A Soil Science Rover: An Intelligent Agricultural Autonomous Ground Mobile Robot

The integration of artificial intelligence (AI) and computer vision in agricultural scenarios provides a significant advancement in crop monitoring, autonomous navigation, and disease detection. The following research proposed a system to improve agricultural automation incorporated with deep learning-based object detection models such as RCNN, ResNet50, and DenseNet121. Using computer vision models, this research aims to optimize the identification of potential obstacles and crops in farming environments. Moreover, this research justified the role of autonomous ground robots for navigation, path planning, and real-time decision making. The following study evaluates existing methodologies and presents an improved framework that combines deep learning architectures with robotic perception systems to enhance agricultural automation. Furthermore, a Soil Science Rover Test Module (SSRTM) has been proposed in…