An Integrated Framework for Smart Monitoring of Orange Orchard

Oranges hold significant sway in the global food market, making the monitoring of their production a matter of utmost importance. Keeping track of the production procedure is the most important part of having a great yield of oranges. Through the integration of cloud computing, IoT, machine learning, and advanced image analysis techniques, we aim to streamline orange orchard management. Machine learning and computer vision have brought revolutionary changes to the agricultural sector, particularly in orchard monitoring. This paper introduces an integrated framework for orchard monitoring, focusing on real-time imaging of oranges, cloud-based storage, and the application of machine learning algorithms for automated monitoring and yield notifications to farmers. Additionally, we have conducted a comparative analysis of various machine learning models to evaluate their capacity to detect and count overlapped oranges within the dataset. Our proposed framework encompasses image capture, cloud storage, and machine learning analysis, providing farmers with invaluable insights into orange production. The comparative analysis delves into the merits and limitations of different machine learning models, including the K-means and Distance Least Square, Hough Transform, and YOLOv3, addressing the challenge of detecting overlapped oranges.