Evaluation of digital soil mapping approach for predicting soil fertility parameters—a case study from Karnataka Plateau, India

Dharumarajan, S.; Lalitha, M.; Niranjana, K.V.; Hegde, Rajendra

doi:10.1007/s12517-022-09629-8

Cited by 15 publications

(6 citation statements)

References 46 publications

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“…The spatial distribution of TN is largely influenced by anthropogenic and natural factors, and there is spatial heterogeneity [16,40]. Therefore, the spatial prediction of TN cannot only use image band reflectance as the model independent variable, but needs to consider more environmental and anthropogenic factors, such as topographic factors [36], climatic factors [41], soil types [6], tillage practices [4,42], and crop types [43].…”

Section: Discussionmentioning

confidence: 99%

“…Machine learning techniques have the best performance in soil nutrient spatial inversion prediction [13], which can make up for the deficiencies of linear models and solve the complex nonlinear relationship between band reflectance and nutrient content and can well reflect the characteristics of nutrient spatial distribution in the study area. The models commonly used in soil nutrient spatial distribution prediction are neural networks (NN) [4,14], support vector machines (SVM) [15,16], random forest (RF) [3,17]. Models such as BPNN and SVM can solve the nonlinear problem well, which makes the prediction accuracy of the model higher and the nutrient spatial distribution information more accurate.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Prediction of Total Nitrogen in Soil Surface Layer Based on Machine Learning

Liu

Lei

et al. 2022

Sustainability

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In order to satisfy the basic requirements of sustainable agricultural development, it is important to understand the spatial distribution characteristics of soil total nitrogen (TN) content to better guide accurate fertilization to increase grain yield. To this end, this paper constructs three inversion models of partial least squares regression (PLSR), back propagation neural network (BPNN) and support vector machines (SVM) with remote sensing data to predict the TN content in Datong County, Xining City, Qinghai Province, China. The results showed that the average TN content was 1.864 g/kg, and the coefficient of variation (CV) was 30.596%. The prediction accuracy of the SVM model (R2 = 0.676, RMSE = 0.296) among the three inversion models was higher than that of the BPNN model (R2 = 0.560, RMSE = 0.305) and the PLSR model (R2 = 0.374, RMSE = 0.334). The model with the highest accuracy predicted the spatial distribution of TN, and TN content showed a spatial distribution trend which was high in the northwest and low in the southeast, and gradually decreased from north to south. This study provides reference basis and support for soil fertility evaluations and sustainable agricultural development.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Total Nitrogen in Soil Surface Layer Based on Machine Learning

Liu

Lei

et al. 2022

Sustainability

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“…The study evaluated digital soil mapping in predicting soil fertility parameters in the Karnataka Plateau, with the RFRK model outperforming others. A soil fertility index was calculated using an additive approach, aiding in nutrient management strategies [27].…”

Section: The Study Compared Extreme Learning Machine (Elm) With Multi...mentioning

confidence: 99%

Leveraging Machine Learning for Soil Fertility Prediction and Crop Management in Agriculture

Asif,

Wahid

2024

Preprint

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This study investigates how machine learning (ML) algorithms can be used in agriculture to forecast soil fertility and maximize crop yield. Machine learning (ML) models are created to predict soil nutrient levels, pH, and organic matter content across a range of geographical locations and land-use types with high accuracy by evaluating large datasets that include soil samples, environmental conditions, and agronomic methods. The research shows the advantages of nonlinear modeling approaches in capturing complex interactions inherent in agricultural systems through a comprehensive evaluation of several machine learning techniques, including ensemble methods like AdaBoost and Extra Tree Classifier. Furthermore, immediate insights and recommendations for improving agronomic decisions are made possible by the integration of real-time sensing technologies, such as proximate sensing, distant sensing, and Internet of Things (IoT) devices. Overall, this work highlights how machine learning (ML) can completely change crop management techniques and soil fertility prediction, enabling a more resilient and sustainable agriculture sector.

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“…Detailed maps based on high-resolution sampling can act as stepping stones for many conservations policies at the national and international levels. Various attempts have been made to construct soil maps using digital and conventional methods at the global level (Arrouays et al, 2020; Grunwald, 2009; Hengl et al, 2014, 2015; Hengl, Leenaars, et al, 2017; Poggio et al, 2021) national-level (Purushothaman et al, 2022; Reddy et al, 2021) and at the local level (Arora et al, 2021; Dharumarajan et al, 2020, 2021, 2022; Kalambukattu et al, 2018; Kaushal et al, 2021; Santra et al, 2017; Srinivasan et al, 2022). However, most of the maps are at low resolution levels.…”

Section: Introductionmentioning

confidence: 99%

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Elias,

Sharma,

Goel

2024

Preprint

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The availability of high throughput soil profile information is an important component in precision agriculture to perform efficient soil management for sustainable production. We collected 14 soil physiochemical features from Nagpur, Pune, and Haveri, representing target environments of safflower cultivation, and also from our experiment station at Delhi, at fine resolution and created graphical maps to depict the variability. Additionally, we evaluated the predictive ability of two statistical learning models, random forest (RF) and self-organizing maps (SOM) against multinomial regression models for correctly classifying the soil profile. Clustering was performed around the medoids produced from the dissimilarity matrices of these models using partitioning around medoids (PAM) model. The robustness, versatility, and predictive ability of models in correctly classifying the soil profile to clusters were then tested using cross-validation which was repeated 100 times. This study was performed using training data with proportionate size varying from 60 to 95%, and increasing the unit area of observation up to nine times (or decreasing the total number of observations up to a ninth). RF model was found to be the best performing with average prediction accuracy above 85% in all settings which reached close to 100% in some settings. The predictive ability of all the models was maintained even when only the most influencing six variables were used for classification. The optimal training population size for prediction was found to be 70 to 80%. Based on our study, it is recommended to i) collect fine resolution edaphic features from a marginal farm before crop season, ii) use RF or SOM model to identify the most influencing features distinguishing the soil samples iii) expand the area of sample collection, find values for the most influencing features, and use RF model to correctly predict the class to which the new set of the soil belongs to.

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Evaluation of digital soil mapping approach for predicting soil fertility parameters—a case study from Karnataka Plateau, India

Cited by 15 publications

References 46 publications

Spatial Prediction of Total Nitrogen in Soil Surface Layer Based on Machine Learning

Spatial Prediction of Total Nitrogen in Soil Surface Layer Based on Machine Learning

Leveraging Machine Learning for Soil Fertility Prediction and Crop Management in Agriculture

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

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