Plant disease and insect pest identification based on vision transformer

Hang Li; Sufang Li; Jiguo Yu; Yubing Han; Anming Dong

doi:10.1117/12.2628467

22 April 2022 Plant disease and insect pest identification based on vision transformer

Hang Li, Sufang Li, Jiguo Yu, Yubing Han, Anming Dong

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 121740V (2022) https://doi.org/10.1117/12.2628467
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

With the rapid development of precision agriculture and smart agriculture, the need to build an automatic identification and detection system for diseases and insect pests is increasing. Using computers to correctly label plant diseases and insect pests is an important prerequisite for achieving accurate classification of plant diseases and insect pests and ensuring system performance. In order to improve the accuracy of computer classification of plant pests and diseases, this paper proposes an automatic pest identification method based on the Vision Transformer (ViT). In order to avoid training overfitting, the plant diseases and insect pests data sets are enhanced by methods such as Histogram Equalization, Laplacian, Gamma Transformation, CLAHE, Retinex-SSR, and Retinex-MSR. Then use the enhanced data set to train the constructed ViT neural network, so as to realize the automatic classification of plant diseases and insect pests. The simulation results show that the constructed ViT network has a test recognition accuracy rate of 96.71% on the plant disease and insect pest public data set Plant_Village, which is about 1.00% higher than the Plant disease and pest identification method based on traditional convolutional neural networks such as GoogleNet and EfficentNetV2.

Citation Download Citation

Hang Li, Sufang Li, Jiguo Yu, Yubing Han, and Anming Dong "Plant disease and insect pest identification based on vision transformer", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 121740V (22 April 2022); https://doi.org/10.1117/12.2628467

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KEYWORDS

Data conversion

Transformers

Data modeling

Neural networks

Agriculture

Computer programming

Internet

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