基于深度学习的目标检测算法在农田有害生物识别中的应用

Shi Wenxiu, Li Nianqiang
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引用次数: 1

摘要

结合深度学习技术,提出了一种基于目标检测算法的农田有害生物识别方法,实现了农田有害生物的自动识别,提高了识别精度。首先,建立有标签的农场有害生物数据库;然后采用Faster R-CNN算法,模型采用改进的Inception网络进行测试;最后,在农场有害生物数据库上对所提出的目标检测模型进行了训练和测试,平均精度可达90.54%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Target Detection Algorithm based on Deep Learning in Farmland Pest Recognition
Combining with deep learning technology, this paper proposes a method of farmland pest recognition based on target detection algorithm, which realizes the automatic recognition of farmland pest and improves the recognition accuracy. First of all, a labeled farm pest database is established; then uses Faster R-CNN algorithm, the model uses the improved Inception network for testing; finally, the proposed target detection model is trained and tested on the farm pest database, with the average precision up to 90.54%.
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