An Analytical Approach for Soil and Land Classification System using Image Processing


Land mapping and classification have piqued the interest of experts in recent decades for a variety of reasons. The increasing need for agricultural land and soil health analysis, as the health of the soil is vital for the healthy production of crops, are the reasons for a shift in the research community's focus.

One method for analyzing soil and land health is image classification. It's a complicated procedure that's influenced by a number of elements. The study of current researches, the difficulties they addressed, and their prospects was proposed in this work. The focus is on the analysis of a variety of innovative and effective classification processes and procedures. It has been attempted to investigate the elements that various approaches have addressed in order to increase classification accuracy.

The most essential factors in enhancing classification accuracy are making proper use of the quantity of characteristics available in remotely sensed data and picking the optimal classifier for the job. In recent years, knowledge-based classification or non-parametric classifiers such as decision tree classifiers and neural networks have grown in popularity for multisource data categorization. However, there is still need for more research in order to eliminate uncertainties in the enhancement of image classification mechanisms' accuracy.

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