[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123763-en":3,"doc-seo-123763-105":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},123763,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Coconut Plant Disease Identified and Management for Agriculture Crops using Machine Learning - Research Paper","This research paper proposes an integrated, technology-driven approach to improve the quality and sustainability of coconut farming and exports in Sri Lanka. It uses advanced image processing to detect, classify, and grade pests and diseases at an early stage in coconut palms, enabling faster interventions and reducing reliance on harsh chemical treatments. The framework also evaluates optimal coconut growth conditions by considering soil quality, water availability, and climate, while adding growth prediction through machine learning and historical data to support resource planning and yield maximization.","Coconut Plant Disease Identified and Management for Agriculture Crops  \nusing Machine Learning  \nWijethunga C.D 1, Ishanka K.C2, Parindya S.D.N3, Priyadarshani T.J.N4, Buddika Harshanath5 and Samantha Rajapaksha6 1Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, SRI LANAKA 2Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, SRI LANAKA 3Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, SRI LANAKA 4Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, SRI LANAKA 5Department of Computer Science and Software Engineering, Sri Lanka Institute of Information Technology, Malabe,  \nSRI LANAKA  \n6Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, SRI LANAKA  \n1Corresponding Author: [chathurangawijethunga@gmail.com](chathurangawijethunga@gmail.com)  \nReceived: 25-09-2023 Revised: 11-10-2023 Accepted: 28-10-2023  \nABSTRACT  \nThis research paper introduces an innovative approach to improve the quality and sustainability of coconut farming and exports in Sri Lanka. It employs advanced image processing techniques to detect, classify, and grade pests and diseases early in coconut palms. This allows for swift interventions and reduces the need for harsh chemical treatments, promoting eco-friendly farming practices. Furthermore, the study goes beyond pest control to evaluate optimal conditions for coconut growth, considering factors like soil quality, water availability, and climate. It empowers farmers with insights to maximize coconut palm yield. Additionally, the system incorporates a growth prediction component using historical data and machine learning, enabling farmers to plan and allocate resources effectively. By combining early pest detection, pest management, growth classification, and predictive analysis, this research offers a comprehensive strategy to enhance Sri Lanka's coconut quality for export. This approach not only improves product quality but also safeguards the industry's sustainability by reducing economic losses and ecological impact. Leveraging cutting-edge tools like image processing and machine learning, this research aims to boost efficiency, economic viability, and international competitiveness in Sri Lanka's coconut farming sector.  \nKeywords--Pest Detection, Machine Learning, Sustainable Cultivation, Grading, Image Processing, Coconut Industry  \nI. INTRODUCTION  \nCoconut farming holds a significant position in the agricultural landscape of Sri Lanka, contributing both to domestic sustenance and international trade. However, despite its economic importance, the industry grapples with numerous challenges, with pests, diseases, and suboptimal growth conditions being primary deterrents to  \nachieving optimal coconut export quality. Addressing these challenges requires an innovative and integrated approach that harmonizes cutting-edge technologies with agricultural practices, aiming to enhance both the quality and sustainability of coconut cultivation.  \nPest and disease management has been alongstanding concern for coconut farmers, as infestationsand infections can severely impact crop yield and quality. Conventional pest control methods often rely onindiscriminate pesticide use, leading to ecological imbalances, health hazards, and diminishing product quality. Hence, a paradigm shift towards sustainable solutions that mitigate the impact of pests and diseases while preserving the environment is imperative. This paper proposes an integrated system that couples early pest detection, image processing-based classification, and grading techniques to provide an effective and eco-friendly pest and disease management strategy for coconut farming. The advent of image-processing technologies has opened doors to innovative agricultural practices. Leveraging these advancements, this research introduces a novel approach for ","cbCaiprsFihddYef","https://ap.wps.com/l/cbCaiprsFihddYef","pdf",397831,1,10,"English","en",105,"# Abstract\n# Introduction\n# Background and Literature Review","[{\"question\":\"How does the system identify coconut pests and diseases?\",\"answer\":\"It captures and analyzes images of coconut palms and their foliage using image processing to detect, classify, and grade pests and diseases early.\"},{\"question\":\"Why does early detection matter for coconut farming?\",\"answer\":\"Early detection enables proactive interventions, which helps minimize aggressive chemical use and lowers environmental and health risks while protecting yield and quality.\"},{\"question\":\"How does the research support better coconut yield beyond pest control?\",\"answer\":\"It evaluates optimal growth conditions using factors such as soil quality, water availability, and climate, and includes a growth prediction component powered by historical data and machine learning for planning and resource allocation.\"}]","Coconut Plant Disease Identified and Management for Agriculture Crops using Machine Learning - 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