{"title":"Automatic Irrigation Model Powered by Smart Rain Prediction Device in India","authors":"Mahadeo Ambildhuke Geeta, Gupta Banik Barnali","doi":"10.4314/jae.v27i1.9","DOIUrl":null,"url":null,"abstract":"This paper presents a simple rain prediction device-based automatic irrigation management algorithm using a combination of weather parameters and soil moisture measurements for the water balance required for a crop at each condition during its growing phase that will reduce farmer intervention for irrigation and avoid unnecessary irrigation by predicting the rainfall before starting the motor for irrigating the field. This device is powered by various technologies like deep learning to classify clouds responsible for rain, machine learning models to predict rainfall based on atmospheric parameters and the Internet of Things (IoT) using different sensors to collect data from the field. This algorithm is very appropriate for farmers who are in remote locations and are not able to use the internet and WIFI due to its unavailability. The device will be attached to the motor, will take the data from sensors and will do the rain prediction at device level only and will switch ON/OFF the motor based on the soil moisture value and rain prediction without any human intervention.","PeriodicalId":43669,"journal":{"name":"Journal of Agricultural Extension","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Agricultural Extension","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4314/jae.v27i1.9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents a simple rain prediction device-based automatic irrigation management algorithm using a combination of weather parameters and soil moisture measurements for the water balance required for a crop at each condition during its growing phase that will reduce farmer intervention for irrigation and avoid unnecessary irrigation by predicting the rainfall before starting the motor for irrigating the field. This device is powered by various technologies like deep learning to classify clouds responsible for rain, machine learning models to predict rainfall based on atmospheric parameters and the Internet of Things (IoT) using different sensors to collect data from the field. This algorithm is very appropriate for farmers who are in remote locations and are not able to use the internet and WIFI due to its unavailability. The device will be attached to the motor, will take the data from sensors and will do the rain prediction at device level only and will switch ON/OFF the motor based on the soil moisture value and rain prediction without any human intervention.
期刊介绍:
The Journal of Agricultural Extension (JAE) is devoted to the advancement of knowledge of agricultural extension services and practice through the publication of original and empirically based research, focusing on; extension administration and supervision, programme planning, monitoring and evaluation, diffusion and adoption of innovations; extension communication models and strategies; extension research and methodological issues; nutrition extension; extension youth programme; women-in-agriculture; extension, Climate Change and the environment, ICT, innovation systems. JAE will normally not publish articles based on research covering very small geographic area that cannot feed into policy except they present critical insights into emerging agricultural innovations.