Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters

Suchithra, M. S.; Pai, Maya L.

doi:10.1016/j.inpa.2019.05.003

Cited by 109 publications

(64 citation statements)

References 21 publications

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“…Pursuant to geographical pattern of Bhimtal Block most of the villages are located in hilly region and there is always the possibilities of soil erosion and leaching of soil nutrients. The soil data we have used in our study is preprocessed by calculating the standard deviation and mean [16] of each input feature. The result of these preprocessing is plotted in Figure 3.…”

Section: Resultsmentioning

confidence: 99%

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Soil Quality Prediction for Determining Soil Fertility in Bhimtal Block of Uttarakhand (India) Using Machine Learning

Pant

Pant²,

Pant³

et al. 2021

ijaa

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Section: Resultsmentioning

confidence: 99%

“…Regression methods were applied in this paper [15] to predict soil fertility for various available nutrients in Maharashtra. In this paper [16] soil nutrients and pH classification and Soil features predictions was described by Extreme Learning Machine. This work was done in the state of Kerala.…”

Section: Introductionmentioning

confidence: 99%

Soil Quality Prediction for Determining Soil Fertility in Bhimtal Block of Uttarakhand (India) Using Machine Learning

Pant

Pant²,

Pant³

et al. 2021

ijaa

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“…The nitrogen, phosphorus, potassium and organic carbon content were determined based on the procedures defined elsewhere 8 . Further, the five-level classification of macronutrients presents in the Indian soils was undertaken similar to the one based on the method defined similarly to one defined elsewhere 9 .…”

Section: Data and Classificationmentioning

confidence: 99%

Classification of Indian States and Union Territories based on their Soil Macronutrient and Organic Carbon Profiles

Kaushik

2020

Preprint

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Soil fertility determines the successful development of a plant, and therefore it is important to achieve food security. Imbalanced and inadequate use of chemical fertilizers, irregular irrigation and harmful cultural practices deplete the soil profile nutrient profile, which is critical for the successful crop production. This study presents the results of the classification of the states across India based on their soil macronutrient profile. The entanglement coefficient of Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and the neighbour-joining method was 0.81. Absolute correlation values were determined among the different classes of the soil nitrogen content and the corresponding classes of the soil phosphorus content. The K-mean clustering method divided the states and union territories into the three clusters. Overall, this works represents the grouping of Indian soils based on their soil macronutrient and organic carbon content.

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“…In recent years machine learning has been very helpful in studying the elimination of nutrients in soil. Nowadays soil classification and prediction problems are easily handled by Machine Learning techniques [2]. Various Machine Learning techniques were used to predict the soil nutrients, soil type and soil moisture [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays soil classification and prediction problems are easily handled by Machine Learning techniques [2]. Various Machine Learning techniques were used to predict the soil nutrients, soil type and soil moisture [2], [3]. Soil fertility is affected by many factors like air, water, organic matter and nutrients.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection towards Soil Classification in the context of Fertility classes using Machine Learning

Pant¹,

Pant²,

Bhatt³

et al. 2019

IJITEE

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Soil is recognized as one of the most valuable entity which is responsible for sustaining life on the earth. It is clear that Indian economy is largely dependent on agriculture. In India, for a large section of the population the sole or major source of earnings is agriculture. There are many factors which are responsible in yield production and also affect agriculture. Soil is one of them and plays a crucial role in yield production. Soil nutrients are important aspects that contribute to soil fertility. In this paper we classify soil in the form of fertility index level using machine learning. We induced rule model on training data and apply this model on test data for making predictions for fertility level classes. In this paper we have used Rough Set method to classify the data based on fertility level class and calculate the accuracy of the used classifier.

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Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters

Cited by 109 publications

References 21 publications

Soil Quality Prediction for Determining Soil Fertility in Bhimtal Block of Uttarakhand (India) Using Machine Learning

Soil Quality Prediction for Determining Soil Fertility in Bhimtal Block of Uttarakhand (India) Using Machine Learning

Classification of Indian States and Union Territories based on their Soil Macronutrient and Organic Carbon Profiles

Feature Selection towards Soil Classification in the context of Fertility classes using Machine Learning

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