Transforming Agriculture with Advanced Robotic Decision Systems via Deep Recurrent Learning

• Published in: Expert systems with applications Vol. 259; p. 125123
• Main Authors: Hu, Fengjun, Wu, Fan, Gu, Hanjie, Abbas, Ghulam, Alanazi, Meshari D., Othmen, Salwa, Wang, Jingming, Zhang, Tianrong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2025
• Subjects: Decision-Making; Deep Recurrent Learning; Robotics; Smart Farming; Deep Recurrent Learning; Smart Farming; Decision-Making; Robotics
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.125123

Global farming methods have improved owing to supply demand, human resource shortages, and seasonal fluctuations. Integrating automated mechanics, smart controllers, and artificial intelligence has become imperative to tackle these problems. Through self-learning and wise decision-making, this integration speeds up processing and encourages sustainability. To help modern farming techniques become more efficient and environmentally friendly, this paper presents a Pliant Decision System (PDS) specifically made for Farming Robots (FRs). The FRs are designed to be time-synchronized and multi-operational, which allows for guided agricultural practices that provide sustainable results. The robotic operations are planned to finish the agricultural processes with individual sustainability verification once they are first classified according to traditional techniques. Two layers of deep recurrent learning are used to evaluate sustainability. Verifying individual completion and sustainability via timely execution and cumulative effect is the primary emphasis of the first layer. In the second layer, the seasonal consistency of production improvements is compared using dual analyses. By training the learning model to synchronize and complete robotic activities promptly, people can execute judgments and synchronize successive agricultural operations throughout the year. The proposed method outwits the existing methods by leveraging sustainable factors by 8.24%, completion by 11.1%, promptness by 7.79%, synchronization by 6.75%, and reducing decision time by 12.07% for the varying process times.