发布开源数字工具 octoPus,促进虫害综合防治

Simone Ugo Maria Bregaglio, Eugenio Rossi, Lorenzo Ascari, Gabriele Mongiano, Eleonora Del Cavallo, Sofia Bajocco, Luisa Maria Manici, Antonio Gerardo Pepe, Chiara Bassi, Rocchina Tiso, Fabio Pietrangeli, Giovanna Cattaneo, Camilla Nigro, Marco Secondo Gerardi, Simone Bussotti, Angela Sanchioni, Danilo Tognetti, Mariangela Sandra, Irene De Lillo, Paolo Framarin, Sandra Di Ferdinando, Riccardo Bugiani
{"title":"发布开源数字工具 octoPus,促进虫害综合防治","authors":"Simone Ugo Maria Bregaglio, Eugenio Rossi, Lorenzo Ascari, Gabriele Mongiano, Eleonora Del Cavallo, Sofia Bajocco, Luisa Maria Manici, Antonio Gerardo Pepe, Chiara Bassi, Rocchina Tiso, Fabio Pietrangeli, Giovanna Cattaneo, Camilla Nigro, Marco Secondo Gerardi, Simone Bussotti, Angela Sanchioni, Danilo Tognetti, Mariangela Sandra, Irene De Lillo, Paolo Framarin, Sandra Di Ferdinando, Riccardo Bugiani","doi":"10.1101/2024.08.07.606987","DOIUrl":null,"url":null,"abstract":"Meeting the EU targets to halve chemical pesticide use by 2030 necessitates European farmers to adopt Integrated Pest Management principles as the standard. Decision support systems are valuable tools to meet this target and rely on individual disease models to identify conducive conditions to fungal infections. These models are often proprietary assets of digital startups and agrochemical companies, leading to a lack of transparency for farmers and a bias towards chemical solutions over sustainable practices. We present octoPus, the first free digital tool designed to support the control of primary infections of grapevine downy mildew, and we evaluate its performance and behavior on a wide set of environmental conditions and agricultural contexts. We implemented eight models from scientific articles (Rule310, Laore, EPI, IPI, DMcast, UCSC, Misfits, Magarey, the tentacles), and evaluated them across Italian grapevine areas from 2001 to 2020. Model outputs were integrated with phenology and susceptibility models (the eyes), which were calibrated using data from regional extension services bulletins. The simulated infections serve as predictors in a Random Forest algorithm (brain) that elaborates an overall risk level (very low to very high). The Llama large language model is used to generate user-supportive messages (the mouth). octoPus is released as an open-source software, which reads weather data, executes the models, and presents outputs in natural language and symbolic syntax. Our results showed reasonable accuracy in simulating grapevine phenology (RMSE = 9-10 days) and seasonal risk (RMSE ≈ 0.75). The infection models consistently identified a moisture and thermal north-south suitability gradient in Italy and accurately detected years with low or high downy mildew pressure. However, the models displayed significant differences in the number and dynamics of simulated infections, with two distinct patterns within the ensemble. Meeting the EU targets to halve chemical pesticide use by 2030 necessitates European farmers to adopt Integrated Pest Management principles as the standard. Decision support systems are valuable tools to meet this target and rely on individual disease models to identify conducive conditions to fungal infections. These models are often proprietary assets of digital startups and agrochemical companies, leading to a lack of transparency for farmers and a bias towards chemical solutions over sustainable practices. We present octoPus, the first free digital tool designed to support the control of primary infections of grapevine downy mildew, and we evaluate its performance and behavior on a wide set of environmental conditions and agricultural contexts. We implemented eight models from scientific articles (Rule310, Laore, EPI, IPI, DMcast, UCSC, Misfits, Magarey, the \"tentacles\"), and evaluated them across Italian grapevine areas from 2001 to 2020. Model outputs were integrated with phenology and susceptibility models (the \"eyes\"), which were calibrated using data from regional extension services' bulletins. The simulated infections serve as predictors in a Random Forest algorithm (\"brain\") that elaborates an overall risk level (very low to very high). The Llama large language model is used to generate user-supportive messages (the \"mouth\"). octoPus is released