Year: 2024
Type: article
Abstract: 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
Author: NIRMAL Kumar
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4409122007