A Hadoop Based Framework for Soil Parameters Prediction

Mezouari, Asmae El; Najib, Mehdi

doi:10.1109/sitis.2019.00111

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…We performed in our previous experimentations in [4] three machine learning algorithms, in particular, the extreme gradient boosting, the random forest, and the auto-regressive moving average for training the soil data set using various inputs features selected by the resampling method. In this paper, we trained the data set using also the deep ANN with the same resampling inputs.…”

Section: Methodsmentioning

confidence: 99%

“…XGBoost is implemented in multiple programming languages in parallel with improving parameters as necessary related to [14]. Another model used in this paper is the random forest, that are a combination of multiple tree predictors that provide autonomous predictions using equivalent input data distribution, and at the end of Table 1 An overview of predictive models and relevant features by Asmae El Mezouari and Mehdi Najib [4] References Forecasting methods Parameters [5] Support vector machines, bagging, random forest, and M5P regression trees…”

Section: Methodsmentioning

confidence: 99%

“…Fig. 8 XGBoost soil moisture prediction in 5 cm depth using only soil moisture parameter [4] layer of the 9 relevant features for the second sampling input (soil moisture, hour, day of the week, quarter, month, year, day of the year, day of the month, and the week of the year), with the activation function relu and using 100 hidden units for all experiments. Moreover, we added a hidden layer with 60 hidden units, with the activation function relu and an output layer for predicting the target feature (soil moisture).…”

Section: Test Periode S_m_5_predicɵonmentioning

confidence: 99%

“…Furthermore, it appears that the most difficult task of researchers who use machine learning is the selection of the best model, data samples, and the corresponding data sets. Hence to come up with these issues, it is required to test different machine learning algorithms and training models with various data samples to determine the best one for an efficient prediction with the highest accuracy that is assumed by our proposed framework in our previous work in [4], in which we computed three forecasting methods specifically the ARIMA, the XGBoost, and the random forest.…”

Section: Introductionmentioning

confidence: 99%

“…Fig 9. Random forests soil moisture prediction in 5 cm depth using soil moisture and temperature parameters[4] …”

mentioning

confidence: 99%

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Hadoop–Spark Framework for Machine Learning-Based Smart Irrigation Planning

2021

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Up-to-date, given the expanding increase of the population and the development of human daily lifestyles, the expenditure of freshwater resources increments progressively. It appears that there is a need to optimize at least the consumption of fresh water in agriculture. For this reason, novel various irrigation technologies have been deployed in this context like drip irrigation, flood irrigation, and decision support systems to come up with the constraints of climate changes that decrease the water availability but it is still limited. Therefore, the majority of researchers are working until today on automating the irrigation systems. These smart systems rely mainly on the advances of information technologies like the internet of things, big data, and machine learning for aligning irrigations with climatic changes. Besides, integrating the predictive process helps in anticipating and adapting to the climatic constraints in agriculture, using meticulous soil and environment dependencies analysis based on features' prediction. In this paper, we enriched our proposed flexible online learning (OL) framework designed for promoting irrigation decisions based on soil characteristics analysis and prediction. We shed the light on a comparative study of four predictive methods, in particular, the auto-regressive moving average, the eXtreme Gradient Boosting, the random forest, and the deep artificial neural networks implemented inside the Hadoop/Spark environment to predict the humidity of the soil, relying on soil temperature and time in several depths. In the end, we discussed the precision of these models in various conditions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Test Periode S_m_5_predicɵonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Fig 9. Random forests soil moisture prediction in 5 cm depth using soil moisture and temperature parameters[4] …”

mentioning

confidence: 99%

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Hadoop–Spark Framework for Machine Learning-Based Smart Irrigation Planning

2021

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Agricultural Irrigation Recommendation and Alert (AIRA) system using optimization and machine learning in Hadoop for sustainable agriculture

Veerachamy

Ramalakshmi

2021

Environ Sci Pollut Res

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Smart Irrigation Systems in Agriculture: A Systematic Review

2023

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This research aims to carry out a systematic review of the available literature about smart irrigation systems. It will be focused on systems using artificial intelligence techniques in urban and rural agriculture for soil crops to identify those that are currently being used or can be adapted to urban agriculture. To this end, a modified PRISMA 2020 method is applied, and three search equations are formulated. From those filters, and after a screening process, 170 articles are obtained. These articles are analyzed through VantagePoint, a text processing software. After this, they are taken through a detailed analysis phase in which 50 sources are selected as the most relevant to be read and analyzed by topic. Finally, the different phases of the analysis are used to draw conclusions that might be interesting for researchers working in this specific field or for the general public interested in rural and urban agriculture and its automation.

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A Hadoop Based Framework for Soil Parameters Prediction

Cited by 3 publications

References 13 publications

Hadoop–Spark Framework for Machine Learning-Based Smart Irrigation Planning

Hadoop–Spark Framework for Machine Learning-Based Smart Irrigation Planning

Agricultural Irrigation Recommendation and Alert (AIRA) system using optimization and machine learning in Hadoop for sustainable agriculture

Smart Irrigation Systems in Agriculture: A Systematic Review

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