南京农业大学学报2025，Vol.48Issue(5)：1001-1012,12.DOI:10.7685/jnau.202405006
基于无人机影像和卷积神经网络的水稻育种材料产量预测研究
Yield prediction in rice breeding materials using UAV-based images and convolutional neural network
摘要
Abstract
[Objectives]Obtaining yield information of rice breeding plots before harvest is an important part of high-throughput phenotyping,and it is also an urgent need for high-yield rice breeding.At present,most of the rice yield prediction models are based on a few varieties,and are modeled by linear regression,machine learning and other methods.Therefore,the yield estimation models generally have poor mobility and low accuracy.This study aimed to use UAV images and deep learning networks to construct a yield prediction model,which was suitable for multiple rice varieties.[Methods]The multi-temporal UAV-based RGB and multispectral images of rice breeding experiments were obtained,and the performance of linear regression,machine learning and deep learning algorithms in yield prediction was systematically compared.Furthermore,a new methodology of rice yield prediction was proposed using attention mechanism and convolutional neural network,and the performance of ResNet50,MobileNetV3 and ShuffleNetV2 were compared.[Results]The linear regression and machine learning algorithms performed poorly in predicting the yield of rice breeding plots(R2＜0.3).The convergence speed and prediction accuracy of the MobileNet model were the highest with R2,RMSE,and RRMSE of 0.55,1.06 t·hm-2,and 12.62％,respectively.The convergence speed and prediction accuracy of the MobileNet model with attention mechanism were improved to a certain extent with R2,RMSE and RRMSE of 0.58,1.03 t·hm-2 and 12.26％,respectively.The time series model constructed by the temporal convolutional network(TCN)had a certain improvement in the prediction accuracy of rice yield,with R2,RMSE and RRMSE reaching 0.64,0.96 t·hm-2 and 11.4％,respectively.[Conclusions]The convolutional neural network provided a reliable method for yield prediction in rice breeding experiments,and provided a good technical support for UAV-based high-throughput phenotyping of rice.关键词
无人机影像/卷积神经网络/产量预测/水稻育种材料Key words
UAV-based images/convolutional neural network/yield prediction/rice breeding materials分类
农业科技引用本文复制引用
吉文翰,曹卫星,程涛,郑恒彪,王迪,唐伟杰,张小虎,郭彩丽,姚霞,江冲亚,朱艳..基于无人机影像和卷积神经网络的水稻育种材料产量预测研究[J].南京农业大学学报,2025,48(5):1001-1012,12.基金项目
国家重点研发计划项目(2022YFD2001100) （2022YFD2001100）
国家自然科学基金项目(32101617) （32101617）
江苏省重点实验室专项(KLIAZZ2301) （KLIAZZ2301）
