[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123149-en":3,"doc-seo-123149-105":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},123149,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Rock Melon Crop Yield Prediction using Supervised Classification Machine Learning on Cloud Computing","Precision agriculture supports farmers in improving crop yields while reducing costs, but accurate early-stage prediction remains difficult, leading to mismanaged labour and resources and reduced production. Existing approaches rely heavily on costly tools and manual observations that are less precise. This study presents a cloud-based crop yield prediction system that uses early growth measurements, pollination treatment, leaf conditions, and variety, applying logistic regression, k-nearest neighbour, and random forest classifiers.","Journal of Advanced Research in Applied Sciences and Engineering Technology 54, Issue 2 (2025) 200-217  \n\n|  | Journal of Advanced Research in Applied Sciences and Engineering Technology\u003Cbr>Journal homepage:\u003Cbr>[https://semarakilmu.com. my/journals/index.php/applied_sciences_eng_tech/index](https://semarakilmu.com. my/journals/index.php/applied_sciences_eng_tech/index)\u003Cbr>ISSN: 2462-1943 |  |  |\n| --- | --- | --- | --- |\n| Rock Melon Crop Yield Prediction using Supervised Classification Machine Learning on Cloud Computing\u003Cbr>Mohamad Khairul Zamidi Zakaria1, Sazlinah Hasan1,*, Rohaya Latip 1, Indrarini Dyah Irawati2, A.V. Senthil Kumar3\u003Cbr>1 Department of Communication Technology and Network, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia\u003Cbr>2 School of Applied Science, Telkom University, Kabupaten Bandung, Jawa Barat 40257, Indonesia\u003Cbr>3 Hindusthan College of Arts & Science, Coimbatore, Tamil Nadu 641028, India |  |  |  |\n| ABSTRACT |  |  |  |\n| Keywords:\u003Cbr>Agriculture; Machine learning; Cloud computing; Logistic regression; Random forest; K-nearest neighbour |  | Precision agriculture is a technology-driven approach to farmer to improve their crop yields and reduce costs. One of the major challenges facing farmers today is the lack of precise prediction which leads to decreased production and mismanagement of labour and resource. Precision technology is costly, and they only rely on manual observations which are less precise. Crop yield prediction systems on cloud computing can solve both problems by predicting the harvested fruit at earlier stages of farming and ease farmers to make decisions. In this study, we proposed a crop yield prediction system for farmers that utilizes cloud computing and machine learning techniques. The system uses data on the physical growth of the plant such as plant’s height at 15 and 30 days after transplant, type of pollination treatment, condition of the leaves, and their variety to predict the crop yield at the early stage. Logistic regression, k-nearest neighbour, and random forest classifier were used to compare the accuracy of the model. Our result shows that by using a random forest classifier, it can achieve an accuracy of 91% which is higher than logistic regression which is only 73% of accuracy, and k-nearest neighbour with 82% accuracy. The study highlights the potential of precision agriculture, cloud computing, and machine learning to revolutionize the way farmers manage their crops and increase their efficiency and productivity, even with the limited resources and hardware that many farmers have. |  |\n| 1. Introduction\u003Cbr>Agriculture is an industry that has co-existed with human societal evolution and the advancement of our species. Resource management in agriculture plays a key role in tackling important issues we face today, such as overpopulation and food supply management. Toward better food security, Malaysia already has implemented National Food Policy Action 2021-2025. The development of technology, enabling research and studies, empowering food security data, expanding strategic |  |  |  |\n\n* Corresponding author.  \n[E-mail address: khairulzamidi2000@gmail.com](E-mail address: khairulzamidi2000@gmail.com)  \n[https://doi.org/10.37934/araset.54.2.200217](https://doi.org/10.37934/araset.54.2.200217)  \ncollaboration, and bolstering departmental and agency governance were all described as part of the plan's five fundamental initiatives [1] .  \nOne of the problems is farmers experience decreased production due to a lack of understanding of their crop’s potential yield. This leads to a lack of proper planning for labour and resource needs. Without accurate predictions, farmers cannot estimate how much fruit can be harvested in a season. In some scenarios, when the produce is more than the farmers expected, it can lead to storage problems or waste due to no buyers. Another problem is farmers spend more energy on","cbCaiixU62tym51K","https://ap.wps.com/l/cbCaiixU62tym51K","pdf",2845212,1,18,"English","en",105,"# Abstract\n# Introduction\n## Precision agriculture challenges\n## Objectives of the proposed system\n## Role of cloud computing and machine learning","[{\"question\":\"What problems does the study target in precision agriculture for farmers?\",\"answer\":\"The study targets the lack of precise early prediction, which can reduce production and cause labour and resource mismanagement. It also addresses the cost and lower accuracy of relying on manual observations.\"},{\"question\":\"What inputs does the proposed Rock melon yield prediction system use?\",\"answer\":\"It uses early plant growth data such as plant height at 15 and 30 days after transplant, pollination treatment type, leaf condition, and variety to predict crop yield at an early stage.\"},{\"question\":\"Which machine learning models are compared, and what accuracy is reported?\",\"answer\":\"Logistic regression, k-nearest neighbour, and random forest classifier are compared. The random forest classifier achieves the highest accuracy at 91%, compared with logistic regression at 73% and k-nearest neighbour at 82%.\"}]","Rock Melon Crop Yield Prediction using Supervised Classification Machine Learning on Cloud Computing | PDF",1785814924,45,{"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},"rock-melon-crop-yield-prediction-using-supervised-classification-machine-learning-on-cloud-computing","",{"@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/rock-melon-crop-yield-prediction-using-supervised-classification-machine-learning-on-cloud-computing/123149/",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 problems does the study target in precision agriculture for farmers?","Question",{"text":75,"@type":76},"The study targets the lack of precise early prediction, which can reduce production and cause labour and resource mismanagement. It also addresses the cost and lower accuracy of relying on manual observations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What inputs does the proposed Rock melon yield prediction system use?",{"text":80,"@type":76},"It uses early plant growth data such as plant height at 15 and 30 days after transplant, pollination treatment type, leaf condition, and variety to predict crop yield at an early stage.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are compared, and what accuracy is reported?",{"text":84,"@type":76},"Logistic regression, k-nearest neighbour, and random forest classifier are compared. The random forest classifier achieves the highest accuracy at 91%, compared with logistic regression at 73% and k-nearest neighbour at 82%.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]