A Comprehensive Review on Automatic Fruit Sorting and Grading Techniques with Emphasis on Weight-based Classification
Keywords:
Automatic sorting, Embedded systems, Fruit grading, Microcontroller systems, Post-harvest technology, Productivity enhancement, Quality control, Smart farming, Weight-based classificationAbstract
Fruit sorting and grading are critical processes in post-harvest management, directly influencing product quality, market value, and customer satisfaction. Traditionally, sorting has been performed manually, which is labor-intensive, inconsistent, and prone to error. With the advancement of automation, diverse techniques such as weight-based classification, image processing, multi-sensor systems, and AI-driven solutions have emerged to enhance accuracy, efficiency, and scalability. This review highlights recent developments in fruit sorting technologies, with a special emphasis on weight-based systems that employ load cells and microcontrollers for affordable and reliable classification. Vision-based approaches, radar techniques, and hybrid systems integrating weight, moisture, or color sensors are also analyzed for their strengths and limitations. Comparative insights from existing studies demonstrate improvements in precision, throughput, and adaptability, but challenges remain in cost-effectiveness, scalability, and integration for small-scale farmers. The review concludes that weight-based systems offer a simple yet effective foundation for automation, while advanced sensor fusion and AI methods hold promise for future applications. By bridging electronics, automation, and agriculture, automated fruit sorting systems represent a step toward smart farming, ensuring better quality control, productivity enhancement, and sustainable food processing practices.
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