A REVIEW: SMART WEED DETECTION AND HEALTH MONITORING AGROBOT
Journal: Environment & Ecosystem Science (EES)
Author: Moiz Ur Rehman, Ali Hassan, Shahzaman Khan, Sohaib Khan
This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
As agriculture becomes increasingly important in ensuring food security for the world’s growing population, there has been a rise in the development of smart agricultural robots to optimize crop yield. One critical area where these robots can make a significant impact is in weed detection and health monitoring, which can have a significant impact on crop yield and quality. This review paper aims to examine the latest research in smart weed detection and health monitoring agrobots. The paper discusses several studies on autonomous agricultural robots that detect and remove weeds from fields using image processing, deep learning, and fuzzy logic-based classification techniques. In addition to weed detection and removal, the review paper also examines research on agrobots that monitor the health of crops. Moreover, the paper also discusses various techniques for path planning and control for autonomous agricultural vehicles. Finally, the review paper analyzes the role of single-board computers such as Raspberry Pi in agriculture. Overall, this review paper presents a comprehensive analysis of the latest research in smart weed detection and health monitoring agrobots. By examining the various techniques, methodologies, and algorithms employed by researchers, this paper offers valuable insights for future research and development in this field.