Vision-Based Tactile Sensors in Precision Agriculture: Deep Learning Approaches, Applications, and Limitations

Abstract

The integration of artificial intelligence with sensor technologies has revolutionized precision agriculture, offering unprecedented opportunities for enhancing crop management and productivity. This review focuses on the latest advancements in vision-based tactile sensors, a technology at the forefront of this transformation. By combining tactile data with vision-based techniques, these sensors provide a more comprehensive understanding of the agricultural environment. We investigate thoroughly the role of deep learning approaches in refining the functionality of these sensors, highlighting their potential to significantly improve the accuracy and efficiency of agricultural operations. The paper also explores the importance of specialized datasets in training deep neural networks for vision-based tactile applications, assessing the current landscape and identifying gaps in the available data. Through a thorough examination of the current state of the art, this review paper aims to shed light on the potential of AI-driven tactile sensing in precision agriculture and outline future research directions to further advance this field.