Research on identification algorithm of crop pests and diseases based on improved DenseNet model

Jiabo Chen; Chen Dong; Ruoxuan Kong; Yi Wei; Hongyu Yao; Yutian Zhao

doi:10.1117/12.2681193

8 June 2023 Research on identification algorithm of crop pests and diseases based on improved DenseNet model

Jiabo Chen, Chen Dong, Ruoxuan Kong, Yi Wei, Hongyu Yao, Yutian Zhao

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127073T (2023) https://doi.org/10.1117/12.2681193
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Aiming at the problem of crop pests and diseases in agricultural production, this paper implements an identification algorithm of crop pests and diseases based on improved DenseNet model to achieve real-time detection of crop pests and diseases and early warning. Based on the DenseNet neural network, the crop dataset is first subjected to image processing and augmentation such as Canny edge detection, flipping, convolution and blurring, and the resulting dataset is used to train the DenseNet model. Moreover, the innovative addition of a pooling layer and a fully-connected layer to the DenseNet allows the model to obtain accurate identification results of crop health conditions with corresponding probabilities. The algorithm was tested on Plant Pathology 2020 - FGVC7, and the experimental results show that it is faster than traditional recognition algorithms, with a correct recognition rate of 96.7%, which can quickly and accurately diagnose crop pests and diseases and effectively improve crop yield and quality.

Citation Download Citation

Jiabo Chen, Chen Dong, Ruoxuan Kong, Yi Wei, Hongyu Yao, and Yutian Zhao "Research on identification algorithm of crop pests and diseases based on improved DenseNet model", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127073T (8 June 2023); https://doi.org/10.1117/12.2681193

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KEYWORDS

Data modeling

Diseases and disorders

Education and training

Image processing

Edge detection

Detection and tracking algorithms

Agriculture

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