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

doi:10.1016/j.jia.2024.07.015

Journal of Integrative Agriculture

2025, Vol. 24

Issue (4): 1403-1423 DOI: 10.1016/j.jia.2024.07.015

Crop Science

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

Tao Liu^{1, 2*}, Jianliang Wang^{1, 2*}, Jiayi Wang^{1, 2}, Yuanyuan Zhao^{1, 2}, Hui Wang⁴, Weijun Zhang^{1, 2}, Zhaosheng Yao^{1, 2}, Shengping Liu³, Xiaochun Zhong^3#, Chengming Sun^{1, 2#}

¹Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology/Agricultural College, Yangzhou University, Yangzhou 225009, China

²Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China

³Agricultural Information Institute, Chinese Academy of Agricultural Sciences/Key Laboratory of Agro-information Services Technology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China

⁴Lixiahe Institute of Agricultural Sciences, Yangzhou 225012, China

Highlights
● Developed a novel biomass estimation model, AUR-50, with an average R² exceeding 0.77.
● Enhanced model accuracy by integrating convolutional features into traditional image features.
● Reduced the impact of vegetation saturation on estimation accuracy using convolutional features.

Abstract
References

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摘要

地上生物量（AGB）是反映小麦群体生命活动的重要指标，对小麦生长监测和产量预测具有重要意义。传统的生物量统计方法主要通过人工取样调查来完成。尽管这些方法具有很高的估算精度，但该方法需要破坏性取样，操作耗时长，且难以大规模监测。本研究在传统遥感估测生物量的基础上进行方法优化，基于改进的卷积特征（CFs）来估算小麦AGB。研究通过低成本的无人机（UAV）作为主要数据采集设备，获取了两种小麦品种在五个关键生长期的RGB和多光谱（MS）影像数据。同时进行了田间测量，以获得实际的小麦生物量数据用于验证。基于遥感指数（RSIs）、结构特征（SFs）和卷积特征（CFs），本研究提出了一种新的特征AUR-50来估算小麦AGB。结果表明，AUR-50比RSIs和SFs更能准确地估算小麦AGB，平均R²超过0.77。在越冬期，AUR-50_MS具有最高的估算精度（R²为0.88）。此外，通过增加CFs，本文提出的方法降低了由于生育后期光谱饱和对生物量估算精度的影响，在开花期的最高R²为0.69。本研究结果为高通量估测小麦AGB提供了一种有效方法，并为其他作物的表型参数研究提供了参考。

Abstract

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 AGB based on improved convolutional features (CFs). Low-cost unmanned aerial vehicles (UAV) were used as the main data acquisition equipment. This study acquired 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 convolutional features (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 more accurately estimate the wheat AGB than RSIs and SFs, and the average R² exceeded 0.77. AUR-50_MShad the highest estimation accuracy (R² of 0.88) in the overwintering period. In addition, AUR-50 reduced the effect of the vegetation index saturation on the biomass estimation accuracy by adding CFs, where the highest R² 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.

Keywords: wheat above-ground biomass UAV entire growth stage convolutional feature

Received: 12 April 2024 Accepted: 30 May 2024

Fund:

This research was 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 Public-interest 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), and the Special Fund for Independent Innovation of Agriculture Science and Technology in Jiangsu Province, China (CX(22)3112).

About author: #Correspondence Xiaochun Zhong, E-mail: zhongxiaochun@caas.cn; Chengming Sun, E-mail: cmsun@yzu.edu.cn * These authors contributed equally to this study.

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