Advancing Agriculture with AI-Powered Robotic Harvesting Systems for Legume Crops

Background: The integration of Artificial Intelligence (AI) in agricultural practices has witnessed substantial advancements, with a focus on enhancing efficiency and sustainability. This research explores the application of AI-powered robotic harvesting systems for legume crops, aiming to revolutionize traditional harvesting methods. By leveraging machine learning algorithms and robotic technology, this study investigates the feasibility and performance of such systems in terms of precision, speed and resource optimization. Methods: This research focussed on creating and implementing a robotic harvesting system that applies artificial intelligence to precisely identify and harvest legume crops. The system’s design relies on a combination of robotic technology, computer vision and machine learning algorithms to achieve optimal performance. In this work, a 4-layer CNN model is used to detect dandelion and soybean. Result: The findings provide valuable insights into the potential benefits and challenges associated with the adoption of AI in legume crop harvesting, contributing to the ongoing discourse on sustainable agriculture. The 4-layer CNN model shows a good overall accuracy of 99.71%. Confusion matrix and classification report are presented for evaluation of the model.