Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review
Abstract
:1. Introduction
- Examining the smart agriculture and digital soil management landscape in developing countries.
- Existing research literature on soil attributes, classifications, and key components in soil databases for soil fertility prediction.
- Identify and review the state-of-the-art smart soil system based on artificial intelligence models (machine learning and deep learning models).
- Overview of the current issues in development and deployment of soil information systems.
- Establishing a roadmap for future research to improve agricultural productivity with DSM and other digital innovation technologies through the development of a smart soil information system.
2. Soil Components and Properties
2.1. Soil Dataset
2.2. Soil Map
- S = soil classes or attributes
- f = function
- s = soil, other or previously measured properties of the soil at a point
- c = climate, climatic properties of the environment at a point
- o = organisms, including land cover and natural vegetation or fauna or human activity
- r = relief, topography, landscape attributes
- p = parent material, lithology
- a = age, the time factor
- n = spatial or geographic position.
2.3. Research Justification
3. Materials and Methods
3.1. Database Search Strategy and Eligibility Criteria
3.2. Review Strategy
- Results for (a): DSM, SPP, ML, deep learning, soil properties, soil nutrients, soil map, soil datasets and crop growth.
- Results of (b): smart soil, soil information system and soil fertility.
- Results for (c): smart farming, plant disease, crop disease, articles in different languages other than English.
- Result (d): a, b, c combined using AND OR.
3.3. Characteristics of Studies
3.4. Quality Assessment
3.5. Data Sources and Search Strategy
3.6. Inclusion and Exclusion Criteria
3.7. Data Extraction and Quality Assessments
4. The Impact of Soil Nutrients and Fertility on Crop Growth
4.1. Research on Soil Nutrients and Crop Yield in Developing Countries
4.2. DSM/ML Soil Prediction in Developing Countries: Challenges
- (a)
- Data scarcity: In many underdeveloped nations, soil data is scarce or nonexistent, making accurate digital soil maps and training machine learning models problematic. This occurs frequently owing to a scarcity of resources and funds for soil surveys and studies.
- (b)
- Low technical expertise: Poor countries may lack professionals with the technical abilities needed to produce and evaluate digital soil maps as well as developing machine learning models. This can make it challenging to effectively implement these technologies.
- (c)
- Restricted access to technology: Many underdeveloped countries may lack the requisite infrastructure or resources to facilitate the usage of digital soil maps and machine learning. This can involve a lack of internet connectivity, computer equipment, and access to software and data.
- (d)
- Inadequate governmental capacity: Poor countries may lack the institutional ability necessary to properly employ digital soil mapping and machine learning technology. These can include ineffective governance systems, insufficient financing for research and development, and a lack of coordination among various government agencies and stakeholders.
5. Soil Information System
6. Artificial Intelligence Models for Soil Properties Prediction
6.1. Data Quality and ML Model Considerations
- (a)
- Data gathering and preprocessing: This entails making sure that the soil types, geographic areas, and environmental conditions represented in the model training data are accurate. In order to understand soil nutrients, data must also be gathered through soil samples, lab testing, remote sensing, and historical records. The final step is data cleaning, which includes handling missing numbers, fixing errors, and removing outliers [117].
- (b)
- Feature engineering: In order to enhance the accuracy of soil nutrient level estimation, it is imperative to identify and extract relevant features from the collected data. The influence of environmental factors, including climate, rainfall, and cultivation of land, as well as the chemical, biological, and physical characteristics of soil, is potentially significant [118].
- (c)
- Integrate domain knowledge: In order to gain further insight into the determinants that impact the levels of nutrients present in the soil, it is recommended to consult with experts in the domain [119], including agricultural scientists or researchers specializing in soil science. Applying this data when constructing the models and determining which attributes to incorporate is essential.
- (d)
- Innovative modelling methods: Conducting research on state-of-the-art machine learning techniques [120] and advanced deep learning architectures is of great significance [121]. Furthermore, it is imperative to consider ensemble methodologies that employ an assemblage of models to enhance the accuracy of predictions.
- (e)
- Model testing and verification: It is imperative to assess the model capacity to extrapolate to new datasets through the application of rigorous evaluation methodologies. Furthermore, assessment criteria are examined and monitored to measure the precision of the models [122].
6.2. Considerations for Choice of ML Technique for Soil Nutrient Properties Prediction
7. Findings and Discussion
- (a)
- Resiliency to distortion: When compared to other algorithms, RF is less susceptible to noise and outliers, which might help it deliver precise forecasts even when working with unclear or missing soil data.
