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Application of Target Detection Algorithm based on Deep Learning in Farmland Pest Recognition

机译:基于深度学习在农田害虫认可的目标检测算法的应用

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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%.
机译:本文结合了深度学习技术,提出了一种基于目标检测算法的农田识别方法,这实现了农田害虫的自动识别并提高了识别准确性。首先,建立了标记的农场害虫数据库;然后使用更快的R-CNN算法,该模型使用改进的初始网络进行测试;最后,拟议的目标检测模型在农场害虫数据库上培训并测试,平均精度高达90.54%。

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