Smart Edge Computing Framework for Real-Time Brinjal Harvest Decision Optimization

Modernizing and mechanizing agriculture are vital to increasing productivity and meeting the growing global food demand. Timely harvesting decisions, traditionally based on farmers’ experience, are crucial for crop management. This study introduces the Brinjal Harvesting Decision System (BHDS), an automated, real-time framework designed to optimize harvesting decisions using a portable, low-power edge computing device. Unlike conventional object detection models, which require substantial pre-training and curated datasets, the BHDS integrates automated data acquisition and dynamic image quality assessment, enabling effective operation with minimal data input. Tested on diverse farm layouts, the BHDS achieved 95.53% accuracy in data collection and captured quality images within an average of 3 s, reducing both time and energy for dataset creation. The brinjal detection algorithm employs pixel-based methods, including background elimination, K-means clustering, and symmetry testing for precise identification. Implemented on a portable edge device and tested in actual farmland, the system demonstrated 79% segmentation accuracy, 87.48% detection precision, and an F1-score of 87.53%, with an average detection time of 3.5 s. The prediction algorithm identifies ready-to-harvest brinjals with 89.80% accuracy in just 0.029 s. Moreover, the system’s low energy consumption, operating for over 7 h on a 10,000 mAh power bank, demonstrates its practicality for agricultural edge applications. The BHDS provides an efficient, cost-effective solution for automating harvesting decisions, minimizing manual data processing, reducing computational overhead, and maintaining high precision and operational efficiency.