- (b)
- Managing massive data: Because RF can accommodate large datasets with many input features, it is well suited for forecasting soil qualities with several factors impacting their values, such as pH, moisture content, organic matter, and nutrient levels.
- (c)
- Features selection: RF automatically chooses the most significant features for making predictions, which can aid in identifying the main soil qualities and nutrients that are most important in determining soil fertility.
- (d)
- Overfitting minimization: Random forest employs numerous decision trees and aggregates their outputs, which can aid in the reduction of overfitting, a typical problem in machine learning in which models perform well on training data but fail to generalize to new data.
- (e)
- Random forest’s ensembling feature, in which it integrates many decision trees, aids in bias reduction and prediction accuracy by using the collective wisdom of multiple trees.
8. Conclusions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Symbol | Meaning | Units | SPT |
---|---|---|---|
N | Nitrogen | % | SN |
P | Phosphorus | mg | SN |
K | Potassium | cmol | SN |
Ca | Calcium | cmol | SN |
Mg | Magnesium | cmol | SN |
S | Sulphur | ppm | SN |
Fe | Iron | ppm | TE |
Mn | Manganese | ppm | TE |
Cu | Copper | ppm | TE |
Zn | Zinc | ppm | TE |
B | Boron | ppm | TE |
Mo | Molybdenum | ppm | TE |
ESP | Exchangeable sodium percentage | % | SN |
CEC | Cation exchange capacity | cmol | SN |
Reference | Problem Addressed | AI Methods | Metric | Accuracy | Dataset Types | Limitations |
---|---|---|---|---|---|---|
[50] | DSM to inform gully erosion mitigation measures | MNLR, CM | KC,, RMSE | 68% | Covariate and climate data, land type maps | Soil depth map not a good representation of reality (covariate layer map required) |
[51] | Assessment of the soil fertility status using DSM and ML | QRF, CM | , CCC, RMSE, MAE | High and average accuracy | Soil dataset (SOC, OM, Kech,, Pass, CEC, SumBas, BS) | Model accuracy was limited for some of the soil properties, such as N and Kech |
[52,53] | Improved machine learning models accuracy in DSM | CM, RM, ANFIS, EGB, ERT, ANN, SVR, MARS, KNN, GP | RMSE, MAE, , CCC, F-score | High accuracy | Clay, sand, CaCO3, SOC, SEC, pH, K, Ca + Mg, Na, SAR, EF, MWD | Uncertainty was observed in the predicted values. Small dataset used |
[54] | Prediction of soil depth using DSM | QRF, RK | RMSE, , CCC | 30% | Covariates dataset | Lower accuracy rate achieved due to the error in locating old coordinates |
[55] | Soil maps for a wide range of soil properties using ML | RF, QRF, CM, SVM | Bias, , RMSE | Best accuracy achieved with QRF | Gravel, clay, sand, density, pH, SOC and soil depths (0–200 cm) 0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm | Overestimation was observed for some probability values |
[56] | Review on DSM algorithms and covariates for SOC mapping | RK, MLR, RF, CM, NN, BRT, SVM, GWR | - | RF performed better than others | Environmental covariates, parent material, climate factor, organic activity, topography | Performance metrics or evaluation methods not reported |
[57] | Spatial prediction of soil aggregate using ML algorithms and environmental variables | RF, SVM, kNN, and ANN and ensemble modelling | RMSE, MAE, , and normalized RMSE | Ensemble achieved high accuracy for all soil targets | Soil properties, remote sensing data, legacy soil maps, and DEM derivatives | Lower accuracy achieved for SOC categories |
[58,59,60] | Prediction of SOC and soil total nitrogen using DSM and ML algorithms | RF, BRT, SVM and Bagged-CART | RMSE, MAE and | BRT model performed best in predicting SOC and STN | DEM derivatives, multi-temporal Sentinel data, environmental data | Investigation using other soil properties is required |
[61] | Predicting and mapping of SOC using ML algorithms | SVM, ANN, RF, XGBoost, CM, RT, DNN | RMSE, MAE, and CCC | DNN mapped SOC contents more accurately | Terrain attributes, remote sensing data, climatic data, categorical data | Further investigation on the dataset using hybrid algorithms is required |
[62] | Soil moisture prediction using multi-sensor data and ML algorithm | RFR, XGBoost, SVM, CBR and GA for feature selection | RMSE and | XGBR-GA hybrid model yielded the highest performance ( = 0. 891; RMSE = 0.875%) | DEM derivatives, Sentinel-1 and Sentinel-2 data. | Testing the framework in large-scale areas with various land-use characteristics is required |
[63] | Supervised maps for predicting soil moisture | Unsupervised SOM, supervised SOM, semi-Supervised SOM, and RF | , accuracy, and Cohen’s KC | Higher accuracy achieved with the SOM methods | Soil moisture and land cover dataset | RMSE and MAE factors are not considered in the performance evaluation |
