Ricardo Yauri, Oscar Llerena, Jorge Santiago, Jean Gonzales
{"title":"Sprinkler Irrigation Automation System to Reduce the Frost Impact Using Machine Learning","authors":"Ricardo Yauri, Oscar Llerena, Jorge Santiago, Jean Gonzales","doi":"10.32985/ijeces.14.7.8","DOIUrl":null,"url":null,"abstract":"Frosts reduce the ambient temperature to the freezing point of water, affecting the agricultural sector and the integrity of plant tissues, severely damaged by freezing, destroying plant cells. In addition, losses are generated in the economy due to the death of cattle due to cold, hunger, diseases, etc. Latin America is a region that depends, to a considerable extent, on its crops for its consumption and export, so frost represents an urgent problem to solve, considering that in Perú the area of agriculture is not technical. Among the methods most used by farmers is anticipated irrigation, through automatic learning techniques, which allows predicting the behavior of a variable based on previous historical data. In this paper, sprinkler irrigation is implemented in crops exposed to frost, using an automated system with machine learning techniques and prediction models. Therefore, three types of models are evaluated (linear regression, random forests, and decision trees) to predict the occurrence of frosts, reducing damage to plants. The results show that the protection activation indicator from 1.1°C to 1.7°C was updated to decrease the number of false positives. On the three models evaluated, it is determined that the most accurate method is the Random Forest Regression method, which has 80.91% reliability, absolute mean error, and mean square error close to zero.","PeriodicalId":41912,"journal":{"name":"International Journal of Electrical and Computer Engineering Systems","volume":"86 1","pages":"0"},"PeriodicalIF":0.8000,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Electrical and Computer Engineering Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32985/ijeces.14.7.8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 1
Abstract
Frosts reduce the ambient temperature to the freezing point of water, affecting the agricultural sector and the integrity of plant tissues, severely damaged by freezing, destroying plant cells. In addition, losses are generated in the economy due to the death of cattle due to cold, hunger, diseases, etc. Latin America is a region that depends, to a considerable extent, on its crops for its consumption and export, so frost represents an urgent problem to solve, considering that in Perú the area of agriculture is not technical. Among the methods most used by farmers is anticipated irrigation, through automatic learning techniques, which allows predicting the behavior of a variable based on previous historical data. In this paper, sprinkler irrigation is implemented in crops exposed to frost, using an automated system with machine learning techniques and prediction models. Therefore, three types of models are evaluated (linear regression, random forests, and decision trees) to predict the occurrence of frosts, reducing damage to plants. The results show that the protection activation indicator from 1.1°C to 1.7°C was updated to decrease the number of false positives. On the three models evaluated, it is determined that the most accurate method is the Random Forest Regression method, which has 80.91% reliability, absolute mean error, and mean square error close to zero.
期刊介绍:
The International Journal of Electrical and Computer Engineering Systems publishes original research in the form of full papers, case studies, reviews and surveys. It covers theory and application of electrical and computer engineering, synergy of computer systems and computational methods with electrical and electronic systems, as well as interdisciplinary research. Power systems Renewable electricity production Power electronics Electrical drives Industrial electronics Communication systems Advanced modulation techniques RFID devices and systems Signal and data processing Image processing Multimedia systems Microelectronics Instrumentation and measurement Control systems Robotics Modeling and simulation Modern computer architectures Computer networks Embedded systems High-performance computing Engineering education Parallel and distributed computer systems Human-computer systems Intelligent systems Multi-agent and holonic systems Real-time systems Software engineering Internet and web applications and systems Applications of computer systems in engineering and related disciplines Mathematical models of engineering systems Engineering management.