Precision Harvesting: Utilizing Inception Neural Networks in Robotic Arms for Fruit Sorting

Fruit sorting is a crucial procedure in the food and agriculture sectors that frequently affects the shelf life and quality of products throughout the supply chain. This sorting has historically been done by hand, with human employees visually evaluating and classifying fruits as fresh or rotting. However, manual sorting has many disadvantages such as high classification quality variability brought on by human error, a slower rate of operation, health risks from repetitive tasks, and extended exposure to rotting produce. This study presents an automated fruit-sorting system that addresses the limitations of manual sorting, such as human error, slow speed, and health risks. The system employs Convolutional Neural Networks (CNNs) for accurate fruit classification, with Inception demonstrating superior performance among various algorithms evaluated. Based on visual characteristics like color and texture, the CNN model effectively distinguishes between fresh and rotten fruits. A robotic arm then physically separates the fruits, enhancing efficiency and consistency. This innovative approach offers significant advantages, including increased accuracy, higher throughput, reduced labor dependence, and improved scalability, making it a promising solution for large-scale fruit processing plants. This version specifically mentions the evaluation and selection of Inception as the most accurate algorithm for fruit classification.