[64] | Predictive mapping using semi-supervised ML | Decision trees, logistic regression (LR), SVMs and graph-based semi-supervised ML (GS-ML) | Mean accuracy (%), accuracy range (%), accuracy standard deviation (%) | GS-ML achieved higher accuracy | Environmental covariate data | Improvement is required for parameter setting, RMSE, and MAE evaluations are not considered |
[37] | ML for predicting soil classes in semi-arid landscapes | Multiple classifications and regression ML | Kappa analysis, Brier scores and confusion index | - | Environmental covariates | Model accuracy was obtained when there are few soil classes, limited dataset to investigate “rare” soil classes |
[65] | Mapping of soil water erosion using ML models | Weighted subspace random forest, Gaussian process and naive Bayes (NB) ML methods | Accuracy, Kappa index and probability of detection | - | Soil texture, land and climate dataset | The data collection and sampling of them were not on the same scale. Also, RMSE, and MAE factors are not considered in the performance evaluation |
[66] | Digital mapping of soil carbon fractions using ML | RF, SVM, CaRT, BaRT, BoRT, RK, OK | Mean, standard deviations, prediction error, and | RF achieved the best accuracy | Soil data (0–20 cm), carbon | Further investigation required on the use of more sophisticated predictors |
[67] | Multi-scale DSM with DL | DL-ANN, RF | DL achieved 4–7 % than RF | Silt, clay, ZC, SFP, DEM resolution | The model is not tested with some environmental data such as climate, lithology, or land cover | |
[68] | Semi-supervised DNN regression for spatial soil properties prediction | DNN, GA, SVR and regression methods | RMSE, MAE, , Bias, ratio of performance to inter-quartile distance | DNN achieved the highest accuracy | Hyperspectral remote sensing image data | Sensitive to the quality of the initial training dataset and model not tested with a large number of samples |
[69] | Assessment of landslide susceptibility using DL with semi-supervised learning | DNN, SVM and LR. | Accuracy, Kappa index, predictive rate curves (AUC), and information gain ratio (IGR) | DNN achieved higher accuracy with AUC of 0.898 | Land cover and soil data | The K-means algorithm was tested using fixed value and limitation by the accuracy of layers and sampling process observed |
Source | Solution | Soil Dataset |
---|---|---|
[97] | Prediction of clay soil expansion using ML models and meta-heuristic dichotomous ensemble classifier | Soil swelling and soil properties data. |
[98] | Predicting crop yield on a particular soil using IoT | Nutritional value of soil data. |
[99] | ML approach to simulate soil fluxes under cropping systems | Soil classification and temperature data. |
[100] | Predicting Africa soil properties using ML techniques | Soil sample measures, soil depth (topsoil or subsoil) and climate data. |
[101] | Soil analysis of micro-nutrients using ML and IoT | Soil micro-nutrient and soil pH data. |
[102] | Estimation of the moisture of vineyard soils from digital photography using ML. | Soil sample and photographic data. |
[103] | Prediction of soil shear strength parameters using ML algorithms | Soil properties and cone penetration test data. |
[104] | Analysis of ML methods for agricultural soil health management | Secondary data. |
[105] | Crops yield prediction based on mL models in West African countries | Climate, yields, pesticides and chemical data. |
Source | Application Name | Year | Functions |
---|---|---|---|
[106] | SQAPP | 2015 | Sustainability of SM and high productivity |
[107] | SoilWeb | 1999 | Instantaneous soil information |
[108] | AgriApp | 2014 | Crop advisory, soil testing and drone services |
[109] | LandPKS | 2020 | Soil health monitoring and land management |
[110] | Crop App Index | 2017 | Agricultural decision support tool |
[111] | MySoil Test Kit | Not Specified | Information to improve soil and plant health |
[112] | SIFSS | 2017 | Provides indication scores for soil types. |
[113] | Soil Test Pro | 2019 | Soil nutrient management system |
[114] | SoilScapes | Not Specified | Digital smart information system |
[115] | SoilInfo App | 2017 | Generate open soil data |
[116] | SoilCares | 2021 | Smart application for monitoring soil nutrients and soil fertility |
ML Technique | Strengths | Weaknesses |
---|---|---|
Support Vector Machine [132,133] | Effective in high-dimensional spaces, less prone to overfitting, versatile kernel functions, effective with small to medium datasets, insensitive to irrelevant features | Performs poorly with large or noisy data. Highly sensitive to hyparameter tuning |
