Nanobot-Assisted Pollination for Sustainable Agriculture: A Review of Image Classification and Deep Learning Techniques With YOLO, SLAM, and MATLAB

The global decrease in native pollinators poses a substantial challenge to agricultural production and food security, particularly in malnutrition prone countries. There is a huge potential in nanobots which nanobots can autonomously explore complicated surroundings to perform targeted pollination by utilizing Convolutional Neural Network (CNN) and You Only Look Once (YOLO) for real-time flower recognition and categorization. A comprehensive study has been depicted in the article and shows how various approaches have utilized Simultaneous Localization and Mapping (SLAM) to improve spatial awareness, allowing the nanobots to adjust directions on the go. The study also presents an extensive literature survey shows the most productive countries that have contributed to this field of study. Furthermore, this article also presents a broad array of studies that have used deep learning approaches to enhance sustainable agriculture methods. Additionally, the study contributes a comparative study of the results that have been showcased in the literature. A performance metric comparison was performed among the deep learning model utilized and for various optimizers used along with the Deep Learning models. The article concludes with potential limitations, challenges and opportunities for advancing sustainable agriculture using deep learning models.