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COCONUT PREDICTIVE ANALYSIS USING MACHINE LEARNING

Ms.ILAMATHI D, Ms. JAYA SREE J, Ms. MONISHA V

Mrs. GAYATRHI.N (MENTOR)

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

      SRI SHAKTHI INSTITUTE OF ENGINEERING AND TECHNOLOGY ,  COIMBATORE.

 

 

ABSTRACT:

“Coconut Predictive Analysis” focuses on leveraging advanced machine learning techniques to predict the suitability of coconuts for oil production. The project employs transfer learning with the Inception V3 model, a state-of-the-art convolutional neural network (CNN), to analyze complex patterns in coconut-related data, such as images of coconuts. Traditional methods for determining coconut suitability for oil production rely on manual inspection, which can be time-intensive, subjective, and inconsistent. By utilizing transfer learning, this project capitalizes on pre-trained Inception V3 models, which extract meaningful features from large-scale datasets like ImageNet, and fine-tunes them for the specific task of classifying dry coconuts based on their suitability for oil extraction.

The fine-tuning process involves freezing initial layers to retain general features while optimizing later layers to improve prediction accuracy for coconut-specific characteristics such as texture, colour, and surface patterns. To ensure accessibility and usability, a Flask web application is developed. This application enables users to upload images of coconuts and receive real-time predictions on their suitability for oil production. The backend integrates the trained model to process inputs, extract features, and generate predictions, making it a scalable and practical solution for agricultural and industrial applications.

This project demonstrates the potential of transfer learning in enhancing agricultural and processing efficiency by reducing training time and computational costs while providing accurate and actionable predictions. By combining cutting-edge deep learning with a user-friendly web interface, it bridges the gap between advanced AI models and practical applications, supporting decision-making for farmers, coconut processing units, and industry stakeholders.

 

Key Words: Inception V3, fine-tuning, CNN, Flask, coconut analysis, oil production suitability

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