Real-time point localization on plants using feature-based soft margin SVM-PCA method

Abstract In the field of robotics, the integration of machine vision-commonly referred to as vision-based measurement-empowers robotic systems to transform visual information into digital data, thereby enabling a broad spectrum of innovative applications across diverse sectors, including precision agriculture. This paper presents a new real-time vision-based method designed for the recognition and precise localization of specific points on young plants. Identifying these key points is essential for many tasks intended to be automated using robotic systems. A particular task of an interest is the robotic coupling of the plant’s stem to a wooden stake using plastic clip, aka stem-stake coupling. In this task, a human or a robot attaches the plant to a stake at a specific point along the plant’s stem using a clip to support the plant during transportation or throughout its growth phase. We have developed a new real-time vision-based method to identify the clipping point. This method has been implemented and evaluated using a robotic system and real images of plants commonly cultivated in propagation facilities. The algorithm’s accuracy was assessed and compared with end-to-end deep learning approaches. The results demonstrate the effectiveness of our method and its superior performance over learning algorithms that are purely driven by data from input to output. Our approach enhances autonomous operations in precision agriculture and improves the accuracy of vision-based measurements in robotic systems.