Supporting Harvest Optimization: Ripeness Detection System Using YOLOv11 and LoRaWAN

¹ Center for International Education, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam
² Faculty of Creative and Cultural Industries, University of Portsmouth, Portsmouth, United Kingdom.

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

The requirement of accurately determining the levels of ripeness of fruits has always been an important issue when it comes to maximizing the efficiency of fruit harvesting. However, current methods for determining the ripeness level of fruits are highly dependent on the experience of farmers, leading to inconsistent post-harvest quality. This study presents an automated four-level ripeness detection system that integrates deep learning and IoT technology. The system uses YOLOv11 to classify fruits by color and characteristics in real time and applies LoRaWAN transmission technology to transmit image data from farm sensors to a central processing unit. FRiMan system has been tested in a pilot field and has achieved very positive results, also demonstrating the accuracy and effectiveness of the system in classifying the ripeness of fruits even under complex environmental conditions. This method provides a cost-effective solution and ensures that the equipment runs at low energy levels, suitable for agriculture, but still ensures accuracy while optimizing efficiency during harvesting and reducing post-harvest losses.