{"title":"优化每小时温湿度设定值的生成方法，以减少番茄病害并节约温室电力成本","authors":"","doi":"10.1016/j.compag.2024.109413","DOIUrl":null,"url":null,"abstract":"<div><h3>Context</h3><p>Grey leaf spot is a main leaf disease of tomato in Mediterranean greenhouses, characterized by warm temperatures and high humidity during the spring and winter seasons, hence suitable for pathogen infection and spore spread. Consequently, the utilization of automatic control and optimization algorithms has emerged as effective means to prevent chemical-oriented disease control and enhancing the overall quality and safety of food and crops.</p></div><div><h3>Objective</h3><p>The aim of this work is to search an optimal strategy for precision management on greenhouse tomato growth environment. So, multi-objective optimization rises to an alternative to achieve this goal. While there were lots of research on determining trajectories to control a desired crop growth, and lacking works that optimize climate conditions for restraining the damage of disease on crop.</p></div><div><h3>Methods</h3><p>Based on the multi-objective genetic algorithm optimization method (MOGA), the solution balances the conflict of two objectives: minimum power cost caused by climate control and maximum health leaves with few effects of grey leaf spot. This study also highlights disease and high temperature impact on tomato growth, which are as inequality constraints of the optimization problems.</p></div><div><h3>Results and conclusions</h3><p>The results showed MOGA strategy performers good, the minimum power cost is only need 0.084€*day<sup>−1</sup> in warm weather condition, as well as 3.74 €*day<sup>−1</sup> in cold weather condition, the uninfected LAI (m<sup>2</sup>[Leaves](m<sup>−2</sup>[soils]*day<sup>−1</sup>)) is the range of [0.14 0.20]. The yearly power cost at least [308 1365]€.These are able to embed within a control scheme for achieving optimization purpose.</p></div><div><h3>Significance</h3><p>The farmer receives the data necessary for decision-making to establish the setpoints during the crop cycle, modifying the control decisions, lowering production costs, reducing the use of pesticides and increasing the system efficiency to optimize crop growth.</p></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An optimized approach to hourly temperature and humidity setpoint generation for reducing tomato disease and saving power cost in greenhouses\",\"authors\":\"\",\"doi\":\"10.1016/j.compag.2024.109413\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Context</h3><p>Grey leaf spot is a main leaf disease of tomato in Mediterranean greenhouses, characterized by warm temperatures and high humidity during the spring and winter seasons, hence suitable for pathogen infection and spore spread. Consequently, the utilization of automatic control and optimization algorithms has emerged as effective means to prevent chemical-oriented disease control and enhancing the overall quality and safety of food and crops.</p></div><div><h3>Objective</h3><p>The aim of this work is to search an optimal strategy for precision management on greenhouse tomato growth environment. So, multi-objective optimization rises to an alternative to achieve this goal. While there were lots of research on determining trajectories to control a desired crop growth, and lacking works that optimize climate conditions for restraining the damage of disease on crop.</p></div><div><h3>Methods</h3><p>Based on the multi-objective genetic algorithm optimization method (MOGA), the solution balances the conflict of two objectives: minimum power cost caused by climate control and maximum health leaves with few effects of grey leaf spot. This study also highlights disease and high temperature impact on tomato growth, which are as inequality constraints of the optimization problems.</p></div><div><h3>Results and conclusions</h3><p>The results showed MOGA strategy performers good, the minimum power cost is only need 0.084€*day<sup>−1</sup> in warm weather condition, as well as 3.74 €*day<sup>−1</sup> in cold weather condition, the uninfected LAI (m<sup>2</sup>[Leaves](m<sup>−2</sup>[soils]*day<sup>−1</sup>)) is the range of [0.14 0.20]. The yearly power cost at least [308 1365]€.These are able to embed within a control scheme for achieving optimization purpose.</p></div><div><h3>Significance</h3><p>The farmer receives the data necessary for decision-making to establish the setpoints during the crop cycle, modifying the control decisions, lowering production costs, reducing the use of pesticides and increasing the system efficiency to optimize crop growth.</p></div>\",\"PeriodicalId\":50627,\"journal\":{\"name\":\"Computers and Electronics in Agriculture\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers and Electronics in Agriculture\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0168169924008044\",\"RegionNum\":1,\"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":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169924008044","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
An optimized approach to hourly temperature and humidity setpoint generation for reducing tomato disease and saving power cost in greenhouses
Context
Grey leaf spot is a main leaf disease of tomato in Mediterranean greenhouses, characterized by warm temperatures and high humidity during the spring and winter seasons, hence suitable for pathogen infection and spore spread. Consequently, the utilization of automatic control and optimization algorithms has emerged as effective means to prevent chemical-oriented disease control and enhancing the overall quality and safety of food and crops.
Objective
The aim of this work is to search an optimal strategy for precision management on greenhouse tomato growth environment. So, multi-objective optimization rises to an alternative to achieve this goal. While there were lots of research on determining trajectories to control a desired crop growth, and lacking works that optimize climate conditions for restraining the damage of disease on crop.
Methods
Based on the multi-objective genetic algorithm optimization method (MOGA), the solution balances the conflict of two objectives: minimum power cost caused by climate control and maximum health leaves with few effects of grey leaf spot. This study also highlights disease and high temperature impact on tomato growth, which are as inequality constraints of the optimization problems.
Results and conclusions
The results showed MOGA strategy performers good, the minimum power cost is only need 0.084€*day−1 in warm weather condition, as well as 3.74 €*day−1 in cold weather condition, the uninfected LAI (m2[Leaves](m−2[soils]*day−1)) is the range of [0.14 0.20]. The yearly power cost at least [308 1365]€.These are able to embed within a control scheme for achieving optimization purpose.
Significance
The farmer receives the data necessary for decision-making to establish the setpoints during the crop cycle, modifying the control decisions, lowering production costs, reducing the use of pesticides and increasing the system efficiency to optimize crop growth.
期刊介绍：
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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