[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123170-en":3,"doc-seo-123170-105":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},123170,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","IMPROVING AGRICULTURAL YIELDS IN THE DEMOCRATIC REPUBLIC OF CONGO USING MACHINE LEARNING ALGORITHMS","This article analyzes agricultural yields in the Democratic Republic of Congo using machine learning regression models. The study relies on about 30,000 records across multiple years, with variables covering seed type, climatic conditions such as temperature, rainfall and humidity, soil properties like pH and nutrients, and farming practices including fertilizer use and irrigation. Data are drawn from sources including METTELSAT, WMO, and WorldClim for climate, and from the DRC Ministry of Agriculture and FAO for soil and agricultural information. Models evaluated include linear regression, random forest regression, GBM, SVM, and artificial neural networks, compared via MSE, MAE, RMSE, R², and MAPE on Farm A, Farm B, and Farm C, where ANN yields the strongest performance.","IMPROVING AGRICULTURAL YIELDS IN THE DEMOCRATIC REPUBLIC OF CONGO USING MACHINE LEARNING ALGORITHMS  \nNsimba Malumba Rodolphe1, Longo Kayembe Mardochee2, Balanganayi Kabutakapua Fiston Chrisnovic3, Boluma Mangata Bopatriciat4*, Mazambi Kilongo Trésor5, Tabiaki Tandele Rufin6, Ntanyungu Ndizieye Emmanuel7, Bukanga Christian Parfum8  \nComputer Science1  \nInstitute National du Batiment et des Travaux Publics, Kinshasa, Democratic Republic of Congo 2  \n[https://inbtp.optsolution.net](https://inbtp.optsolution.net1)[1](https://inbtp.optsolution.net1)  \n[rodolphemalumba25@gmail.com](rodolphemalumba25@gmail.com1)[1](rodolphemalumba25@gmail.com1)  \nComputer Science2,3  \nUniversity of Kinshasa, Kinshasa, Democratic Republic of Congo2,3 [https://www.unikin.ac.cd/](https://www.unikin.ac.cd/2)[2](https://www.unikin.ac.cd/2),3  \n[mardochee.longo@unikin.ac.cd](mardochee.longo@unikin.ac.cd2)[2](mardochee.longo@unikin.ac.cd2), [fistonbalang@gmail.com](fistonbalang@gmail.com3)[3](fistonbalang@gmail.com3)  \nComputer Science4*, 8  \nHaute Ecole de Commerce de Kinshasa, Kinshasa, Democratic Republic of Congo4,* 8  \n[https://heckin.ac.cd/etudiant/](https://heckin.ac.cd/etudiant/4)[4](https://heckin.ac.cd/etudiant/4)*, 8  \n[bopatriciat.boluma@unikin.ac.cd](bopatriciat.boluma@unikin.ac.cd4)[4](bopatriciat.boluma@unikin.ac.cd4)* , [parfum.bukanga@unikin.ac.cd](parfum.bukanga@unikin.ac.cd8)[8](parfum.bukanga@unikin.ac.cd8)  \nComputer Science5,6  \nUniversity of Bunia, Bunia, Democratic Republic of Congo5,6  \n[https://www.unibu.ac.cd/](https://www.unibu.ac.cd/5)[5](https://www.unibu.ac.cd/5),6  \n[mazambitresor@outlook.com](mazambitresor@outlook.com5)[5](mazambitresor@outlook.com5), [rufintandele@gmail.com](rufintandele@gmail.com6)[6](rufintandele@gmail.com6)  \nComputer Science7  \nInstitut Supérieur de Commerce de Bunia, Bunia, Democratic Republic of Congo7  \n[https://www.unibu.ac.cd/](https://www.unibu.ac.cd/7)[7](https://www.unibu.ac.cd/7)  \n[bopatriciat@gmail.com](bopatriciat@gmail.com7)[7](bopatriciat@gmail.com7)  \nComputer Science8  \nHaute Ecole de Commerce de Kinshasa, Kinshasa, Democratic Republic of Congo8  \n[https://heckin.ac.cd/etudiant/](https://heckin.ac.cd/etudiant/8)[8](https://heckin.ac.cd/etudiant/8)  \n(*) Corresponding Author  \nCiptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.  \nAbstract—This article presents an analysis of agricultural yields in the Democratic Republic of Congo (DRC) using machine learning algorithms. The study is based on around 30,000 records covering several years of agricultural production. Each record includes variables such as seed type, climatic conditions (temperature, rainfall and humidity), soil characteristics (pH, nutrients), farming practices (fertilizer use, irrigation) and yields obtained. The data comes from a variety of sources, including METTELSAT, the World Meteorological Organization (WMO) and WorldClim for climate data, and the DRC Ministry of Agriculture and the FAO for soil and agricultural data. The algorithms evaluated include linear regression, random forest regression, Gradient  \nBoosting Machines (GBM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). The performance of the algorithms is measured using metrics such as MSE, MAE, RMSE, R ² Score and MAPEon three separate case studies (Farm A, Farm B and Farm C). The results show that artificial neural networks (ANNs) perform best, with MSE ranging from 600 to 850, MAE from 12 to 17, RMSE from 24.49 to 29.15, R ² Score from 0.92 to 0. 95, and MAPE from 8.5% to 10.7%. Next came GBM, random forest regression, SVM and finally linear regression. These results highlight the potential of machine learning algorithms to improve agricultural yield forecasts in the DRC.  \nKeywords: agricultural yields, climatic data, machine learning, , predictive analysis, regression models.  \nIntisari—Artikel ini menyajikan analisis hasil pertanian di Republik Demokratik Kongo (RDK) dengan menggunakan al","cbCairQfOl7gDB7W","https://ap.wps.com/l/cbCairQfOl7gDB7W","pdf",1094904,1,9,"English","en",105,"# Introduction\n# Data and Variables\n## Climate and soil sources\n## Farming practices and yield records\n# Machine Learning Models\n## Linear regression and baseline\n## Random forest, GBM, SVM, and ANN\n# Experimental Setup and Evaluation\n## Case studies (Farm A, Farm B, Farm C)\n## Performance metrics (MSE, MAE, RMSE, R², MAPE)\n# Results and Discussion\n## Model comparison and best-performing approach\n# Conclusion","[{\"question\":\"What data and variables are used to predict agricultural yields in the study?\",\"answer\":\"The dataset includes about 30,000 records with seed type, climatic conditions (temperature, rainfall, humidity), soil characteristics (pH, nutrients), farming practices (fertilizer use, irrigation), and the resulting yields.\"},{\"question\":\"Which machine learning algorithms are evaluated for the yield prediction task?\",\"answer\":\"The study evaluates linear regression, random forest regression, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN).\"},{\"question\":\"How are the models compared, and which algorithm performs best?\",\"answer\":\"Performance is measured using MSE, MAE, RMSE, R² Score, and MAPE across three case studies (Farm A, Farm B, Farm C). Artificial neural networks (ANNs) perform best in all comparisons, with the lowest error ranges reported.\"}]","IMPROVING AGRICULTURAL YIELDS IN THE DEMOCRATIC REPUBLIC OF CONGO USING MACHINE LEARNING ALGORITHMS | PDF",1785815014,23,{"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},"improving-agricultural-yields-in-the-democratic-republic-of-congo-using-machine-learning-algorithms","",{"@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/improving-agricultural-yields-in-the-democratic-republic-of-congo-using-machine-learning-algorithms/123170/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data and variables are used to predict agricultural yields in the study?","Question",{"text":75,"@type":76},"The dataset includes about 30,000 records with seed type, climatic conditions (temperature, rainfall, humidity), soil characteristics (pH, nutrients), farming practices (fertilizer use, irrigation), and the resulting yields.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are evaluated for the yield prediction task?",{"text":80,"@type":76},"The study evaluates linear regression, random forest regression, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN).",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models compared, and which algorithm performs best?",{"text":84,"@type":76},"Performance is measured using MSE, MAE, RMSE, R² Score, and MAPE across three case studies (Farm A, Farm B, Farm C). Artificial neural networks (ANNs) perform best in all comparisons, with the lowest error ranges reported.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]