Comparing machine learning algorithms for predicting and digitally mapping surface soil available phosphorous: a case study from southwestern Iran

IF 5.4 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Precision Agriculture Pub Date : 2023-12-27 DOI:10.1007/s11119-023-10099-5

Saeid Hojati, Asim Biswas, Mojtaba Norouzi Masir

{"title":"Comparing machine learning algorithms for predicting and digitally mapping surface soil available phosphorous: a case study from southwestern Iran","authors":"Saeid Hojati, Asim Biswas, Mojtaba Norouzi Masir","doi":"10.1007/s11119-023-10099-5","DOIUrl":null,"url":null,"abstract":"In developing countries like Iran, where information is scarce, understanding the spatial variability of soil available phosphorous (SAP), one of the three major nutrients, is crucial for effective agricultural ecosystem management. This study aimed to predict and digitally map the spatial distribution and related uncertainty of SAP while also assessing the impact of environmental factors on SAP variability in the topsoils. A study area from northern Khuzestan province, Iran was selected as case study area. Three machine learning (ML) models, namely, Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR), were used to develop predictive relationship between surface soil (0–10 cm) SAP content and environmental covariates derived from a digital elevation model and Landsat 8 images. A total of 250 topsoil samples were collected following the conditioned Latin Hypercube Sampling (cLHS) approach and several soil properties were measured in the laboratory. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Lin’s Concordance Correlation Coefficient (LCCC) were used to determine the accuracy of models. The findings indicated that the RF algorithm demonstrated the most favorable performance, with a mean absolute error (MAE) of 0.85 mg SAP kg−1, the lowest root mean square error (RMSE) of 0.99 mg SAP kg−1, and the highest linear correlation coefficient (LCCC) values of 0.96. This suggests that the RF algorithm had the least tendency to overestimate or underestimate SAP contents compared to other methods. Consequently, the RF algorithm was selected as the optimal choice. Predictive ML models were employed to digitally map SAP contents within the region. Spatial patterns of SAP contents showed an increasing gradient from west to east. The spatial variability information provides a basis for developing sustainable production system in the area.","PeriodicalId":20423,"journal":{"name":"Precision Agriculture","volume":"3 1","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Precision Agriculture","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s11119-023-10099-5","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

In developing countries like Iran, where information is scarce, understanding the spatial variability of soil available phosphorous (SAP), one of the three major nutrients, is crucial for effective agricultural ecosystem management. This study aimed to predict and digitally map the spatial distribution and related uncertainty of SAP while also assessing the impact of environmental factors on SAP variability in the topsoils. A study area from northern Khuzestan province, Iran was selected as case study area. Three machine learning (ML) models, namely, Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR), were used to develop predictive relationship between surface soil (0–10 cm) SAP content and environmental covariates derived from a digital elevation model and Landsat 8 images. A total of 250 topsoil samples were collected following the conditioned Latin Hypercube Sampling (cLHS) approach and several soil properties were measured in the laboratory. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Lin’s Concordance Correlation Coefficient (LCCC) were used to determine the accuracy of models. The findings indicated that the RF algorithm demonstrated the most favorable performance, with a mean absolute error (MAE) of 0.85 mg SAP kg⁻¹, the lowest root mean square error (RMSE) of 0.99 mg SAP kg⁻¹, and the highest linear correlation coefficient (LCCC) values of 0.96. This suggests that the RF algorithm had the least tendency to overestimate or underestimate SAP contents compared to other methods. Consequently, the RF algorithm was selected as the optimal choice. Predictive ML models were employed to digitally map SAP contents within the region. Spatial patterns of SAP contents showed an increasing gradient from west to east. The spatial variability information provides a basis for developing sustainable production system in the area.

Abstract Image

查看原文本刊更多论文

地表土壤可利用磷的预测和数字化绘图的机器学习算法比较：伊朗西南部的一项案例研究

在伊朗等信息匮乏的发展中国家，了解三大营养元素之一的土壤可利用磷（SAP）的空间变化对于有效的农业生态系统管理至关重要。本研究旨在预测和数字化绘制 SAP 的空间分布和相关不确定性，同时评估环境因素对表层土壤中 SAP 变化的影响。研究选取了伊朗胡齐斯坦省北部的一个研究区域作为案例研究区。研究人员使用了三种机器学习（ML）模型，即随机森林（RF）、人工神经网络（ANN）和支持向量回归（SVR），来建立表层土壤（0-10 厘米）SAP 含量与数字高程模型和 Landsat 8 图像中的环境协变量之间的预测关系。采用条件拉丁超立方取样法（cLHS）共采集了 250 个表层土壤样本，并在实验室测量了多个土壤特性。采用平均绝对误差 (MAE)、均方根误差 (RMSE) 和林氏协和相关系数 (LCCC) 来确定模型的准确性。研究结果表明，RF 算法表现最出色，其平均绝对误差（MAE）为 0.85 mg SAP kg-1，均方根误差（RMSE）最低，为 0.99 mg SAP kg-1，线性相关系数（LCCC）最高，为 0.96。这表明，与其他方法相比，射频算法高估或低估 SAP 含量的倾向最小。因此，射频算法被选为最佳选择。采用预测性 ML 模型对区域内的 SAP 含量进行了数字化测绘。SAP 含量的空间模式呈现出由西向东递增的梯度。空间变化信息为该地区发展可持续生产系统提供了依据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Precision Agriculture 农林科学-农业综合

CiteScore

12.30

自引率

8.10%

发文量

103

审稿时长

>24 weeks

期刊介绍： Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.