, the classic objective functions are not thoroughly compatible with agricultural farming tasks as the corresponding models suffer from misclassifying crop to weed, often more likely than in other deep learning application domains. In this paper, we applied autonomous machine learning with a new objective function for crop and weed classification, achieving higher accuracy and lower crop killing rate (rate of identifying a crop as a weed). The experimental results show that our method outperforms state-of-the-art applications, for example, ResNet and VGG19.
