Data Mining Technique to Analyze Soil Nutrients Based on Hybrid Classification

Manjula, E.; S.Djodiltachoumy,

doi:10.26483/ijarcs.v8i8.4794

Cited by 23 publications

(19 citation statements)

References 3 publications

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“…In 2017, data mining techniques are used to conduct research in the field of agriculture using soil datasets from various districts in Tamil Nadu. They investigated 12 soil qualities from Ariyalur, Coimbatore, Karur, Salem, Thanjavur, and Trichy, and classified them into type 1, type 2, and type 3 with a hybrid technique that produced a 99.93% accuracy for 3000 data in less classification time [23].…”

Section: Cu Cu 2+mentioning

confidence: 99%

“…Various studies have used soil datasets such as pH, EC, OC, soil type, macro and micronutrients to predict the fertility of the soil [23][24] [27][28] [31]. The researchers have utilized several machine learning models such as naive bayes, SVM, ELM, deep neural networks, decision tree, hybrid approach, and random forest, with hybrid, random forest, and ELM being the best models for predicting soil fertility, as shown in Table 1.…”

Section: Classification Of the Fertility Of The Soilmentioning

confidence: 99%

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A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients

Prabavathi¹,

Chelliah²

2022

ujar

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Agriculture is the backbone of India's economy, as it is the most important factor in the country's socio-economic development. Because of the rapid expansion in human population, the "Green Revolution" introduced high yield variety (HYV) seeds, which increased crop productivity but degraded crop and soil quality. This is due to the use of excessive amounts of chemical fertilizers in HYV seeds, as well as the irrigation system utilized to grow these seeds. This stunts the growth of the crops, resulting in financial and productivity losses. Because of field surveys, traditional ways to crop production prediction will take longer, and contemporary agriculture will face certain obstacles. As a result, a comprehensive review of various crop key factors such as climatic factors, soil nutrients, production factors, and environmental factors is conducted using a variety of machine learning approaches such as Support Vector Machine, bayes classifier, decision tree, random forest, linear regression and Extreme Learning Machines. The accuracy measures such as root mean square error, coefficient of determination and mean absolute error are used for comparing the performance of the system. Based on the findings of the reviews, an intelligent and robust machine learning technique provides the optimum option for achieving (i) soil fertility, (ii) crop prediction, and (iii) yield prediction. The importance of soil variables and the amount of nutrients available in the soil for growing crops has been found, according to an examination of 51 peer-reviewed studies, to create qualitative yield prediction. Furthermore, the investigations will yield recommendations for future fertilizer research.

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Section: Cu Cu 2+mentioning

confidence: 99%

Section: Classification Of the Fertility Of The Soilmentioning

confidence: 99%

A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients

Prabavathi¹,

Chelliah²

2022

ujar

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“…Manjula et al has discussed about the micronutrient elements present in the soil namely Fe, Zn, Ca, Ni, K, Mn and S that are used by several classification data mining techniques like Nave Bayes (NB), Decision Tree (DT), and a hybrid classification technique with DT and NB. The performance metrics of time as well as accuracy are compared using several classification methods based on the performance [19]. Rohit Kumar Rajak et al have implemented Support Vector Machine (SVM) and Artificial Neural Network (ANN) for evaluating the soil nutrients by specified efficiency and accuracy metrics which have been utilized for a dataset of soil testing lab based on the accomplishment of parameters to select a crop [20].…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Approach for Stochastic Pattern Analysis for the Measurement of Time-Series Datasets

Ahmed¹,

Kamalakannan²,

Kavitha³

2022

I2M

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Time series data analysis is used in many domains of applications to perform efficient prediction, anomaly detection and forecasting using various techniques. Recently, there is a surge in demand for comprehensive data mining techniques that can be applied on agricultural prediction. Data mining techniques can be used effectively for predicting the agricultural growth using stochastic model sensing concept which is used to perform better analysis for predicting the dataset. The proposed work introduces a stochastic model that can be applied for agricultural data to predict the growth of any crops using primary time series datasets The framework has assisted in taking correct decisions to classify the dataset based on the threshold value of micro and macro nutrient obtained from National Food Security Mission (NFSM). This paper focus on analyzing the stochastic pattern that assist the proportion of micronutrient elements and also predicts the feature that affect the growth of agriculture by increase or decrease the level of micronutrient elements. Thus, the Expected Growth of Agriculture (EGA) has increased or decreased based on the strength of soil nutrients. Moreover, it will recommend chemical fertilizers and nutrients range to improvise the agriculture growth for each crop. The processed soil sample dataset based on the threshold value of micro nutrient level is obtained from National Food Security Mission (NFSM). There are several numerical illustrations that are performed for micronutrient data prediction as well as analyze the increase or decrease of growth level in agriculture based on micronutrient levels like Fe, Mn, Zn and Cu using Stochastic Weibull Distribution (SWD) model.

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“…N. Sneha and J. Majumdar, "Translated copy of Tank of [14] presented an efficient strategy for crops cultivation of crop yield prediction. It is the most important factor where the farmers need some prior information about the crop yield before sowing seeds in their fields with available requirements.…”

Section: Related Workmentioning

confidence: 99%

Extemporize Agriculture Yield with Predictions Based on Water and Soil Properties using Multivariate Analytics and Machine Learning Algorithm

Sudha*,

Mohan

2019

IJEAT

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the extensive attention of farmers to get extraordinary ideas for future crop development based on soil and water is an essential key factor of agriculture to predict data. In future farmers apply the extracts PCA (Principal component analysis) in the agriculture for crops to find best yield. The level of data is analyzed using PCA and PLS (Projection to latent structure) datasets like Crop data, Soil properties and Water properties such as Linear Regression, Multi Linear Regression, Discriminate Analysis, Partial Least Square, Least squares algorithm is used instead of Multivariate Curve Resolution. Predictive analytics can be used to make a modular decision in farming by observation of actual time data on crops, soil and water data received from different agriculture sources would contain multi-dimensions, the entire content is needed for performing analysis. Multivariate data Analysis based on Partial Least Square Programming model that identifies the cropping pattern to getting maximum yield correlated. The motive of the work is to compare various techniques which give the maximum accuracy of crop.

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Data Mining Technique to Analyze Soil Nutrients Based on Hybrid Classification

Cited by 23 publications

References 3 publications

A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients

A Comprehensive Review on Machine Learning Approaches for Yield Prediction Using Essential Soil Nutrients

A Machine Learning Approach for Stochastic Pattern Analysis for the Measurement of Time-Series Datasets

Extemporize Agriculture Yield with Predictions Based on Water and Soil Properties using Multivariate Analytics and Machine Learning Algorithm

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