as an open-source software, which reads weather data, executes the models, and presents outputs in natural language and symbolic syntax. Our results showed reasonable accuracy in simulating grapevine phenology (RMSE = 9-10 days) and seasonal risk (RMSE ≈ 0.75). The infection models consistently identified a moisture and thermal north-south suitability gradient in Italy and accurately detected years with low or high downy mildew pressure. However, the models displayed significant differences in the number and dynamics of simulated infections, with two distinct patterns within the ensemble. By developing and releasing the first free and open-source tool to support the control of grapevine downy mildew, we address a critical gap in the availability and transparency of decision support systems for European farmers. Unlike proprietary models that often lack transparency and may favor agribusiness' logic, octoPus provides a comprehensive and accessible alternative that promotes Integrated Pest Management practices. We propose the adoption of octoPus by plant health authorities to identify areas for performance refinement and capabilities expansion.","PeriodicalId":501320,"journal":{"name":"bioRxiv - Ecology","volume":"84 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Releasing the octoPus, an open-source digital tool to promote Integrated Pest Management\",\"authors\":\"Simone Ugo Maria Bregaglio, Eugenio Rossi, Lorenzo Ascari, Gabriele Mongiano, Eleonora Del Cavallo, Sofia Bajocco, Luisa Maria Manici, Antonio Gerardo Pepe, Chiara Bassi, Rocchina Tiso, Fabio Pietrangeli, Giovanna Cattaneo, Camilla Nigro, Marco Secondo Gerardi, Simone Bussotti, Angela Sanchioni, Danilo Tognetti, Mariangela Sandra, Irene De Lillo, Paolo Framarin, Sandra Di Ferdinando, Riccardo Bugiani\",\"doi\":\"10.1101/2024.08.07.606987\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Meeting the EU targets to halve chemical pesticide use by 2030 necessitates European farmers to adopt Integrated Pest Management principles as the standard. Decision support systems are valuable tools to meet this target and rely on individual disease models to identify conducive conditions to fungal infections. These models are often proprietary assets of digital startups and agrochemical companies, leading to a lack of transparency for farmers and a bias towards chemical solutions over sustainable practices. We present octoPus, the first free digital tool designed to support the control of primary infections of grapevine downy mildew, and we evaluate its performance and behavior on a wide set of environmental conditions and agricultural contexts. We implemented eight models from scientific articles (Rule310, Laore, EPI, IPI, DMcast, UCSC, Misfits, Magarey, the tentacles), and evaluated them across Italian grapevine areas from 2001 to 2020. Model outputs were integrated with phenology and susceptibility models (the eyes), which were calibrated using data from regional extension services bulletins. The simulated infections serve as predictors in a Random Forest algorithm (brain) that elaborates an overall risk level (very low to very high). The Llama large language model is used to generate user-supportive messages (the mouth). octoPus is released as an open-source software, which reads weather data, executes the models, and presents outputs in natural language and symbolic syntax. Our results showed reasonable accuracy in simulating grapevine phenology (RMSE = 9-10 days) and seasonal risk (RMSE ≈ 0.75). The infection models consistently identified a moisture and thermal north-south suitability gradient in Italy and accurately detected years with low or high downy mildew pressure. However, the models displayed significant differences in the number and dynamics of simulated infections, with two distinct patterns within the ensemble. Meeting the EU targets to halve chemical pesticide use by 2030 necessitates European farmers to adopt Integrated Pest Management principles as the standard. Decision support systems are valuable tools to meet this target and rely on individual disease models to identify conducive conditions to fungal infections. These models are often proprietary assets of digital startups and agrochemical companies, leading to a lack of transparency for farmers