Research on the estimation of wheat AGB at the entire growth stage based on improved convolutional features  

作　　者：Tao Liu Jianliang Wang Jiayi Wang Yuanyuan Zhao Hui Wang Weijun Zhang Zhaosheng Yao Shengping Liu Xiaochun Zhong Chengming Sun 

机构地区：[1]Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology/Agricultural College,Yangzhou University,Yangzhou 225009,China [2]Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops,Yangzhou University,Yangzhou 225009,China [3]Agricultural Information Institute,Chinese Academy of Agricultural Sciences/Key Laboratory of Agro-information Services Technology,Ministry of Agriculture and Rural Affairs,Beijing 100081,China [4]Lixiahe Institute of Agricultural Sciences,Yangzhou 225012,China

出　　处：《Journal of Integrative Agriculture》2025年第4期1403-1423,共21页农业科学学报(英文版)

基　　金：supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(SJCX23_1973);the National Natural Science Foundation of China(32172110,32071945);the Key Research and Development Program(Modern Agriculture)of Jiangsu Province,China(BE2022342-2,BE2020319);the Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center Open Project,China(ZHKF04);the National Key Research and Development Program of China(2023YFD2300201,2023YFD1202200);the Special Funds for Scientific and Technological Innovation of Jiangsu Province,China(BE2022425);the Priority Academic Program Development of Jiangsu Higher Education Institutions,China(PAPD);the Central Publicinterest Scientific Institution Basal Research Fund,China(JBYW-AII-2023-08);the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences(CAAS-CS-202201);the Special Fund for Independent Innovation of Agriculture Science and Technology in Jiangsu Province,China(CX(22)3112)。

摘　　要：The wheat above-ground biomass(AGB)is an important index that shows the life activity of vegetation,which is of great significance for wheat growth monitoring and yield prediction.Traditional biomass estimation methods specifically include sample surveys and harvesting statistics.Although these methods have high estimation accuracy,they are time-consuming,destructive,and difficult to implement to monitor the biomass at a large scale.The main objective of this study is to optimize the traditional remote sensing methods to estimate the wheat AGBbased on improved convolutional features(CFs).Low-cost unmanned aerial vehicles(UAV)were used as the main data acquisition equipment.This study acquired image data acquired by RGB camera(RGB)and multi-spectral(MS)image data of the wheat population canopy for two wheat varieties and five key growth stages.Then,field measurements were conducted to obtain the actual wheat biomass data for validation.Based on the remote sensing indices(RSIs),structural features(SFs),and CFs,this study proposed a new feature named AUR-50(multi-source combination based on convolutional feature optimization)to estimate the wheat AGB.The results show that AUR-50 could estimate the wheat AGB more accurately than RSIs and SFs,and the average R^(2) exceeded 0.77.In the overwintering period,AUR-50_(MS)(multi-source combination with convolutional feature optimization using multispectral imagery)had the highest estimation accuracy(R^(2) of 0.88).In addition,AUR-50 reduced the effect of the vegetation index saturation on the biomass estimation accuracy by adding CFs,where the highest R^(2) was 0.69 at the flowering stage.The results of this study provide an effective method to evaluate the AGB in wheat with high throughput and a research reference for the phenotypic parameters of other crops.

关 键 词：WHEAT above-ground biomass UAV entire growth stage convolutional feature 

分 类 号：S127[农业科学—农业基础科学]