k-Nearest Neighbours [134,135] | Simple, highly intuitive, non-parametric, flexible decision boundaries, considers the local structure of the data, can be effective with both linear and non-linear relationships, handles outliers relatively more efficiently | There is high computational complexity during prediction phase, distance metric selection may be ambiguous, sensitive to the curse of dimensionality, struggles with imbalance data, has storage issues during prediction |
Decision Trees [136,137] | Offers good explainability and interpretability, cognaissant to feature importance, handles non-linear relationships among features relatively well, good for mixed data (categorical + non-categorical), has low computational complexity, handles outliers well | Prone to overfitting, highly unstable, especially to a slight variation in the training set, makes locally optimal decisions without considering the global optimal structure, tends to favour features with a large number of categories or high cardinality, not well-suited for problems where classes are linearly separable, struggle to represent complex relationships that require global knowledge or long-range dependencies in the data |
Linear Regression [138,139] | Interpretable, simple, resource efficient, robust feature importance identification, often useful as a baseline model for comparison with more complex algorithms | Often assumes a linear relationship between the input features and the target variable, does not handle outliers efficiently, relatively limited predictive power, naive assumption of homoscedasticity, also sensitive to multicollinearity |
Logistic Regression [140,141,142] | Interpretability, efficiency, probabilistic problems, less prone to overfitting and allows for internal feature selection | Assumes linearity like the linear counterpart, handles limited complexity, cannot handle outliers, limited for binary classification, and can be afftected by imbalance dataset |
Artificial Neural Network [143,144,145] | Ability to learn complex patterns, flexible architecture, automatically learn relevant features, supports parallel processing and has high generalization power thereby reducing fitting problems | Requires large amount of data, has high computational complexity, they lack good interpretability because of their black-box nature, sensitive to hyperparameter tuning |
Naive Bayes [146] | Efficient with large datasets, scalable, robust to irrelavant features, effective with limited training sets, interpretable | Sensitive to skewness, does not capture complex relationships between features, highly sensitive to scaling problems |
Random Forest [147] | Known for high accuracy, handles outliers and noisy data, handles high cardinality, good with variable importance, resistant to overfitting | Lacks explainability, computationally expensive, requires good hyperparameter tunning for optimal performance, biased towards majority classes |
Gradient Boosting [148] | High predictive accuracy, high flexibility in handling mixed data types, provides insights into feature importance, handles outliers internally, handles missing data, can be parrallelized efficiently | Computationally expensive, has a potential problem of overfitting, difficult to interpret, relies heavuly on the order (or sequence) of the training data |
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Folorunso, O.; Ojo, O.; Busari, M.; Adebayo, M.; Joshua, A.; Folorunso, D.; Ugwunna, C.O.; Olabanjo, O.; Olabanjo, O. Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review. Big Data Cogn. Comput. 2023, 7, 113. https://doi.org/10.3390/bdcc7020113
Folorunso O, Ojo O, Busari M, Adebayo M, Joshua A, Folorunso D, Ugwunna CO, Olabanjo O, Olabanjo O. Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review. Big Data and Cognitive Computing. 2023; 7(2):113. https://doi.org/10.3390/bdcc7020113
Chicago/Turabian StyleFolorunso, Olusegun, Oluwafolake Ojo, Mutiu Busari, Muftau Adebayo, Adejumobi Joshua, Daniel Folorunso, Charles Okechukwu Ugwunna, Olufemi Olabanjo, and Olusola Olabanjo. 2023. "Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review" Big Data and Cognitive Computing 7, no. 2: 113. https://doi.org/10.3390/bdcc7020113
APA StyleFolorunso, O., Ojo, O., Busari, M., Adebayo, M., Joshua, A., Folorunso, D., Ugwunna, C. O., Olabanjo, O., & Olabanjo, O. (2023). Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review. Big Data and Cognitive Computing, 7(2), 113. https://doi.org/10.3390/bdcc7020113