摘要
为快速、准确地诊断水稻钾素的胁迫程度,以晚稻品种黄华占为材料进行大田试验,对基肥和拔节期追肥分别设置4个钾素施肥水平处理。以分蘖期和拔节期水稻主茎顶部3片展开叶的扫描图像作为数据集,以MobileViT为骨架,将Coordinate Attention引入到MobileViT的Layer中的每个3×3卷积的BN层之后,构建CA_MobileViT模型并引入迁移学习。通过对比EfficientNet-V2、ConvNeXt、MobileViT和CA_MobileViT模型进行验证,结果表明:4个模型的准确率分别为98.4%、98.5%、94.2%、95.3%。其中CA_MobileViT模型的准确率为95.3%,比MobileViT模型提高了1.1百分点,却比EfficientNet-V2模型准确率低3.1百分点,比ConvNeXt模型准确率低3.2百分点,但CA_MobileViT模型参数量约EfficientNet-V2和ConvNeXt大模型的1/4,训练时间缩短了约1/3。改进的CA_MobileViT模型对于水稻钾素胁迫程度诊断具有较高的准确率,能有效地指导水稻钾素科学追肥管理,也为其他农作物的营养快速、精确诊断提供了一种普适、可行的方法。
In order to rapidly and accuratelely diagnose and recognize potassium stress degree of rice,The Huanghuazhan variety of late rice was used as material,and 4 potassium fertilization levels were set respectively for base fertilizer and topdressing at jointing stage.Taking scanned images of 3 spread leaves of main stem at tiller stage and jointing stage as data set,and MobileViT as the skeleton,the CA_MobileViT model was constructed after introducing Coordinate Attention to the BN Layer of each 3×3 convolution in the layer of MobileViT and introduce transfer learning.The results were verified by comparing EfficientNet-V2,ConvNeXt,MobileViT and CA_MobileViT models.The results showed that the accuracy rates of the 4 models were 98.4%,98.5%,94.2%and 95.3%,respectively.The accuracy of CA_MobileViT model was 95.3%,1.1 percent point higher than that of MobileViT model,but 3.1 percent point lower than that of EfficientNet-V2 model and 3.2 percent point lower than that of ConvNeXt model.However,the number of parameters of CA_MobileViT models was about 1/4 of the EfficientNet-V2 and ConvNeXt large models,and the training time was reduced by about 1/3.The improved CA_MobileViT model has a high accuracy for the diagnosis of potassium stress degree of rice,and could effectively guide the scientific potassium topdressing management of rice,and also provide a universal and feasible method for the rapid and accurate diagnosis of nutrition of other crops.
作者
吴正
杨红云
孙爱珍
孔杰
黄淑梅
WU Zheng;YANG Hongyun;SUN Aizhen;KONG Jie;HUANG Shumei(School of Computer and Information Engineering,Jiangxi Agricultural University,Nanchang 330045,China;School of Software,Jiangxi Agricultural University,Nanchang 330045,China)
出处
《中国农业科技导报(中英文)》
北大核心
2025年第8期80-88,共9页
Journal of Agricultural Science and Technology
基金
国家自然科学基金项目(62162030,61562039)。
