Fruit Ripeness Detection Using Deep Learning

Abstract

The Fruit Ripeness Detection project aims to develop an automated solution for detecting the ripeness of fruits using deep learning techniques, specifically leveraging the VGG16 model. The project focuses on classifying papaya fruits into two ripeness stages: raw (unripe) and ripe. By utilizing transfer learning, the pre-trained VGG16 model is fine-tuned on a dataset of papaya images, achieving an accuracy of 97.75% and a validation loss of 1.34. The project addresses the limitations of manual ripeness detection methods, which are subjective, labor-intensive, and prone to human error, by providing an objective, efficient, and scalable solution. The system's workflow includes data collection, preprocessing, model training, evaluation, and deployment. The dataset, collected manually using mobile phone cameras, consists of images of papayas under various lighting and background conditions, ensuring diversity and robustness. The project demonstrates the potential of deep learning in agriculture, particularly in improving harvest timing, reducing food waste, and enhancing quality control in supply chains. Future enhancements include expanding the system to classify multiple fruits, incorporating additional ripeness stages, and integrating real-time detection capabilities. The system's success highlights the importance of leveraging advanced AI technologies to address real-world challenges in agriculture and food industries, paving the way for more sustainable and efficient practices