Authors - Shriraj A. Patil, Chudaman D. Sukte, Jayesh R. Patil, Chinmay R. Mhaske, Mandar Dakhorkar, Manohar K. Kodmelwar Abstract - This paper presents a novel IoT-based fruit-picking system that integrates Reinforcement Learning (RL), Transfer Learning (TL), and Neuroevolution to address the inefficiencies of current robotic harvesting methods. As demand for efficient agricultural practices rises, traditional fruit-picking systems face significant challenges, including operational inefficiencies, fruit damage, and limited adaptability to diverse environments. Our proposed solution leverages RL to optimize picking strategies through adaptive learning, enhancing the robotic arm's efficiency over time. TL is employed to improve fruit recognition capabilities, utilizing pre-trained models for accurate ripeness detection, even with limited training data for specific fruit varieties. Additionally, Neuroevolution evolves control strategies for the robotic arm, enabling it to adapt to dynamic harvesting conditions. Comprehensive simulations demonstrate significant improvements in picking accuracy, efficiency, and adaptability compared to existing methods. The findings highlight the potential of integrating these AI models within IoT frameworks to revolutionize fruit harvesting, ultimately contributing to smarter farming practices and enhanced agricultural productivity. This research underscores the interdisciplinary nature of modern agriculture, combining advancements in AI, robotics, and IoT technologies to provide innovative solutions for the challenges facing the agricultural sector today.
