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Classification of field wheat varieties based on a lightweight G-PPW-VGG11 model.
Pan, Yu; Yu, Xun; Dong, Jihua; Zhao, Yonghang; Li, Shuanming; Jin, Xiuliang.
Afiliación
  • Pan Y; College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
  • Yu X; Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
  • Dong J; Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi, China.
  • Zhao Y; Engineering Research Center for Production Mechanization of Oasis Characteristic Cash Crop, Ministry of Education, Shihezi, China.
  • Li S; Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
  • Jin X; State Key Laboratory of Crop Gene Resources and Breeding, Chinese Academy of Agricultural Sciences, Beijing, China.
Front Plant Sci ; 15: 1375245, 2024.
Article en En | MEDLINE | ID: mdl-38831908
ABSTRACT

Introduction:

In agriculture, especially wheat cultivation, farmers often use multi-variety planting strategies to reduce monoculture-related harvest risks. However, the subtle morphological differences among wheat varieties make accurate discrimination technically challenging. Traditional variety classification methods, reliant on expert knowledge, are inefficient for modern intelligent agricultural management. Numerous existing classification models are computationally complex, memory-intensive, and difficult to deploy on mobile devices effectively. This study introduces G-PPW-VGG11, an innovative lightweight convolutional neural network model, to address these issues.

Methods:

G-PPW-VGG11 ingeniously combines partial convolution (PConv) and partially mixed depthwise separable convolution (PMConv), reducing computational complexity and feature redundancy. Simultaneously, incorporating ECANet, an efficient channel attention mechanism, enables precise leaf information capture and effective background noise suppression. Additionally, G-PPW-VGG11 replaces traditional VGG11's fully connected layers with two pointwise convolutional layers and a global average pooling layer, significantly reducing memory footprint and enhancing nonlinear expressiveness and training efficiency.

Results:

Rigorous testing showed G-PPW-VGG11's superior performance, with an impressive 93.52% classification accuracy and only 1.79MB memory usage. Compared to VGG11, G-PPW-VGG11 showed a 5.89% increase in accuracy, 35.44% faster inference, and a 99.64% reduction in memory usage. G-PPW-VGG11 also surpasses traditional lightweight networks in classification accuracy and inference speed. Notably, G-PPW-VGG11 was successfully deployed on Android and its performance evaluated in real-world settings. The results showed an 84.67% classification accuracy with an average time of 291.04ms per image.

Discussion:

This validates the model's feasibility for practical agricultural wheat variety classification, establishing a foundation for intelligent management. For future research, the trained model and complete dataset are made publicly available.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Plant Sci Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Plant Sci Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza