The Use of Artificial Intelligence for Estimating Soil Resistance to Penetration

Pereira, Tonismar dos Santos; Robaina, Adroaldo Dias; Peiter, Márcia Xavier; Torres, Rogério Ricalde; Bruning, Jhosefe

doi:10.1590/1809-4430-eng.agric.v38n1p142-148/2018

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…Various investigators have made attempts to develop relationships to estimate soil penetration resistance by taking into account its soil properties such as type of the soil, particle size distribution, bulk density, and moisture content etc. (Bayat et al, 2008;Gunaydin et al, 2010;Holguin et al, 2011;Abrougui et al, 2014;Silva et al, 2016;Rizaldi et al, 2018;Pereira et al, 2018;Hosseini et al, 2018). Collecting soil penetration resistance data from huge production fields is usually much tedious and time-consuming task.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Penetration Resistance with Three Different Artificial Neural Networking Methods

Ünal¹,

Kabaş²,

Sözer³

et al. 2022

RAR

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Soil compaction is one of the major problems of the agricultural sector affecting negatively the soil structure and impedes plant root growth. Soil penetration resistance should be measured from many points of the production land to determine the effects of plant growth problems. Collection of soil penetration values from huge lands is time-consuming, tiring, and tedious for researchers. Also, the number of measured points to what extent will be sufficient to evaluation on whole production area is not clear. To eliminate this ambiguity, soil penetration values of the unmeasured points should be estimated to evaluate the whole land. Artificial Neural Networks (ANN) are one of the most popular mathematical computing and modeling method used to estimate unknown data with the help of known data. In this study, we collected 1603 samples of geographical position and soil penetration value from 40 cm depth within the 20 ha field. From the 1603 values, 24% records were selected for testing and the remaining 76% records were used for training. Soil penetration values of the unmeasured points were estimated using Generalized Regression Neural Network (GRNN), Multi-Layer Perceptron (MLP) and Radial Bias Function (RBF) methods in MATLAB. In addition to mean squared error (MSE), root mean square error (RMSE) and mean absolute error (MAE) has been also used for evaluation of prediction accuracy on these methods. RBF results showed very good agreement between the predicted and the measured real values of soil resistance (MSE: 0.1608; RMSE: 0.3717; MAE: 0.3682).

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Section: Introductionmentioning

confidence: 99%

Prediction of Soil Penetration Resistance with Three Different Artificial Neural Networking Methods

Ünal¹,

Kabaş²,

Sözer³

et al. 2022

RAR

View full text Add to dashboard Cite

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Machine Learning and Data Analytics for Environmental Science: A Review, Prospects and Challenges

Maganathan¹,

Senthilkumar²,

Balakrishnan³

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Innovations in Machine Learning and Data Analytics can possibly affect numerous aspects of Environmental Science (ES). Data Analytics refers to a collection of data resources indicated in terms of variety, velocity, veracity and volume. Big data contributes to the ES arena in applications such as weather forecasting, energy sustainability and disaster management with the advent of techniques such as Remote Sensing, Information and Communication technologies. Though big data is used to accomplish data analysis and interpretation for ES, there are still requirements for efficient ways of data storage, processing and retrieval. Machine Learning and Deep Learning are the sub fields of artificial intelligence which deals with training the models to learn from data without being explicitly programmed. When Machine Learning and Deep Learning are combined together it is possible to unleash the supremacy of data analytics. These techniques show high prospective for process optimization, information-centric decision making and scientific discovery. Scientific developments like these will assist ES to make real time autonomous decisions by extracting useful insights from huge data. These advancements also aid in bridging the gap between the theoretical backgrounds on ES to practical implementation. The primary objective of this survey is to figure out the basic concepts of Machine Learning, Deep Learning, and Data Analytics and find the state-of-the-art applications in ES, and observe the impending benefits of information-centric investigation on ES.

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ARTIFICIAL NEURAL NETWORKS TO PREDICT EFFICIENCIES IN SEMI-MECHANIZED BEAN (Phaseolus vulgaris L.) HARVEST

Souza

Padilha²,

Arcoverde

et al. 2022

Eng. Agríc.

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The Use of Artificial Intelligence for Estimating Soil Resistance to Penetration

Cited by 9 publications

References 24 publications

Prediction of Soil Penetration Resistance with Three Different Artificial Neural Networking Methods

Prediction of Soil Penetration Resistance with Three Different Artificial Neural Networking Methods

Machine Learning and Data Analytics for Environmental Science: A Review, Prospects and Challenges

ARTIFICIAL NEURAL NETWORKS TO PREDICT EFFICIENCIES IN SEMI-MECHANIZED BEAN (Phaseolus vulgaris L.) HARVEST

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