从全球到地方的多农场草莓产量预测专家机器学习系统

Matthew Beddows, Georgios Leontidis
{"title":"从全球到地方的多农场草莓产量预测专家机器学习系统","authors":"Matthew Beddows, Georgios Leontidis","doi":"10.2139/ssrn.4747534","DOIUrl":null,"url":null,"abstract":"The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all the aspects of the supply chain from staffing to supplier demand, food waste, and other business decisions. However, the process is often inaccurate and far from perfect. This paper explores the potential of using expert forecasts to enhance the crop yield predictions of our global-to-local XGBoost machine learning system. Additionally, it investigates the ERA5 climate model’s viability as an alternative data source for crop yield forecasting in the absence of on-farm weather data. We find that, by combining both the expert’s pre-season forecasts and the ERA5 climate model with the machine learning model, we can—in most cases—obtain better forecasts that outperform the growers’ pre-season forecasts and the machine learning-only models. Our expert-informed model attains yield forecasts for 4 weeks ahead with an average RMSE of 0.0855 across all the plots and an RMSE of 0.0872 with the ERA5 climate data included.","PeriodicalId":507782,"journal":{"name":"SSRN Electronic Journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multi-farm global to local expert-informed machine learning system for strawberry yield forecasting\",\"authors\":\"Matthew Beddows, Georgios Leontidis\",\"doi\":\"10.2139/ssrn.4747534\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all the aspects of the supply chain from staffing to supplier demand, food waste, and other business decisions. However, the process is often inaccurate and far from perfect. This paper explores the potential of using expert forecasts to enhance the crop yield predictions of our global-to-local XGBoost machine learning system. Additionally, it investigates the ERA5 climate model’s viability as an alternative data source for crop yield forecasting in the absence of on-farm weather data. We find that, by combining both the expert’s pre-season forecasts and the ERA5 climate model with the machine learning model, we can—in most cases—obtain better forecasts that outperform the growers’ pre-season forecasts and the machine learning-only models. Our expert-informed model attains yield forecasts for 4 weeks ahead with an average RMSE of 0.0855 across all the plots and an RMSE of 0.0872 with the ERA5 climate data included.\",\"PeriodicalId\":507782,\"journal\":{\"name\":\"SSRN Electronic Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SSRN Electronic Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.4747534\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SSRN Electronic Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.4747534","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

预测作物产量在农业中的重要性怎么强调都不为过。产量预测的影响体现在供应链的方方面面,从人员配备到供应商需求、食物浪费以及其他商业决策。然而,这一过程往往不准确,也远非完美。本文探讨了使用专家预测来提高我们的全球到本地 XGBoost 机器学习系统的作物产量预测的潜力。此外,本文还研究了在缺乏农场气象数据的情况下,ERA5 气候模型作为作物产量预测替代数据源的可行性。我们发现,通过将专家的季前预测和ERA5 气候模型与机器学习模型相结合,我们可以在大多数情况下获得更好的预测结果,其预测结果优于种植者的季前预测和纯机器学习模型。由专家提供信息的模型可提前 4 周预测产量,所有地块的平均均方根误差为 0.0855,包含 ERA5 气候数据时的均方根误差为 0.0872。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A multi-farm global to local expert-informed machine learning system for strawberry yield forecasting
The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all the aspects of the supply chain from staffing to supplier demand, food waste, and other business decisions. However, the process is often inaccurate and far from perfect. This paper explores the potential of using expert forecasts to enhance the crop yield predictions of our global-to-local XGBoost machine learning system. Additionally, it investigates the ERA5 climate model’s viability as an alternative data source for crop yield forecasting in the absence of on-farm weather data. We find that, by combining both the expert’s pre-season forecasts and the ERA5 climate model with the machine learning model, we can—in most cases—obtain better forecasts that outperform the growers’ pre-season forecasts and the machine learning-only models. Our expert-informed model attains yield forecasts for 4 weeks ahead with an average RMSE of 0.0855 across all the plots and an RMSE of 0.0872 with the ERA5 climate data included.
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
自引率
0.00%
发文量
0
期刊最新文献
Multilingualism and International Mental Health Research – The Barriers for Non-native Speakers of English The Oxford Olympics Study 2024: Are Cost and Cost Overrun at the Games Coming Down? The Frontiers of Nullification and Anticommandeering: Federalism and Extrajudicial Constitutional Interpretation Wasserstein gradient flow for optimal probability measure decomposition Using Legitimacy Strategies to Secure Organisational Survival Over Time: The Case of EFRAG
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1