Non-Destructive Robotic Arm for Efficient and Economic Fruit Harvesting

Fruit ripeness detection and automated harvesting represent essential components of precision agriculture, yet manual methods remain labor-intensive, inconsistent, and inefficient. This paper introduces a smart agricultural solution that leverages edge computing and computer vision to automate ripeness detection and harvesting. The system employs an ESP32-CAM module to capture fruit images and performs real-time classification using HSV color space analysis. Once ripeness is detected, the data is transmitted via Wi-Fi to a NodeMCU microcontroller, which activates a robotic arm with a servo motor and soft gripper for precise harvesting. Ripeness data and harvesting events are logged in a centralized database, ensuring traceability and enhancing transparency. The system was evaluated using apples and mangoes, demonstrating high accuracy and adaptability across fruit types. By minimizing latency and reducing reliance on manual labor, the platform increases efficiency and consistency in the harvesting process. The integration of edge computing and automated actuation makes the system scalable, cost-effective, and well-suited for smart farming applications, advancing sustainability and productivity in modern agriculture.