Detecting Unhealthy Wheat Plants Using Transfer Learning Method

Plenty of studies use image processing techniques to detect unhealthy wheat plants, but each disease of wheat have different symptoms. Thus, it is necessary to use different algorithms to have an accurate results, which is so consuming in energy and time and need a very powerful machine to do so. In this paper, we apply an intelligent system which uses deep learning methods that proved their accuracy in image classification. We use 300 digital images of unhealthy and healthy wheat plants, from different platforms of images on the net and TensorFlow as an intelligent system based on deep learning and transfer learning. Experimental results indicate that: significant classification performance (with an average accuracy of 70.66%) was achieved by the proposed method.

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