Coconut plant disease identified and management for agriculture crops using machine learning Wijethunga C.D.1,*, Ishanka K.C.2, Parindya S.D.N.3, Priyadarshani T.J.N.4, Harshanath Buddika5, Rajapaksha Samantha6 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, Sri Lanaka 6Department of Information Technology, Sri Lanka Institute of Information Technology, Malabe, Sri Lanaka *Corresponding Author: chathurangawijethunga@gmail.com
Online Published on 8 February, 2024. Abstract This 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. Top Keywords Pest Detection, Machine Learning, Sustainable Cultivation, Grading, Image Processing, Coconut Industry. Top |