As robotics and artificial intelligence (AI) technologies have become increasingly relevant over the past couple of years, they will inevitably be key components for industries of all aspects which continue to expand to technological solutions. Particularly, the agricultural industry has progressed to using such means to minimize human involvement and reduce tasks that are time-consuming and costly. Motivated by this, we developed a robot-Assisted crop maturity recognition and harvest system to accurately classify and detect the stages of ripeness the crops are in-ripe, medium ripe, and not ripe. Our proposed approach integrates computer vision, image processing, collaborative robotics, and a subcategory of artificial intelligence-transfer learning. The transfer learning-based model is trained to classify and recognize the crop in its maturity stages and locate the crop during real-Time detection. Experimental results and analysis in real-world robot-Assisted smart agriculture environments successfully demonstrated crop ripeness recognition accuracy, proving transfer learning could be utilized to effectively improve the efficiency and productivity of harvesting processes in the agricultural industry. The future work of this study is also discussed.