山东农业科学2024，Vol.56Issue(12)：139-146,8.DOI:10.14083/j.issn.1001-4942.2024.12.019
基于MobileNetV2和卷积注意力机制的轻量化玉米籽粒品种识别研究
Lightweight Maize Seed Variety Recognition Model Based on MobileNetV2 and Convolution Attention Mechanism
摘要
Abstract
Rapid and accurate identification of crop variety is of great significance to food security and development of agriculture in China.In order to achieve fast identification and protection of maize seeds,this study proposed a maize seed variety recognition algorithm based on MobileNetV2 and convolution attention mechanism.Firstly,the seeds of nine popular maize varieties were bought from the market,and their images were captured using a Canon 80D camera to create a dataset of 3 408 maize seed images.The dataset was di-vided into training,validation and testing sets at a ratio of 7∶2∶1,and the images in training set were subjec-ted to data augmentation treatment.Then,an attention module called ISPAM(Improved Spatial Attention Module)was designed:based on the convolutional block attention module(CBAM),a new channel attention module ICAM was proposed to improve the channel attention mechanism of CBAM,and the spatial pyramid pooling(SPP)module was introduced to replace the average pooling module and the maximum pooling module in the CBAM spatial attention module.Finally,a maize seed variety recognition model called MobileNetV2_IS-PAM was constructed.Compared with the models incorporating with other attention modules,MobileNetV2_IS-PAM achieved an accuracy of 99.11%on the test set,which was obviously higher than that in MobileNetV2 and the models with SE(Squeeze-and-Excitation)and CBAM attention mechanisms.The visualization of the gradient-weighted class activation mapping network demonstrated that MobileNetV2_ISPAM paid more atten-tion to salient features of maize seed images,thus improved the accuracy.Moreover,the model had a parame-ter size of only 7.15 M,making it suitable for deployment on mobile devices.This study enhanced the ability of model to resist overfitting and improved the classification performance,meanwhile,ensured its lightweight and high efficiency,which provided insights for future researches on deep learning-based maize seed image recog-nition models for mobile platforms.关键词
MobileNetV2/ISPAM注意力机制/深度学习/玉米籽粒/品种识别Key words
MobileNetV2/ISPAM attention mechanism/Deep learning/Maize seed/Variety recognition分类
农业科技引用本文复制引用
孙孟研,孙彤辉,郝凤琦,穆春华,马德新..基于MobileNetV2和卷积注意力机制的轻量化玉米籽粒品种识别研究[J].山东农业科学,2024,56(12):139-146,8.基金项目
山东省自然科学基金项目(ZR2022MC152) （ZR2022MC152）
中央引导地方科技发展专项计划(23-1-3-6-zyyd-nsh) （23-1-3-6-zyyd-nsh）
山东省重点研发计划项目(2023TZXD023) （2023TZXD023）
