[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120197-en":3,"doc-seo-120197-105":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},120197,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Leveraging Machine Learning for Crop Disease Detection and Prediction in African (Nigerian) Agriculture - Study","This study investigates machine learning techniques for detecting and predicting crop diseases in Nigerian agriculture. Because crop pests and diseases threaten farm productivity and cause substantial annual yield losses, the research evaluates how early detection can reduce losses through practical ML-driven decision support. The work outlines data collection and the modeling process, and considers the value of integrating ML systems into field practices. Results indicate high accuracy from ML algorithms and support feasibility for broader deployment in African agricultural contexts.","Leveraging Machine Learning for Crop Disease Detection and Prediction in African (Nigerian) Agriculture  \nDr. ANYARAGBU Hope 1 and Engr. Dr. OKORIE Emeka2  \n1Department of Computer Science  \nTansian University Umunya, Anambra State, Nigeria  \n[Anyaragbu.hope@tansianuniversity.edu.ng](Anyaragbu.hope@tansianuniversity.edu.ng)  \n2Department of Computer Science  \nTansian University Umunya, Anambra State, Nigeria  \n[Emeka.okorie@tansianuniversity.edu.ng](Emeka.okorie@tansianuniversity.edu.ng)  \nAbstract – This study investigates the use of machine learning (ML) techniques for detecting and predicting crop diseases in Nigerian agriculture. With agriculture playing a vital role in Nigeria's economy and crop diseases posing significant challenges, the research assesses the effectiveness of various ML algorithms in reducing losses through early detection. It explores data collection methods, the modeling process, and the transformative potential of integrating ML systems into agricultural practices across Africa. The findings demonstrate the high accuracy achieved by machine learning algorithms, underscoring their feasibility for widespread implementation.  \nKeywords – Machine learning, 5G technology, disease detection, disease prediction.  \nI. Introduction  \nAgriculture is a cornerstone of Nigeria’s economy, employing about 70% of the population and contributing nearly 24% to the GDP. Effective tools for diagnosing crop diseases and disseminating agricultural information are essential for growth and development in the sector. Crop pests and diseases pose a significant threat, with annual yield losses of 35–40% reported in the sub-Saharan region [1] . Traditionally, disease detection has relied on agricultural experts. However, there is a notable shift toward using machine learning and computer vision techniques for crop inspection, facilitated by mobile devices [2][3] .  \nFor rural and smallholder farmers, mobile phones serve as vital tools for accessing information, markets, and services. Conventional methods of disease detection, which depend heavily on manual observation, are both time-consuming and errorprone. Disease management in Nigerian agriculture typically involves manual inspection and chemical treatments. While somewhat effective, these methods are labor-intensive, reactive, and lack a preventive approach.  \nThe information needs of farmers are constantly evolving and can be understood as part of an agricultural cycle across different seasons [4] . Farmers frequently seek insights about their farms or gardens and often rely on advice from agricultural experts [5] . In cases where this process has been automated, feedback is typically delayed [6] . For instance, some diagnostic applications take approximately 5–7 days to provide farmers with feedback [7] . The lack of real-time information has contributed to poor farming practices among smallholder farmers, resulting in significant yield losses. Annually, crop diseases and pests cause yield losses of 20–40%, further intensifying food insecurity and poverty.  \nThis study explores the application of machine learning (ML) to provide real-time diagnosis and assessment of crop diseases directly in the field, empowering non-experts to take timely actions. It examines how ML techniques can transform disease detection and prediction by enabling early interventions and reducing losses.  \n2. Research Methodology  \nThis study adheres to the PRISMA guidelines, an evidence-based framework for conducting systematic reviews. The focus is exclusively on the application of machine learning in farming. The search timeframe spanned from 2016 to 2024, with any articles outside this scope or unrelated to the research focus excluded. Following the PRISMA methodology, a systematic literature search was performed using digital journal databases such as ResearchGate, Google Scholar, and IEEE Xplore.  \nAfter screening, a selection of research publications and articles meeting the eligibility criteri","cbCaivUtM3k3wSZl","https://ap.wps.com/l/cbCaivUtM3k3wSZl","pdf",885282,1,9,"English","en",105,"# Introduction\n## Background and problem in Nigerian agriculture\n## Limitations of traditional disease management\n## Goals of the study\n# Research Methodology\n## PRISMA-based systematic review approach\n## Databases and selection criteria\n## Comparison focus: IoT, Big Data Analytics, and ML\n# Review of Machine Learning Models and Applications in Farming\n## Supervised learning\n## Unsupervised learning\n## Reinforcement learning\n## Semi-supervised learning\n## Transfer learning\n## Ensemble learning","[{\"question\":\"Why is early crop disease detection important in Nigerian agriculture?\",\"answer\":\"Crop pests and diseases significantly reduce yields, contributing to food insecurity and poverty. Early detection helps reduce losses by enabling timely interventions rather than relying only on reactive measures.\"},{\"question\":\"What methodology does the study use to gather and analyze research?\",\"answer\":\"The study follows PRISMA guidelines for evidence-based systematic reviews, searching literature from 2016 to 2024 in databases such as ResearchGate, Google Scholar, and IEEE Xplore.\"},{\"question\":\"What machine learning learning paradigms are discussed in the model review?\",\"answer\":\"The document covers supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, transfer learning, and ensemble learning, highlighting how each supports different data and training conditions.\"}]","Leveraging Machine Learning for Crop Disease Detection and Prediction in African (Nigerian) Agriculture - Study | PDF",1785728669,23,{"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},"leveraging-machine-learning-for-crop-disease-detection-and-prediction-in-african-nigerian-agriculture-study","",{"@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/leveraging-machine-learning-for-crop-disease-detection-and-prediction-in-african-nigerian-agriculture-study/120197/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early crop disease detection important in Nigerian agriculture?","Question",{"text":75,"@type":76},"Crop pests and diseases significantly reduce yields, contributing to food insecurity and poverty. Early detection helps reduce losses by enabling timely interventions rather than relying only on reactive measures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What methodology does the study use to gather and analyze research?",{"text":80,"@type":76},"The study follows PRISMA guidelines for evidence-based systematic reviews, searching literature from 2016 to 2024 in databases such as ResearchGate, Google Scholar, and IEEE Xplore.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning learning paradigms are discussed in the model review?",{"text":84,"@type":76},"The document covers supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, transfer learning, and ensemble learning, highlighting how each supports different data and training conditions.","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"]