利用电生理信号对温室营养缺乏症进行高级评估

IF 2.4 3区 农林科学 Q1 Agricultural and Biological Sciences Horticulture Environment and Biotechnology Pub Date : 2024-02-13 DOI:10.1007/s13580-023-00589-w
Daniel Tran, Elena Najdenovska, Fabien Dutoit, Carrol Plummer, Nigel Wallbridge, Marco Mazza, Cédric Camps, Laura Elena Raileanu
{"title":"利用电生理信号对温室营养缺乏症进行高级评估","authors":"Daniel Tran, Elena Najdenovska, Fabien Dutoit, Carrol Plummer, Nigel Wallbridge, Marco Mazza, Cédric Camps, Laura Elena Raileanu","doi":"10.1007/s13580-023-00589-w","DOIUrl":null,"url":null,"abstract":"<p>Nutrient deficiencies are one of the main causes of significant reductions in commercial crop production by affecting associated growth factors. Proper plant nutrition is crucial for crop quality and yield therefore, early and objective detection of nutrient deficiency is required. Recent literature has explored the real-time monitoring of plant electrical signal, called electrophysiology, applied on tomato crop cultivated in greenhouse. This sensor allows to identify the stressed state of a plant in the presence of different biotic and abiotic stressors by employing machine learning techniques. The aim of this study was to evaluate the potential of electrophysiology signal recordings acquired from tomato plants growing in a production greenhouse environment, to detect the stress of a plant triggered by the deficiency of several main nutrients. Based on a previously proposed workflow consisting of continuous acquisition of electrical signal then application of machine learning techniques, the minimum signal features was evaluated. This study presents classification models that are able to distinguish the plant’s stressed state with good accuracy, namely 78.5% for manganese, 78.1% for iron, 89.6% for nitrogen, and 78.1% for calcium deficiency, and therefore suggests a novel path to detect nutrient deficiencies at an early stage. This could constitute a novel practical tool to help and assist farmers in nutrition management.</p>","PeriodicalId":13123,"journal":{"name":"Horticulture Environment and Biotechnology","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advanced assessment of nutrient deficiencies in greenhouse with electrophysiological signals\",\"authors\":\"Daniel Tran, Elena Najdenovska, Fabien Dutoit, Carrol Plummer, Nigel Wallbridge, Marco Mazza, Cédric Camps, Laura Elena Raileanu\",\"doi\":\"10.1007/s13580-023-00589-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Nutrient deficiencies are one of the main causes of significant reductions in commercial crop production by affecting associated growth factors. Proper plant nutrition is crucial for crop quality and yield therefore, early and objective detection of nutrient deficiency is required. Recent literature has explored the real-time monitoring of plant electrical signal, called electrophysiology, applied on tomato crop cultivated in greenhouse. This sensor allows to identify the stressed state of a plant in the presence of different biotic and abiotic stressors by employing machine learning techniques. The aim of this study was to evaluate the potential of electrophysiology signal recordings acquired from tomato plants growing in a production greenhouse environment, to detect the stress of a plant triggered by the deficiency of several main nutrients. Based on a previously proposed workflow consisting of continuous acquisition of electrical signal then application of machine learning techniques, the minimum signal features was evaluated. This study presents classification models that are able to distinguish the plant’s stressed state with good accuracy, namely 78.5% for manganese, 78.1% for iron, 89.6% for nitrogen, and 78.1% for calcium deficiency, and therefore suggests a novel path to detect nutrient deficiencies at an early stage. This could constitute a novel practical tool to help and assist farmers in nutrition management.</p>\",\"PeriodicalId\":13123,\"journal\":{\"name\":\"Horticulture Environment and Biotechnology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-02-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Horticulture Environment and Biotechnology\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1007/s13580-023-00589-w\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Agricultural and Biological Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Horticulture Environment and Biotechnology","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s13580-023-00589-w","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
引用次数: 0

摘要

养分缺乏会影响相关的生长因子,是导致商业作物产量大幅下降的主要原因之一。适当的植物营养对作物的质量和产量至关重要,因此需要及早客观地检测养分缺乏情况。最近有文献探讨了对植物电信号的实时监测,这种方法被称为电生理学,应用于温室栽培的番茄作物上。这种传感器可通过机器学习技术识别植物在不同生物和非生物压力下的受压状态。本研究的目的是评估从生长在生产温室环境中的番茄植株获取的电生理学信号记录的潜力,以检测植株因缺乏几种主要养分而引发的压力。根据之前提出的工作流程(包括连续采集电信号,然后应用机器学习技术),对最小信号特征进行了评估。这项研究提出的分类模型能够准确区分植物的压力状态,即锰缺乏为 78.5%、铁缺乏为 78.1%、氮缺乏为 89.6%、钙缺乏为 78.1%。这可以成为帮助和协助农民进行营养管理的一种新型实用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
Advanced assessment of nutrient deficiencies in greenhouse with electrophysiological signals

Nutrient deficiencies are one of the main causes of significant reductions in commercial crop production by affecting associated growth factors. Proper plant nutrition is crucial for crop quality and yield therefore, early and objective detection of nutrient deficiency is required. Recent literature has explored the real-time monitoring of plant electrical signal, called electrophysiology, applied on tomato crop cultivated in greenhouse. This sensor allows to identify the stressed state of a plant in the presence of different biotic and abiotic stressors by employing machine learning techniques. The aim of this study was to evaluate the potential of electrophysiology signal recordings acquired from tomato plants growing in a production greenhouse environment, to detect the stress of a plant triggered by the deficiency of several main nutrients. Based on a previously proposed workflow consisting of continuous acquisition of electrical signal then application of machine learning techniques, the minimum signal features was evaluated. This study presents classification models that are able to distinguish the plant’s stressed state with good accuracy, namely 78.5% for manganese, 78.1% for iron, 89.6% for nitrogen, and 78.1% for calcium deficiency, and therefore suggests a novel path to detect nutrient deficiencies at an early stage. This could constitute a novel practical tool to help and assist farmers in nutrition management.

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
Horticulture Environment and Biotechnology
Horticulture Environment and Biotechnology Agricultural and Biological Sciences-Horticulture
CiteScore
4.30
自引率
4.20%
发文量
0
审稿时长
6 months
期刊介绍: Horticulture, Environment, and Biotechnology (HEB) is the official journal of the Korean Society for Horticultural Science, was launched in 1965 as the "Journal of Korean Society for Horticultural Science". HEB is an international journal, published in English, bimonthly on the last day of even number months, and indexed in Biosys Preview, SCIE, and CABI. The journal is devoted for the publication of original research papers and review articles related to vegetables, fruits, ornamental and herbal plants, and covers all aspects of physiology, molecular biology, biotechnology, protected cultivation, postharvest technology, and research in plants related to environment.
期刊最新文献
Alternating red-blue light alleviated physiological injury by reducing oxidative stress under both high light and continuous light from red-blue LEDs Comparative chloroplast genomic analysis of an important horticultural plant, Dendrobium sulcatum (Orchidaceae) and phylogenetic position in Dendrobium Triple-color FISH chromosome constitution and genome size analyses confirmed the ploidy of newly developed ten Hibiscus Sp. Cultivars with different breeding history The potential of coated iron nanoparticles for modulating of negative effects of salinity stress in Ajowan Impact of pre-harvest application of 1-methylcyclopropene (1-MCP) on fruit quality, physiological disorders and respiration rate of ‘Scilate’ apple
×
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