The advancements in precision agriculture are vital to support the increasing demand for global food supply. Precision spot spraying is a major step towards reducing chemical usage for pest and weed control in agriculture. A novel spot spraying algorithm that autonomously detects weeds and performs trajectory planning for the sprayer nozzle has been proposed. Furthermore, this research introduces a vision-based autonomous navigation system that operates through the detected crop row, effectively synchronizing with an autonomous spraying algorithm. This proposed system is characterized by its cost effectiveness that enable the autonomous spraying of herbicides onto detected weeds.

Exploring whether Enriched Category Theory could provide the foundation of an alternative approach to Machine Learning. This paper is the first to construct and motivate a Machine Learning algorithm solely with Enriched Category Theory. In order to supplement evidence that Category Theory can be used to motivate robust and explainable algorithms, it is shown that a series of reasonable assumptions about a dataset lead to the construction of the Nearest Neighbours Algorithm. In particular, as an extension of the original dataset using profunctors in the category of Lawvere metric spaces. This leads to a definition of an Enriched Nearest Neighbours Algorithm, which consequently also produces an enriched form of the Voronoi diagram. This paper is intended to be accessible without any knowledge of Category Theory

The Otto Group Product Classification Challenge made Kaggle history as our most popular competition ever. Alexander Guschin finished in 2nd place ahead of 3,845 other data scientists. In this blog, Alexander shares his stacking centered approach and explains why you should never underestimate the nearest neighbours algorithm. I have some theoretical understanding of machine learning thanks to my base institute (Moscow Institute of Physics and Technology) and our professor Konstantin Vorontsov, one of the top Russian machine learning specialists. As for my acquaintance with practical problems, another great Russian data scientist who once was Top-1 on Kaggle, Alexander D'yakonov, used to teach a course on practical machine learning every autumn which gave me very good basis. Kagglers may know this course as PZAD.

We continue our study of online prediction of the labelling of a graph. We show a fundamental limitation of Laplacian-based algorithms: if the graph has a large diameter thenthe number of mistakes made by such algorithms may be proportional to the square root of the number of vertices, even when tackling simple problems. We overcome this drawback by means of an efficient algorithm which achieves a logarithmic mistake bound. It is based on the notion of a spine, a path graph which provides a linear embedding of the original graph. In practice, graphs may exhibit cluster structure; thus in the last part, we present a modified algorithm which achieves the "best of both worlds": it performs well locally in the presence of cluster structure, and globally on large diameter graphs.