Soil classification using machine learning.

A topic that evokes a large base for assorted studies, across numerous countries is soil classification. Recently, the world recorded an exorbitant rise in human population, the denouement is a higher demand for food and shelter. Both these factors directly demand exceptional study in geo technology, if not, a more reliable and faster method to do so. Conventional strategies taken by farmers are insufficient to meet the skyrocketing demands, and as a result, they must impede soil cultivation. This leads to the requirement to remain cognizant of the optimum soil type for a certain crop to maximize agricultural production. Several laboratory and field methods for classifying soil exist, however, these have constraints namely time and labour sine qua non. A need for a software-based soil categorization modus operandi that will assist humankind from a range of engineers to farmers, in reducing the time deviation is sought. This paper discusses apropos software, that allows soil classification to be achieved more efficiently. The functioning includes image processing and Computer vision-based soil classification methodologies, embodying the customary image assessing algorithms based on parameters, namely texture, colour, and particle size. [ABSTRACT FROM AUTHOR]