An Image Processing Techniques Used for Soil Moisture Inspection and Classification

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.