Deep Learning Based Fruit Harvest and Quality Control System (Agripick)

In recent years, the integration of deep learning technologies in agriculture has shown significant potential for enhancing efficiency and productivity. This paper presents a novel fruit picking and quality analysis system that leverages deep learning techniques to automate the harvesting process and assess fruit quality in real-time. Utilizing a combination of image processing and deep learning, the system analyzes visual attributes such as color, texture, and shape to evaluate fruit quality. The proposed solution not only reduces labor costs and minimizes waste but also ensures that only high-quality produce reaches consumers. Experimental results demonstrate high accuracy rates in fruit detection and quality assessment, showcasing the effectiveness of deep learning in agricultural applications. This system represents a significant advancement toward smart farming, contributing to sustainable agricultural practices and improved supply chain efficiency.