In order to better promote the construction of ecological irrigation areas, it is required to detect and control weeds in the areas. Thus, in this paper a method was proposed based on convolutional neural network(CNN)for weed classification and density estimation in ecological irrigation areas.The images were taken by a drone at low-altitude for three kinds of weeds namely chenopodium album, humulusscandens and xanthium sibiricum as well as 3 sorts of crops such as wheat, peanut seeding and maize, and then 17 115 training samples and 750 test samples were harvested through trimming, gray scale and rotation. Finally, the training sets were input into the CNN, and the classification of 6 types of plants was conducted by means of softmax regression.In order to reduce the network parameters, the effect of 100×100 and 300×300 resolution images on recognition accuracy was also clarified.The results showed that the highest recognition rate of 300×300 resolution could reach as high as 95.6%accuracy.In order to prevent and control specific weeds, a method of detecting single weeds density was presented, too.Through this method, accurate monitoring of various weeds in irrigation areas can be achieved. This method can provide a basis for precise applying pesticide, and has important significance and theoretical and practical values for realizing efficient, green, and safe modern agriculture.
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