International Research Journal of Education and Technology

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.

1. Robustness to Environmental Variability: By training the model on diverse datasets, our framework improves resilience to variations in temperature, lighting, and fruit surface conditions, enhancing classification reliability in real-world agricultural settings.

1. Scalable and Customizable Architecture: The model's design allows for easy adaptation to different fruit types and ripeness criteria, offering flexibility for
2. various applications, from research to industrial use.

1. Improved Detection Accuracy: Through the integration of advanced techniques such as data augmentation and transfer learning, our approach achieves notable improvements in classification accuracy, even under challenging conditions where ripeness differences may be subtle.

Experimental results demonstrate that our CNN-based framework outperforms existing state-of-the-art methods in guava ripeness classification. This work represents a significant advancement in the application of deep learning for fruit quality assessment, providing a robust solution that enhances accuracy and efficiency, ultimately contributing to improved post-harvest management and decision-making in the agricultural industry.