ISSN No:2250-3676
   Email: ijesatj@gmail.com,   

Scholarly Peer Reviewed and Fully Referred Open Access Multidisciplinary Monthly Research Journal


    MACHINE LEARNING APPROACHES FOR SOIL TYPE CLASSIFICATION IN PRECISION AGRICULTURE

    P. Satish1, P. Yamini2, P. Akshaya3, K. Rupa4, Chinnari5

    Author

    ID: 1163

    DOI:

    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.

    How to Cite

    P. Satish1, P. Yamini2, P. Akshaya3, K. Rupa4, Chinnari5 , MACHINE LEARNING APPROACHES FOR SOIL TYPE CLASSIFICATION IN PRECISION AGRICULTURE , 2025, International Journal of Engineering Sciences and Advanced Technology, 25(6), Page 159-170, ISSN No: 2250-3676.

    DOI: