TY - GEN
T1 - Ready or Not? A Robot-Assisted Crop Harvest Solution in Smart Agriculture Contexts
AU - Nguyen, Thai Thao
AU - Parron, Jesse
AU - Obidat, Omar
AU - Tuininga, Amy R.
AU - Wang, Weitian
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Collaborative robotics
KW - artificial intelligence
KW - computer vision
KW - image processing
KW - smart agriculture
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85169418510&partnerID=8YFLogxK
U2 - 10.1109/SMARTCOMP58114.2023.00088
DO - 10.1109/SMARTCOMP58114.2023.00088
M3 - Conference contribution
AN - SCOPUS:85169418510
T3 - Proceedings - 2023 IEEE International Conference on Smart Computing, SMARTCOMP 2023
SP - 373
EP - 378
BT - Proceedings - 2023 IEEE International Conference on Smart Computing, SMARTCOMP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Smart Computing, SMARTCOMP 2023
Y2 - 26 June 2023 through 29 June 2023
ER -