CNN-BASED GRADING OF GUAVA RIPENESS USING THERMAL IMAGING

We plan to develop an automated system for grading guava ripeness using convolutional neural networks (CNNs) and thermal imaging, ensuring accurate and non-destructive assessment of fruit quality.This project focuses on the development of a robust deep learning-based framework with the primary objective of detecting and classifying guava ripeness stages, aiding in efficient sorting and post-harvest management.Additionally, we aim to explore model ensembling techniques to enhance classification accuracy and reliability while providing real-time grading capabilities for agricultural and commercial applications.Our architecture leverages the strengths of CNNs, incorporating several key enhancements:1. High Efficiency and Speed: The optimized CNN model ensures rapid classification performance, enabling real-time processing of thermal images without significant latency, essential for large-scale fruit sorting operations.