Abstract:
To address the growing demand for sustainable agriculture practices, new technologies to
boost crop productivity and soil health must be developed. In this research, we propose
designing and building an agricultural rover capable of autonomous vegetable harvesting and
soil analysis utilizing cutting-edge deep learning algorithms (YOLOv5). The precision and
recall score of the model was 0.8518% and 0.7624% respectively. The rover uses robotics,
computer vision, and soil sensing technology to perform accurate and efficient agricultural
tasks. We go over the rover’s hardware and software, as well as the soil analysis system and
the tomato ripeness detection system using deep learning models. Field experiments indicate
that this agricultural rover is effective and promising for improving crop management and soil
monitoring in modern agriculture, hence achieving the UN’s SDG 2 Zero Hunger goals.
