Abstract :

Soil Type Classification Plays A Vital Role In Precision Agriculture, Enabling Optimized Crop Management And Maximizing Productivity Through Informed Decision-making. Traditional Methods—such As Manual Soil Sampling And Laboratory Analysis—are Often Labor-intensive, Costly, And Limited In Spatial And Temporal Resolution, Making Them Insufficient For Capturing The Dynamic Variability Of Soils Across Agricultural Fields. These Conventional Approaches Can Also Introduce Human Error And May Overlook Subtle Yet Critical Differences In Soil Properties. To Overcome These Limitations, This Study Proposes A Machine Learning-based System For Soil Type Classification Using Image Data. By Applying Supervised Learning Algorithms To Extract And Learn Discriminative Features From Soil Images, The System Automates And Enhances Classification Accuracy. This Enables Effective Identification Of Soil Types Such As Black Soil, Cinder Soil, Laterite Soil, Peat Soil, And Yellow Soil. The Integration Of Machine Learning With Advanced Imaging Supports Precision Agriculture By Improving Soil Management, Optimizing Resource Use, And Minimizing Environmental Impact Through Site-specific Practices. Keywords: Soil Classification, Precision Agriculture, Machine Learning, Image Analysis, Resource Optimization

Published:

09-6-2025

Issue:

Vol. 25 No. 6 (2025)

Page Nos:

159-170

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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