An edge computing wireless sensor network for diagnosing orange fruit disease

Abstract

[EN] This study introduces an innovative Edge Computing Wireless Sensor Network and Designing a new algorithm for diagnosing orange fruit diseases. The network combines Raspberry Pi using wireless technologies like Zigbee and LoRa with Wireless Mesh Routers using Wireless Technologies like LoRa and Cellular technologies. By using a new system that includes a YOLOv8 model and an image processing algorithm that detects the color spectrum of the diseased part of the fruit, it is possible to quickly identify certain diseases, such as canker, black spot, and melanosis. The system achieves a high accuracy of 92.2% in disease detection. This cost-effective and efficient solution offers farmers a practical tool for early disease detection, enabling timely interventions to protect crops and improve overall agricultural outcomes. In this study, in connection with the proposed algorithm, 97 images of diseased orange fruit, including Canker, melanosis, and black spot, as well as healthy oranges have been tested. It has also been tested in an orange orchard. The proposed new model successfully identified orange black spot disease with 30 correct detections out of 32 images and 2 errors, melanosis disease with 18 correct detections out of 21 images and 3 errors, canker disease with 9 correct detections out of 11 images and 2 errors, and 33 images of healthy oranges fruits with 100% accuracy. The Python codes for the proposed model and the dataset used in this study are available in a GitHub repository and accessible to the public.