Affiliation:
1. Department of Computer Science, University of Engineering and Technology, Lahore 39161, Pakistan
2. Department of Computer Engineering, University of Engineering and Technology, Lahore 39161, Pakistan
3. Department of Computer Science, New Campus, University of Engineering and Technology, Lahore 39161, Pakistan
4. Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa
5. College of Computer Science and Engineering, University of Hail, Hail 55476, Saudi Arabia
Abstract
Soil, a significant natural resource, plays a crucial role in supporting various ecosystems and serves as the foundation of Pakistan’s economy due to its primary use in agriculture. Hence, timely monitoring of soil type and salinity is essential. However, traditional methods for identifying soil types and detecting salinity are time-consuming, requiring expert intervention and extensive laboratory experiments. The objective of this study is to propose a model that leverages MODIS Terra data to identify soil types and detect soil salinity. To achieve this, 195 soil samples were collected from Lahore, Kot Addu, and Kohat, dating from October 2022 to November 2022. Simultaneously, spectral data of the same regions were obtained to spatially map soil types and salinity of bare land. The spectral reflectance of band values, salinity indices, and vegetation indices were utilized to classify the soil types and predict soil salinity. To perform the classification and regression tasks, the study employed three popular techniques in the research community: Random Forest (RF), Ada Boost (AB), and Gradient Boosting (GB), along with Decision Tree (DT), K-Nearest Neighbor (KNN), and Extra Tree (ET). A 70–30 test train validation split was used for the implementation of these techniques. The efficacy of the multi-class classification models for soil types was evaluated using accuracy, precision, recall, and f1-score. On the other hand, the regression models’ performances were evaluated and compared using R-squared (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The results demonstrated that Random Forest outperformed other methods for both predicting soil types (accuracy = 65.38, precision = 0.60, recall = 0.57, and f1-score = 0.57) and predicting salinity (R2 = 0.90, MAE = 0.56, MSE = 0.98, RMSE = 0.97). Finally, the study designed a web portal to enable real-time prediction of soil types and salinity using these models. This web portal can be utilized by farmers and decision-makers to make informed decisions regarding soil, crop cultivation, and agricultural planning.
Funder
Higher Education Commission of Pakistan
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference79 articles.
1. Uehara, G., and Ikawa, H. (2000). Plant Nutrient Management in Hawaii’s Soils, Approaches for Tropical and Subtropical Agriculture, College of Tropical Agriculture and Human Resources, University of Hawai’i at Manoa.
2. A soil science renaissance;Hartemink;Geoderma,2008
3. Digital mapping of soil texture classes using Random Forest classification algorithm;Dharumarajan;Soil Use Manag.,2022
4. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon;Castaldi;Remote Sens. Environ.,2016
5. Pedotransfer functions for predicting soil hydraulic properties in semi-arid regions of Karnataka Plateau, India;Dharumarajan;Curr. Sci.,2019