Leaf disease detection and Soil classification in Machine Learning

Abstract

In engineering domains like geotechnics and petroleum engineering, the task of classifying intervals of observed series data such as signals-requires a sophisticated methodology that goes beyond traditional classification techniques. This complexity results from the requirement to respect the contiguity constraint while assigning classes to data intervals, a procedure that frequently calls for the expertise of a specialist. Experts depend on any accessible a priori knowledge in addition to the signals' observed size and trends. In a similar vein, food security is seriously threatened by the quick detection of agricultural diseases, especially in areas without the infrastructure required for such a diagnosis. But the development of sophisticated methods for classifying images based on leaves has shown promise in overcoming these obstacles.