Image-based direct geo-referencing of farm boundaries for autonomous farming vehicles using computer vision
Agriculture is undergoing a transformative technological revolution that has led to significant improvements in automation and efficiency. Although contemporary satellite imagery is limited in resolution and update frequency, it presents inherent challenges in the determination of farm boundaries. A novel image-based methodology has been developed in response to these limitations, which is poised to redefine precision agriculture by enabling accurate geo-referencing of agricultural boundaries within the context of autonomous vehicles. Through the integration of onboard cameras and GPS data into the very fabric of autonomous farming vehicles, this approach allows for the detection and geo-referencing of farm boundaries at an acceptable level of accuracy in comparison to conventional satellite imagery. Upon completion of our research, a comprehensive framework is developed, ranging from the acquisition of images to the intricate boundary detection process and culminating with geo-referencing. By utilizing this holistic methodology, farming operations are not only simplified but the manual labor typically associated with these activities is significantly reduced, leading to a significant improvement in operational efficiency. Among the most important components of our research is the meticulous examination of single-stripe boundary regions, iterative boundary detection, and the crucial process of outlier elimination and data refinement employed to detect farm boundaries. By combining these methods, farm boundaries within images can be delineated very precisely. The 3D position estimation of objects is then carried out over long distances with an impressive level of accuracy. This accuracy is inextricably related to how precisely the calibration procedures were carried out. The Haversine Formula is used in the geo-referencing component of our methodology to translate the calculated 3D coordinates of farm boundaries into the universally understandable language of GPS coordinates. Google Map Plotter is then used to visually represent this transformative step, successfully bridging the gap between intangible real-world contexts and abstract numerical data. In conclusion, this research represents a big advancement in precision agriculture. By improving accuracy and operational efficiency, the image-based approach introduced here offers transformative benefits to the agriculture industry. Future research opportunities aim to further enhance precision agriculture, solidifying the role of autonomous farming vehicles as pioneers of farming technology.
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Faculty of Graduate Studies and Research, University of Regina