The field of agricultural research is witnessing a significant shift towards automation, with a focus on developing innovative robotic systems and machine learning algorithms to enhance efficiency and accuracy in various tasks. Recent developments indicate a strong emphasis on designing and integrating robotic systems that can perform tasks such as tissue sampling, yield estimation, and leaf manipulation autonomously. These systems often combine computer vision, sensor technologies, and machine learning to navigate and interact with their environment effectively. Noteworthy papers in this area include the development of a dual-arm robotic system for high-throughput tissue sampling from potato tubers, which achieved an average positional error of 1.84 mm and a success rate of 81.5% for core extraction and deposition. Another significant contribution is the proposal of a hybrid geometric-neural approach for autonomous leaf grasping, which achieved an 88.0% success rate in controlled environments and 84.7% in real greenhouse conditions.