and a bias towards chemical solutions over sustainable practices. We present octoPus, the first free digital tool designed to support the control of primary infections of grapevine downy mildew, and we evaluate its performance and behavior on a wide set of environmental conditions and agricultural contexts. We implemented eight models from scientific articles (Rule310, Laore, EPI, IPI, DMcast, UCSC, Misfits, Magarey, the \\\"tentacles\\\"), and evaluated them across Italian grapevine areas from 2001 to 2020. Model outputs were integrated with phenology and susceptibility models (the \\\"eyes\\\"), which were calibrated using data from regional extension services' bulletins. The simulated infections serve as predictors in a Random Forest algorithm (\\\"brain\\\") that elaborates an overall risk level (very low to very high). The Llama large language model is used to generate user-supportive messages (the \\\"mouth\\\"). octoPus is released as an open-source software, which reads weather data, executes the models, and presents outputs in natural language and symbolic syntax. Our results showed reasonable accuracy in simulating grapevine phenology (RMSE = 9-10 days) and seasonal risk (RMSE ≈ 0.75). The infection models consistently identified a moisture and thermal north-south suitability gradient in Italy and accurately detected years with low or high downy mildew pressure. However, the models displayed significant differences in the number and dynamics of simulated infections, with two distinct patterns within the ensemble. By developing and releasing the first free and open-source tool to support the control of grapevine downy mildew, we address a critical gap in the availability and transparency of decision support systems for European farmers. Unlike proprietary models that often lack transparency and may favor agribusiness' logic, octoPus provides a comprehensive and accessible alternative that promotes Integrated Pest Management practices. We propose the adoption of octoPus by plant health authorities to identify areas for performance refinement and capabilities expansion.\",\"PeriodicalId\":501320,\"journal\":{\"name\":\"bioRxiv - Ecology\",\"volume\":\"84 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"bioRxiv - Ecology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2024.08.07.606987\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"bioRxiv - Ecology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.08.07.606987","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

要实现欧盟到 2030 年将化学农药使用量减半的目标,欧洲农民就必须采用病虫害综合防治原则作为标准。决策支持系统是实现这一目标的重要工具,它依靠单个疾病模型来确定真菌感染的有利条件。这些模型通常是数字初创公司和农用化学品公司的专有资产,导致农民缺乏透明度,并偏向于化学解决方案而非可持续实践。我们介绍了第一款免费数字工具 octoPus,该工具旨在支持葡萄霜霉病初级感染的控制,我们还评估了它在各种环境条件和农业背景下的性能和行为。我们采用了八种科学文章中的模型(Rule310、Laore、EPI、IPI、DMcast、UCSC、Misfits、Magarey、触角),并从 2001 年到 2020 年在意大利葡萄种植区进行了评估。模型输出与物候学和易感性模型(眼睛)进行了整合,后者利用地区推广服务公告中的数据进行了校准。模拟感染作为随机森林算法(大脑)中的预测因子,可得出总体风险水平(从极低到极高)。octoPus 是一款开源软件,可读取天气数据、执行模型并以自然语言和符号语法显示输出结果。我们的结果表明,模拟葡萄物候(均方根误差 = 9-10 天)和季节性风险(均方根误差≈ 0.75)的准确性相当高。感染模型一致确定了意大利的湿度和热度南北适宜梯度,并准确检测出霜霉病压力较低或较高的年份。然而,这些模型在模拟感染的数量和动态方面存在显著差异,在集合中呈现出两种截然不同的模式。要实现欧盟到 2030 年将化学农药使用量减半的目标,欧洲农民必须采用病虫害综合防治原则作为标准。决策支持系统是实现这一目标的重要工具,它依靠单个疾病模型来确定真菌感染的有利条件。这些模型通常是数字初创公司和农用化学品公司的专有资产,导致农民缺乏透明度,并偏向化学解决方案而非可持续实践。我们介绍了第一款免费数字工具 octoPus,该工具旨在支持葡萄霜霉病初级感染的控制,我们还评估了它在各种环境条件和农业背景下的性能和行为。我们采用了八种科学文章中的模型(Rule310、Laore、EPI、IPI、DMcast、UCSC、Misfits、Magarey、"触角"),并从 2001 年到 2020 年在意大利葡萄种植区进行了评估。模型输出与物候学和易感性模型("眼睛")进行了整合,并利用地区推广服务公报中的数据对其进行了校准。模拟感染情况可作为随机森林算法("大脑")的预测因子,从而得出总体风险水平(从极低到极高)。octoPus 是一款开源软件,可读取天气数据、执行模型并以自然语言和符号语法显示输出结果。我们的结果表明,模拟葡萄物候(RMSE = 9-10 天)和季节性风险(RMSE ≈ 0.75)的准确性相当高。感染模型一致确定了意大利的湿度和热度南北适宜梯度,并准确检测出霜霉病压力较低或较高的年份。然而,这些模型在模拟感染的数量和动态方面存在显著差异,在集合中呈现出两种截然不同的模式。通过开发和发布首个支持葡萄霜霉病防治的免费开源工具,我们填补了欧洲农民在决策支持系统的可用性和透明度方面的一个重要空白。专有模型往往缺乏透明度,而且可能偏向于农业综合企业的逻辑,与之不同的是,octoPus 提供了一个全面、易用的替代方案,可促进病虫害综合防治实践。我们建议植物卫生当局采用 octoPus,以确定性能改进和能力扩展的领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Releasing the octoPus, an open-source digital tool to promote Integrated Pest Management
