cnnPest: Insect detection and classification based on improved convolutional neural network


Regarding the growth of crops, one of the important factors affecting crop yield is insect disaster. Since most insect species are extremely similar, insect detection on field crops, such as rice, soybean and other crops, is more challenging than generic object detection. Presently, distinguishing insects in crop fields mainly relies on manual classification, but it is extremely time-consuming and expensive. This work proposes a convolutional neural network model to solve the multi-classification of crop insects. The model can make full use of the advantages of the neural network to comprehensively extract multifaceted insect features. During the regional proposal stage, the Region Proposal Network is adopted rather than a traditional selective search technique to generate a smaller number of proposal windows, which is especially important for improving prediction accuracy and accelerating computations. Experimental results show that the proposed method achieves a heightened accuracy and is superior to the state-of-the-art traditional insect classification algorithms.


Sample images:

Sample images of 24 insect species collected from crop fields.


Supplementary files:

Source codes: supplementary.


Denan Xia, Peng Chen*, Bing Wang, Jun Zhang, Chengjun Xie, Insect detection and classification based on improved convolutional neural network. Sensors, 2018, 18: 4169.

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