Improving soybean yield prediction by integrating UAV nadir and cross-circling oblique imaging
Authors:
- Guangyao SunGSGuangyao Sun
- Yong ZhangYZYong Zhang
- Haochong ChenHCHaochong Chen
- Lei WangLWLei Wang
- Mingxue LiMLMingxue Li
- Xuhong SunXSXuhong Sun
- Shuaipeng FeiSFShuaipeng Fei
- Shunfu XiaoSXShunfu Xiao
- Long YanLYLong Yan
- Yinghui LiYLYinghui Li
- Yun XuYXYun Xu
- Lijuan QiuLQLijuan Qiu
- 4 March 2024
- journal article
- research article
- Published by Elsevier in European Journal of Agronomy
- Vol. 155
Abstract
No abstract availableKeywords
Funding Information
- China Agricultural University
- Inner Mongolia Autonomous Region Department of Science and Technology
This publication has 82 references indexed in Scilit:
- Artificial intelligence and satellite‐based remote sensing can be used to predict soybean (Glycine max) yieldAgronomy Journal, 2023
- Soybean genetic resources contributing to sustainable protein productionTheoretical and Applied Genetics, 2022
- UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheatPrecision Agriculture, 2022
- The effect of growth stage and plant counting accuracy of maize inbred lines on LAI and biomass predictionPrecision Agriculture, 2022
- Machine learning to differentiate small round cell malignant tumors and non-small round cell malignant tumors of the nasal and paranasal sinuses using apparent diffusion coefficient valuesEuropean Radiology, 2022
- Automated Crop and Weed Monitoring in Widely Spaced CerealsPrecision Agriculture, 2006
- Optimization of soil-adjusted vegetation indicesRemote Sensing of Environment, 1996
- Estimating PAR absorbed by vegetation from bidirectional reflectance measurementsRemote Sensing of Environment, 1995
- A modified soil adjusted vegetation indexRemote Sensing of Environment, 1994
- Red and photographic infrared linear combinations for monitoring vegetationRemote Sensing of Environment, 1979