Framework for Image Processing-Based Robotic Car for Agricultural Ploughing Using Ensembling Machine Learning Approach

The use of technologies such as robotics, image processing, and machine learning has the potential to bring about substantial shifts in agriculture. This is particularly true when it comes to ploughing, one of the agricultural practices that demands the most rigorous physical effort there is to be found. As a consequence of the continuous use of traditional agricultural practices, which are marked by a strong dependence on human effort as well as huge pieces of equipment, there are numerous worries about the long-term sustainability of both the economy and the environment. These practices are characterised by a high reliance on both human labour and large pieces of gear. These concerns are principally brought about by the fact that conventional farming practices substantially, and to varied degrees, depend on both the work of humans and considerable quantities of technology. In order to find solutions to these issues, the primary emphasis of our study has been placed on the development of an image-processing-based robotic automobile that has the capability of tilling agricultural land. Thanks to computer vision and machine learning algorithms, the self-driving robotic vehicle is able to travel the fields while simultaneously assessing the depth of the ploughing, as well as the pace and direction of the ploughing. Due to the fact that the vehicle is capable of driving itself, this is not an improbable scenario at all. It is essential to cut down on the quantity of manpower that is necessary, as well as the expenses of operations and the negative consequences that existing agricultural practices have on the surrounding environment. The findings of the trial indicate that the technology is able to accurately recognise crops, navigate around obstacles, and determine the appropriate depth at which to plough the soil. These findings offer light on the potential for this technology to raise agricultural production and sustainability, which in turn will promote both the development of the agricultural sector as well as the degree of global food security.