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Automatic detection of tomato diseases and pests based on leaf images

机译:基于叶片图像的番茄病虫害自动检测

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There are many species of tomato diseases and pests, and the pathology of which is complex. It is difficult and error-prone to simply rely on manual identification. For the ten most common tomato diseases and pests in China, This paper explores the detection algorithms on leaf images and constructs the convolution neural network model to detect tomato pests and diseases based on VGG16[8] and transfer learning. The detection model is trained with Keras/TensorFlow deep learning framework and achieves an average classification accuracy of 89%.
机译:番茄病虫害的种类很多,而且其病理也很复杂。仅依靠手动识别既困难又容易出错。针对中国十种最常见的番茄病虫害,本文探索了叶片图像的检测算法,并构建了基于VGG16 [8]和转移学习的卷积神经网络模型来检测番茄病虫害。该检测模型采用Keras / TensorFlow深度学习框架进行训练,平均分类精度达到89%。

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