基于改进ResNet18的植物生长阶段识别方法及智慧植物补光的实现  

Identification Method of Plant Growth Stages Based on Improved ResNet18 and Realization of Intelligent Plant Lighting Supplement

作　　者：王安 陈湛旭 孔景徐 吴思源 何绍威 张嘉玲 万巍 Wang An;Chen Zhanxu;Kong Jingxu;Wu Siyuan;He Shaowei;Zhang Jialing;Wan Wei(College of Optoelectronic Engineering,Guangdong Polytechnic Normal University,Guangzhou 510450,Guangdong,China)

机构地区：[1]广东技术师范大学光电工程学院,广东广州510450

出　　处：《作物杂志》2025年第1期250-259,共10页Crops

基　　金：广东省普通高校重点领域项目(人工智能)项目(2019KZDZX1042);2022年国家级创新训练项目(202210588009)。

摘　　要：为了能准确地识别植物的生长阶段从而实现智慧植物补光,设计一套能识别植物种类和生长阶段的智慧植物补光系统,其中识别植物生长阶段以ResNet18模型进行改进,用深度可分离卷积代替传统卷积,并引入SE模块(squeeze and excitation module)来提高模型任务处理的效率和准确性,结合早停法和学习率衰减机制来训练,避免过拟合。以番茄为研究对象进行验证,识别其幼苗期、开花坐果期和果实成熟期。结果表明,改进ResNet18模型的识别准确率达到了96.57%,比原模型提高了4.93个百分点,单张识别时间为0.27 s,比原模型快了0.30 s,模型体积为原模型的14%,同时,改进后的模型在测试集准确率、参数量和Macro F1得分等方面都优于ResNet18、ResNet34、AlexNet和VGG16四种卷积神经网络。最后,将改进ResNet18模型应用于植物补光系统,实际识别番茄生长阶段的准确率达到了96.49%,并能输出预期的光谱。该系统能精准地识别植物种类及其生长阶段,从而调用匹配植物及其生长阶段的光配方,达到智慧补光的目的。In order to accurately identify the growth stage of plants and achieve intelligent plant light supplement,a set of intelligent plant supplementary light system that can identify plant species and growth stages was designed.The ResNet18 model was used to improve the identification of plant growth stage,and the deep separable convolution was used to replace traditional convolution.The SE module(squeeze and excitation module)was introduced to improve the efficiency and accuracy of model task processing,and early stop method and learning rate attenuation mechanism were combined for training to avoid overfitting.Tomato was used as the research object to verify and identify its seedling,flowering and fruiting,fruit ripening stages.The results showed that the recognition accuracy of the improved ResNet18 model reaches 96.57%,which was 4.93 percentage points higher than the original model.The single recognition time was 0.27 seconds,which was 0.30 seconds faster than the original model.The model volume was 14%of the original model.At the same time,the improved model was superior to the four convolutional neural networks of ResNet18,ResNet34,AlexNet and VGG16 in terms of test set accuracy,parameter quantity and Macro F1 score.Finally,the improved ResNet18 model was applied to the plant light supplement system,and the actual recognition accuracy of tomato growth stage reached 96.49%,and the expected spectrum was output.The system can accurately identify plant species and their growth stage,so as to invoke the light formula matching the plant and its growth stage,to achieve the purpose of intelligent plant light supplement.

关 键 词：智慧补光 卷积神经网络 改进ResNet18模型 植物生长阶段识别 

分 类 号：S126[农业科学—农业基础科学]