Meeting the EU targets to halve chemical pesticide use by 2030 necessitates European farmers to adopt Integrated Pest Management principles as the standard. Decision support systems are valuable tools to meet this target and rely on individual disease models to identify conducive conditions to fungal infections. These models are often proprietary assets of digital startups and agrochemical companies, leading to a lack of transparency for farmers and a bias towards chemical solutions over sustainable practices. We present octoPus, the first free digital tool designed to support the control of primary infections of grapevine downy mildew, and we evaluate its performance and behavior on a wide set of environmental conditions and agricultural contexts. We implemented eight models from scientific articles (Rule310, Laore, EPI, IPI, DMcast, UCSC, Misfits, Magarey, the tentacles), and evaluated them across Italian grapevine areas from 2001 to 2020. Model outputs were integrated with phenology and susceptibility models (the eyes), which were calibrated using data from regional extension services bulletins. The simulated infections serve as predictors in a Random Forest algorithm (brain) that elaborates an overall risk level (very low to very high). The Llama large language model is used to generate user-supportive messages (the mouth). octoPus is released as an open-source software, which reads weather data, executes the models, and presents outputs in natural language and symbolic syntax. Our results showed reasonable accuracy in simulating grapevine phenology (RMSE = 9-10 days) and seasonal risk (RMSE ≈ 0.75). The infection models consistently identified a moisture and thermal north-south suitability gradient in Italy and accurately detected years with low or high downy mildew pressure. However, the models displayed significant differences in the number and dynamics of simulated infections, with two distinct patterns within the ensemble. Meeting the EU targets to halve chemical pesticide use by 2030 necessitates European farmers to adopt Integrated Pest Management principles as the standard. Decision support systems are valuable tools to meet this target and rely on individual disease models to identify conducive conditions to fungal infections. These models are often proprietary assets of digital startups and agrochemical companies, leading to a lack of transparency for farmers and a bias towards chemical solutions over sustainable practices. We present octoPus, the first free digital tool designed to support the control of primary infections of grapevine downy mildew, and we evaluate its performance and behavior on a wide set of environmental conditions and agricultural contexts. We implemented eight models from scientific articles (Rule310, Laore, EPI, IPI, DMcast, UCSC, Misfits, Magarey, the "tentacles"), and evaluated them across Italian grapevine areas from 2001 to 2020. Model outputs were integrated with phenology and susceptibility models (the "eyes"), which were calibrated using data from regional extension services' bulletins. The simulated infections serve as predictors in a Random Forest algorithm ("brain") that elaborates an overall risk level (very low to very high). The Llama large language model is used to generate user-supportive messages (the "mouth"). octoPus is released as an open-source software, which reads weather data, executes the models, and presents outputs in natural language and symbolic syntax. Our results showed reasonable accuracy in simulating grapevine phenology (RMSE = 9-10 days) and seasonal risk (RMSE ≈ 0.75). The infection models consistently identified a moisture and thermal north-south suitability gradient in Italy and accurately detected years with low or high downy mildew pressure. However, the models displayed significant differences in the number and dynamics of simulated infections, with two distinct patterns within the ensemble. By developing and releasing the first free and open-source tool to support the control of grapevine downy mildew, we address a critical gap in the availability and transparency of decision support systems for European farmers. Unlike proprietary models that often lack transparency and may favor agribusiness' logic, octoPus provides a comprehensive and accessible alternative that promotes Integrated Pest Management practices. We propose the adoption of octoPus by plant health authorities to identify areas for performance refinement and capabilities expansion.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信