Proceedings of the 4th International Conference on Innovation in Education, Science and Culture, ICIESC 2022, 11 October 2022, Medan, Indonesia

Research Article

An Image Processing Techniques Used for Soil Moisture Inspection and Classification

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  • @INPROCEEDINGS{10.4108/eai.11-10-2022.2325509,
        author={Mansur  AS and Herkules  Abdullah and Hermawan  Syahputra and Brahim  Benaissa and Fauziyah  Harahap},
        title={An Image Processing Techniques Used for Soil Moisture Inspection and Classification},
        proceedings={Proceedings of the 4th International Conference on Innovation in Education, Science and Culture, ICIESC 2022, 11 October 2022, Medan, Indonesia},
        publisher={EAI},
        proceedings_a={ICIESC},
        year={2022},
        month={12},
        keywords={images processing classification glcm svms soil moisture},
        doi={10.4108/eai.11-10-2022.2325509}
    }
    
  • Mansur AS
    Herkules Abdullah
    Hermawan Syahputra
    Brahim Benaissa
    Fauziyah Harahap
    Year: 2022
    An Image Processing Techniques Used for Soil Moisture Inspection and Classification
    ICIESC
    EAI
    DOI: 10.4108/eai.11-10-2022.2325509
Mansur AS1,*, Herkules Abdullah1, Hermawan Syahputra1, Brahim Benaissa2, Fauziyah Harahap1
  • 1: Department of Matematics, Faculty of Mathematic and Natural Science, Universitas Negeri Medan, Indonesia
  • 2: Toyota Technological Institute, Department of Mechanical Systems Engineering, Design Engineering Lab, Japan
*Contact email: asmansur@unimed.ac.id

Abstract

A soil inspection provides information on the soil's fertility, an important starting point for determining soil fertility. Therefore, soil quality determination is essential in agricultural systems before planting. Image processing techniques associated with the computer vision model are widely used today, having applications in many branches of agriculture, closely related to technologies used in precision farming. This research aims to created an accurate model in image processing approaches for checking and categorizing soil quality based on external data detection. The visible and invisible systems gathered using spectral technology were used to identify the exterior texture (computer vision). The Grey Level Co-occurrence Matrix (GLCM) approach was used to analyze picture texture, and then the Support Vector Machines (SVMs) method was used for classification. This study demonstrated that the model is an effective technique for evaluating soil moisture. Since the concealed texture features are not visible to the human eye, the experiment also shows that the invisible channels have promise in the classification model.