基于多光谱和RGB图像的小麦籽粒产量田间表型数据融合提高模型性能

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2025-01-07 DOI:10.1007/s11119-024-10211-3

Paul Heinemann, Lukas Prey, Anja Hanemann, Ludwig Ramgraber, Johannes Seidl-Schulz, Patrick Ole Noack

{"title":"基于多光谱和RGB图像的小麦籽粒产量田间表型数据融合提高模型性能","authors":"Paul Heinemann, Lukas Prey, Anja Hanemann, Ludwig Ramgraber, Johannes Seidl-Schulz, Patrick Ole Noack","doi":"10.1007/s11119-024-10211-3","DOIUrl":null,"url":null,"abstract":"Assessing the grain yield of wheat remains a great challenge in field breeding trials.\nMultispectral and RGB images acquired by UAVs offer a promising tool for in-season prediction yet with varying results during the growing season.\nTherefore, enhancing prediction accuracy through optimizing multi-date models seems necessary but needs to be weighted with time and costs.\nMulti-date models outperform single-date models, with repeated data collection during the grain-filling phase being most effective.\nRGB indices can compete with multispectral indices.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"42 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing model performance through date fusion in multispectral and RGB image-based field phenotyping of wheat grain yield\",\"authors\":\"Paul Heinemann, Lukas Prey, Anja Hanemann, Ludwig Ramgraber, Johannes Seidl-Schulz, Patrick Ole Noack\",\"doi\":\"10.1007/s11119-024-10211-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Assessing the grain yield of wheat remains a great challenge in field breeding trials.\\nMultispectral and RGB images acquired by UAVs offer a promising tool for in-season prediction yet with varying results during the growing season.\\nTherefore, enhancing prediction accuracy through optimizing multi-date models seems necessary but needs to be weighted with time and costs.\\nMulti-date models outperform single-date models, with repeated data collection during the grain-filling phase being most effective.\\nRGB indices can compete with multispectral indices.\",\"PeriodicalId\":20423,\"journal\":{\"name\":\"Precision Agriculture\",\"volume\":\"42 1\",\"pages\":\"\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2025-01-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Precision Agriculture\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1007/s11119-024-10211-3\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Agriculture","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11119-024-10211-3","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

摘要

在田间育种试验中，小麦产量评估仍然是一个巨大的挑战。无人机获取的多光谱和RGB图像为季节性预测提供了一种很有前途的工具，但在生长季节会产生不同的结果。因此，通过优化多日期模型来提高预测精度似乎是必要的，但需要对时间和成本进行加权。多日期模型优于单日期模型，在灌浆阶段重复收集数据是最有效的。RGB指数可以与多光谱指数竞争。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Enhancing model performance through date fusion in multispectral and RGB image-based field phenotyping of wheat grain yield

Assessing the grain yield of wheat remains a great challenge in field breeding trials.

Multispectral and RGB images acquired by UAVs offer a promising tool for in-season prediction yet with varying results during the growing season.

Therefore, enhancing prediction accuracy through optimizing multi-date models seems necessary but needs to be weighted with time and costs.

Multi-date models outperform single-date models, with repeated data collection during the grain-filling phase being most effective.

RGB indices can compete with multispectral indices.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.