Coconut Disease Prediction System Using Image Processing and Deep Learning Techniques

Project Code :TCMAPY918

Objective

The objective of the Coconut Disease Prediction System using Image Processing and Deep Learning Techniques is to develop a robust and efficient system that can accurately identify and classify diseases affecting coconut trees based on images of their leaves or other relevant parts. This system aims to aid farmers, agricultural experts, and researchers in early detection and management of coconut diseases, which can significantly affect crop yield and overall agricultural productivity.

Abstract

The Coconut Disease Prediction System employs cutting-edge Image Processing and Deep Learning techniques to revolutionize agricultural disease management. By analyzing high-resolution images of coconut palms, this system accurately detects and classifies diseases, enhancing early diagnosis. Leveraging Convolutional Neural Networks (CNNs) and advanced algorithms, it provides farmers with real-time disease risk assessments, enabling timely interventions. The system's user-friendly interface facilitates seamless integration into agricultural practices, empowering farmers to make informed decisions for crop protection and yield optimization. This innovative approach promises to revolutionize coconut farming, ensuring sustainable production and mitigating economic losses due to diseases.

Keywords: Coconut leaf disease dataset and deep learning algorithms

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