Deep Learning Model to Enhance Precision Agriculture using Superpixel

In recent years, superpixels have become increasingly popular in computer vision applications, although it's not always clear what makes an effective superpixel algorithm. We empirically analyze Simple Linear Iterative Clustering (SLIC) superpixel algorithm for its ability to stick to a picture border, performance, memory efficiency, and impact on classification results in order to better understand the advantages and risks of current solutions. We then describe Simple Linear Iterative Clustering (SLIC), a new superpixel algorithm that uses a k-means clustering strategy to efficiently construct superpixels. Simple Linear Iterative Clustering (SLIC) sticks to bounds as well as or better than prior approaches, despite its simplicity. It is also quicker and more computationally efficient, increases segmentation efficiency, and is simple to extend to supervoxel generation. Agriculture is a topic that is devoid of deep network visualization due to the immense complexities that comes with it. The Superpixel technique used in this paper aids us in resolving the problem, which is related to the fault. They can hold more data than pixels. Because pixels belonging to the same superpixel have similar visual qualities, superpixels have a visual perception. They give a simple and compact visual representation that can be incredibly useful for computationally intensive problems.