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Lightweight compressed depth neural network for tomato disease diagnosis
Author(s): Yang Wu; Lihong Xu
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Paper Abstract

Aiming at the shortcoming of deep neural network in crop disease diagnosis, a lightweight compressed depth neural network for tomato disease diagnosis is proposed. Multi-scale convolution is used to increase receptive field, extract more abundant features, reduce model parameters and realize lightweight of the network by adopting the strategies of group convolution, depth separable convolution, pointwise convolution, channel shuffle, etc. For the lightweight model that has been initially trained, pruning operation is used to cut filter weight that is not important to reduce redundancy of the model. Experiments show that the accuracy of tomato disease diagnosis using the lightweight model is 98.61% after training only 10 epochs, it meets the needs of tomato disease diagnosis in agricultural production due to the small calculation and fast detection speed, and when cutting about 50% filter weight, the accuracy has only dropped 0.70%, which has a good effect.

Paper Details

Date Published: 3 January 2020
PDF: 9 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731S (3 January 2020); doi: 10.1117/12.2557180
Show Author Affiliations
Yang Wu, Tongji Univ. (China)
Lihong Xu, Tongji Univ. (China)


Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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