[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120167-en":3,"doc-seo-120167-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120167,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Satellite Imagery Solution for Rice Crop Yield Estimation using Machine Learning Models","Machine learning models for crop yield prediction using satellite imagery enable accurate, reliable, and timely rice yield estimation for India’s rice-producing regions. The research develops and evaluates models that learn from satellite-derived area information, including Google Dynamic World Earth Satellite imagery. Weather, irrigation, NDVI, temperature, and season variables are collected from multiple sources, then analyzed using XGBoost Gradient, Random Forest, and Support Vector Regressor. XGBoost delivers the strongest performance, with RMSE 80,400 and R² = 0.94, supporting agricultural decision-making for farmers and policymakers.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| ACIS 2023 Proceedings | Australasian (ACIS) |\n| --- | --- |\n| 12-2-2023\u003Cbr>Satellite Imagery Solution for Rice Crop Yield Estimation using Machine Learning Models\u003Cbr>Shruti Mantri\u003Cbr>Indian School of Business, India, [shrutimantri@gmail.com](shrutimantri@gmail.com)\u003Cbr>Seema Purohit\u003Cbr>B. K. Birla College of Arts, Science & Commerce (Autonomous), India, [supurohit@gmail.com](supurohit@gmail.com)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/acis2023](https://aisel.aisnet.org/acis2023) |  |\n\nRecommended Citation  \nMantri, Shruti and Purohit, Seema, \"Satellite Imagery Solution for Rice Crop Yield Estimation using Machine Learning Models\" (2023) . ACIS 2023 Proceedings. 73.  \n[https://aisel.aisnet.org/acis2023/73](https://aisel.aisnet.org/acis2023/73)  \nThis material is brought to you by the Australasian (ACIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ACIS 2023 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact elibrary@aisnet.org](contact elibrary@aisnet.org).  \nSatellite Imagery Solution for Rice Crop Yield Estimation using Machine Learning Models  \nFull research paper  \nShruti Mantri  \nInstitute of Data Sciences, Indian School of Business, Hyderabad, India  \nEmail: [shrutimantri@gmail.com](shrutimantri@gmail.com)  \nSeema Purohit  \nDept. of IT, DS and AI,  \nB. K. Birla College of Arts, Science and Commerce, Kalyan, India  \nEmail: [supurohit@gmail.com](supurohit@gmail.com)  \nAbstract  \nMachine learning models for crop yield prediction using satellite imagery allow accurate, reliable, and timely estimations of crop yield. The aim and objective of this research are to develop and evaluate machine learning models to estimate crop yield from satellite data. In the current study, machine learning models are applied to predict rice crop yield in the rice-producing regions of India. The study area is computed from the Google Dynamic World Earth Satellite images. Weather, irrigation, NDVI, temperature, and season data are obtained from various sources. The collected data is analysed using XGBoost Gradient, Random Forest, and Support Vector Regressor. The models are trained and tested. The results indicated XGBoost is the best model with a root mean square error of 80, 400 for rice, whereas R2 = 0.94 is for the same crop datasets. The findings of the study can facilitate agricultural decision-making for policymakers and farmers.  \nKeywords Crop Production, Satellite Imagery, Area, XGBoost Gradient.  \n1 Introduction  \nAs per the United Nations Report, world’s population is expected to reach 8.6 billion in 2030 and 11.2 billion in 2100. The increase in population growth drives up global food demand. The global food demand is expected to increase from 59% to 98% by 2050. Farmers around the world need to improve crop production by enhancing productivity on existing agricultural lands through irrigation and by adopting new techniques such as precision framing. Agriculture plays a vital role in a country’s economy. Since the ancient period, agriculture has been considered the foremost culture practiced in India (Kaleet al.2023). Due to its huge population, India has an increasing demand for food and is also the largest employer of workforce. The pandemic has accelerated growth in the agricultural sector i.e., a growth of 3.9% in 2021-22; accounting for more than 18.8%(2021-22) in gross value added (GVA) of the country but it is come down to 18.3% in 2022-2023 according to the Ministry of Statistics and Program Implementation (MoSPI). One way of dealing with this is to forecast agricultural products.  \nMost crops depend on multiple different auxiliary factors such as water, wind sunlight, temperature, rainfall, etc. (Amankulova et al. 2023) . In India, crop yield mainly depends on weather conditions (Kaleet al.2023) . The rice is the most cultivated food c","cbCaibwVEX8maUoM","https://ap.wps.com/l/cbCaibwVEX8maUoM","pdf",1212450,1,12,"English","en",105,"# Abstract\n# 1 Introduction\n## Motivation and food security context\n## Rice production importance and forecasting need\n## Existing estimation approaches and limitations\n# Keywords","[{\"question\":\"What is the main goal of the research?\",\"answer\":\"To develop and evaluate machine learning models that estimate rice crop yield from satellite data in India’s rice-producing regions.\"},{\"question\":\"Which satellite and environmental data are used?\",\"answer\":\"The study uses Google Dynamic World Earth Satellite images to compute the study area, plus weather, irrigation, NDVI, temperature, and season data from various sources.\"},{\"question\":\"Which machine learning model performs best and what metrics are reported?\",\"answer\":\"XGBoost is the best model, achieving RMSE of 80,400 for rice and R² of 0.94 on the crop datasets.\"}]","Satellite Imagery Solution for Rice Crop Yield Estimation using Machine Learning Models | PDF",1785728521,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"satellite-imagery-solution-for-rice-crop-yield-estimation-using-machine-learning-models","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/satellite-imagery-solution-for-rice-crop-yield-estimation-using-machine-learning-models/120167/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the research?","Question",{"text":75,"@type":76},"To develop and evaluate machine learning models that estimate rice crop yield from satellite data in India’s rice-producing regions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which satellite and environmental data are used?",{"text":80,"@type":76},"The study uses Google Dynamic World Earth Satellite images to compute the study area, plus weather, irrigation, NDVI, temperature, and season data from various sources.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performs best and what metrics are reported?",{"text":84,"@type":76},"XGBoost is the best model, achieving RMSE of 80,400 for rice and R² of 0.94 on the crop datasets.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]