INTELLIGENT FRUIT SORTING, SEGREGATION, AND QUALITY CONTROL FOR SMART FARMING SYSTEMS
Abstract
Fruits are a mainstay of a healthy diet. They keep our bodies healthy because they contain minerals, vitamins, fiber, and water. Manpower is required for the segregation of the fruits to maintain their quality. A lot of time is wasted on segregating fruits to maintain quality. Due to the poor quality of fruits, farmers are facing a huge loss in their agricultural fields. Automation enhances the quality of the fruits and speeds up the segregation process by ensuring accuracy and efficiency. Many Algorithms have been developed by researchers for the segregation of fruits. The proposed Deep learning model YOLOv11 will segregate (Healthy or Rotten) the fruits into their specific classes, ensuring the quality by processing the images of the fruits, gaining validation Accuracy of 97.91%. This study fills the gap between agriculture and technology. It represents the potential of AI in food quality inspection processes.
Key points: Fruits Detection, Classification, Segregation, YOLO (You Only Look Once), Deep Learning, Computer Vision.
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