Land suitability evaluation using image processing based on determination of soil texture-structure and soil features

Land evaluation is the process of land performance predictions over time based on land uses and soil features. Traditional methods in determining soil features are proved to be time-consuming and costly. Therefore, in order to overcome these limitations, a simpler automated method using the image segmentation was developed in this study. The method was designed by integrating dynamic region merging and genetic algorithm. An area index was calculated for each soil profile using the automated method. It was used to present the amount of soil coarse particles and thereupon to determine the rating value of text-structure. Using the method, the mean intersection over union of above 0.7 was obtained for detecting the coarse particles which confirms its suitability. Data analysis showed that (a) compared to the Storie-land index (R-2 = 0.71), the Square root-land index was more correlated to the harvest index (R-2 = 0.73), and (b) comparing to manual methods, not only the automated text-structure had a higher correlation with the harvest index (R-2 = 0.64) but also it decreased the determination time (>3.75 times). Furthermore, among the models developed by response surface methodology for estimation of soil features, the developed model for estimation of soil lime showed the highest accuracy (R-2 = 0.89). In conclusion, since the developed method is more accurate, more economic and faster than the usual manual methods, it can be widely used in land suitability evaluation.