利用CNN检测棉花植株病害

Javlon Tursunov, Gulrukh Memonova
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引用次数: 0

摘要

棉花生产在世界各地都被认为是至关重要的,提前确定病害是直接影响产量的重要因素。为了解决这个问题,提出了一种基于CNN的方法来检测病害植物和叶片。在检测方面,利用谷歌协作实验室对VGG19人工神经网络进行了训练。利用Kaggle棉株数据集进行无监督学习,对模型进行训练,并进行验证和测试。一旦训练完成,保存的模型可以很容易地预测植物或叶片是否患病。
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Detection of Cotton Plant Disease Using CNN
Cotton production is considered crucial in various parts of the world and determining the diseases well in advance is a vital factor that directly has an effect on the yield. To tackle this issue, a CNN - based approach has been proposed which can detect a diseased plant and the leaf. For detection, the VGG19 artificial neural network has been trained by using google collaboratory. Moreover, unsupervised learning was used with Kaggle cotton plant dataset for training the model followed by validation and testing. Once the training is done, the saved model can easily predict whether the plant or leaf is diseased or not.
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