Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods

Sirsat, Manisha Sanjay; Cernadas, E.; Fernández-Delgado, Manuel; Barro, Senén

doi:10.1016/j.compag.2018.08.003

Cited by 61 publications

(22 citation statements)

References 57 publications

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“…Another approach is the use of quantile regression (Koenker and Bassett Jr., 1978) to estimate the complete conditional distribution of the prediction. This method has been recently applied in some DSM studies (Vaysse and Lagacherie, 2017;Sirsat et al, 2018;Cao et al, 2019). Less common approaches are the use of the fuzzy k-means with the extragrades (Tranter et al, 2010) algorithm, which defines areas within the covariate space, with different levels of uncertainty, where a new observation (to be predicted) can be placed, and the use of Bayesian optimisation approaches (Snoek et al, 2015;Gal and Ghahramani, 2016).…”

Section: Uncertainty Assessmentmentioning

confidence: 99%

Machine learning and soil sciences: a review aided by machine learning tools

Padarian

Minasny

McBratney

2020

SOIL

220

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Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (latent Dirichlet allocation) to find patterns in a large collection of text corpora. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that (a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, (b) the reviewed publications can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, neural networks, SVM, support vector machines), spectroscopy, modelling (classes), crops, physical, and modelling (continuous), and (c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships. From these findings, we found research gaps, in particular, about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.

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Section: Uncertainty Assessmentmentioning

confidence: 99%

Machine learning and soil sciences: a review aided by machine learning tools

Padarian

Minasny

McBratney

2020

SOIL

220

View full text Add to dashboard Cite

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“…Usually, scientists, in their environmental studies, assess multiple ML models so as to find the one that maximizes the prediction accuracy for a specific phenomenon [14][15][16][17]. They use specific ML implementations (packages, methods) and try to estimate the best hyperparameters for their models that produce the most accurate results.…”

Section: Introductionmentioning

confidence: 99%

Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction

Tziachris¹,

Aschonitis²,

Chatzistathis³

et al. 2020

IJGI

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In the current paper we assess different machine learning (ML) models and hybrid geostatistical methods in the prediction of soil pH using digital elevation model derivates (environmental covariates) and co-located soil parameters (soil covariates). The study was located in the area of Grevena, Greece, where 266 disturbed soil samples were collected from randomly selected locations and analyzed in the laboratory of the Soil and Water Resources Institute. The different models that were assessed were random forests (RF), random forests kriging (RFK), gradient boosting (GB), gradient boosting kriging (GBK), neural networks (NN), and neural networks kriging (NNK) and finally, multiple linear regression (MLR), ordinary kriging (OK), and regression kriging (RK) that although they are not ML models, they were used for comparison reasons. Both the GB and RF models presented the best results in the study, with NN a close second. The introduction of OK to the ML models’ residuals did not have a major impact. Classical geostatistical or hybrid geostatistical methods without ML (OK, MLR, and RK) exhibited worse prediction accuracy compared to the models that included ML. Furthermore, different implementations (methods and packages) of the same ML models were also assessed. Regarding RF and GB, the different implementations that were applied (ranger-ranger, randomForest-rf, xgboost-xgbTree, xgboost-xgbDART) led to similar results, whereas in NN, the differences between the implementations used (nnet-nnet and nnet-avNNet) were more distinct. Finally, ML models tuned through a random search optimization method were compared with the same ML models with their default values. The results showed that the predictions were improved by the optimization process only where the ML algorithms demanded a large number of hyperparameters that needed tuning and there was a significant difference between the default values and the optimized ones, like in the case of GB and NN, but not in RF. In general, the current study concluded that although RF and GB presented approximately the same prediction accuracy, RF had more consistent results, regardless of different packages, different hyperparameter selection methods, or even the inclusion of OK in the ML models’ residuals.

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“…Song et al [68] proposed a novel model combining deep belief network with macroscopic cellular automata (MCA) approach to predict the soil moisture content over an irrigated cornfield. Sirsat et al [69] used almost all available regression methods to predict four key soil nutrients and fertility indices for soil organic carbon. Zambrano et al [70] predicted the reduction of drought-related agricultural productivity in Chile using rainfall estimates, and climate oscillation indices.…”

Section: G Smart Irrigationmentioning

confidence: 99%

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

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Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

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Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods

Cited by 61 publications

References 57 publications

Machine learning and soil sciences: a review aided by machine learning tools

Machine learning and soil sciences: a review aided by machine learning tools

Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

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