[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121207-en":3,"doc-seo-121207-105":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":4,"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},121207,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Evaluation of Machine Learning Models for Mapping Food Crops using Sentinel-2A Imagery in West Java, Indonesia","Food crop distribution patterns and locations are essential for monitoring agricultural sustainability and strengthening food security. Machine-learning-based plant classification is widely used to detect cropland areas, yet challenges remain in mapping plant types and estimating crop area efficiently. This study evaluates SVM, Random Forest, and CART for mapping and calculating rice crop area in West Java, Indonesia, using Sentinel-2A time-series features (NDVI and EVI) in Google Earth Engine.","Evaluation of Machine Learning Models for Mapping Food Crops using Sentinel-2A Imagery in West Java, Indonesia  \nRiki Ridwana1,2, Muhammad Kamal3, Sanjiwana Arjasakusuma3, and Muh Fiqri Abdi  \nRabbi4  \n1Mapping Survey and Geographic Information Study Program, Faculty of Social Sciences Education, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudhi No. 229, Bandung, Indonesia  \n2Doctoral Program in Geography, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia  \n3Departement of Geography Information Science, Faculty of Geography, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281, Indonesia  \n4Geography Information Science Study Program, Faculty of Social Sciences Education, Universitas Pendidikan Indonesia, Jawa Barat, Indonesia.  \nAbstract. Data on the distribution patterns and locations of food crops are  \ncrucial for monitoring and controlling the sustainability of agricultural  \nresources and guaranteeing food security. Plant classification based on  \nmachine learning has been widely used to detect food crop areas. However,  \nthere are still challenges in mapping plant types and plant area effectively  \nand efficiently. The aim of this research is to evaluate machine learning  \nmodels in mapping and calculating the area of food crops (rice) in West Java  \nProvince, Indonesia. Google Earth Engine is used in this study as a big data  \ncloud computing platform for remote sensing. Normalized Difference  \nVegetation Index (NDVI) and Enhanced Vegetation Index (EVI) Sentinel-  \n2A imagery is utilized to employ time series data as input characteristics for  \nthe three most popular machine learning models: Support Vector Machine  \n(SVM), Random Forest (RF), and Classification and Regression Trees  \n(CART) . The research results show that the three machine learning models  \nare able to map and calculate the area of food crops in West Java, Indonesia.  \nThe RF algorithm produces the highest overall accuracy rate (98.51%) and  \nis the fastest in the accuracy assessment and image classification process  \ncompared to the SVM and CART algorithms.  \n1 Introduction  \nThe quick expansion of world urbanization and population is pressing for food production from limited agricultural resources to maintain food security [1–4] . In order to satisfy the demands for food supply, a large number of sophisticated agricultural systems have been developed with careful monitoring and management of agricultural resources to boost efficiency and crop yields [5-7]. Monitoring and managing sustainable agricultural resources greatly benefits from knowing the locations and patterns of food crop distribution [8-11] .  \nThis information is used as a basis for policy makers in estimating harvest yields [12, 13],  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nEstimated crop specific water consumption [14-16], disaster assessment [17, 18], and ensuring balanced regional food security [19, 20]. Consequently, crop maps are a trustworthy tool for sustainable agriculture management that decision makers may rely on.  \nSo far, spatial information on plant types and planting areas still uses traditional methods, namely based on field surveys, area unit frameworks, and statistical reports, which take a lot of time, are labor intensive, cost a lot of money, and lack timely data updates [21–23] . On the other hand, Nowadays, plant kinds may be reliably mapped using remote sensing technology, which offers broad coverage capabilities, quick data collecting, and cost and human resource savings [24-26] . Although methods for mapping plant types in the last two decades have been developed using different remote sensing data [27–29], There are still gaps and challenges to develop remote sensing technology.  \nOne of these challenges is the limitation between ima","cbCaigAKi1nA7fuM","https://ap.wps.com/l/cbCaigAKi1nA7fuM","pdf",1238291,1,17,"English","en",105,"# Introduction\n## Food security needs and agricultural monitoring\n## Remote sensing for crop mapping\n## Challenges: data quality vs cost and resolution limits\n## Sentinel-2A as an alternative for mapping\n## Agronomic factors affecting classification","[{\"question\":\"What is the goal of this research on food crops mapping?\",\"answer\":\"To evaluate machine learning models for mapping and calculating the area of food crops, specifically rice, in West Java, Indonesia.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) are used and compared for mapping crop areas.\"},{\"question\":\"Why are NDVI and EVI from Sentinel-2A imagery used?\",\"answer\":\"NDVI and EVI Sentinel-2A time series are used as input characteristics to capture vegetation signals that support plant type mapping and area estimation.\"}]","Evaluation of Machine Learning Models for Mapping Food Crops using Sentinel-2A Imagery in West Java, Indonesia | 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is the goal of this research on food crops mapping?","Question",{"text":75,"@type":76},"To evaluate machine learning models for mapping and calculating the area of food crops, specifically rice, in West Java, Indonesia.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the study?",{"text":80,"@type":76},"Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) are used and compared for mapping crop areas.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are NDVI and EVI from Sentinel-2A imagery used?",{"text":84,"@type":76},"NDVI and EVI Sentinel-2A time series are used as input characteristics to capture vegetation signals that support plant type mapping and area 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