Prediction of effective rainfall and crop water needs using data mining techniques

Abishek, B.; Priyatharshini, R.; Eswar, Meghana; Deepika, P.

doi:10.1109/tiar.2017.8273722

Cited by 19 publications

(5 citation statements)

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“…Abhishek B. et al [11] used classification data mining techniques for forecasting of Rainfall status accurately and required Water for Crops using these Techniques.…”

Section: Related Research Workmentioning

confidence: 99%

Performance Analysis of Soil Health Classifiers Using Data Analytics Tools and Techniques for Best Model and Tool Selection

Vispute

Saini

2022

Int. J. Onl. Eng.

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One of the most crucial stages in the building of a Machine Learning (ML) model is the evaluation and analysis of classifier model performance. The agricultural sector is the economic backbone of India and needs extensions to provide solutions to the problems faced by the farmers. This paper presents agriculture soil health analysis using Machine Learning approaches for best model and tool selection and also bibliometric analysis to identify different sources and author’s keywords for finding the area of focus for proposed work. Models are built on SK-Learn, KNIME, WEKA and Rapid Miner tools using different ML algorithms. Nave Bayes, Random Forest (RF), Decision Tree (DT), Ensemble learning (EL), and k-Nearest Neighbor (KNN) are used to analyze soil data on these tools. Results show that Decision Tree model outperforms other algorithms, followed by RF algorithm which is a set of multiple Decision tree algorithms and SK-Learn tool gives better accuracy followed by WEKA tool then KNIME tool. Maximum accuracy obtained by Decision Tree algorithm is 98.40% using SK-Learn followed by KNIME tool with 73.07%, Maximum accuracy obtained by Naïve Bayes algorithm is 69.50% using SK-Learn followed by KNIME tool with 68.14%, maximum accuracy obtained by Random Forest algorithm is 85.00% using SK-Learn followed by 73.06% using WEKA tool, maximum accuracy obtained by Ensemble algorithm is 89.00% using SK-Learn followed by 73.06% using WEKA tool and for KNN it is 95.50% using SK-Learn followed by 71.85% using WEKA tool.

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“…Abhishek B. et al [11] used classification data mining techniques for forecasting of Rainfall status accurately and required Water for Crops using these Techniques.…”

Section: Related Research Workmentioning

confidence: 99%

Performance Analysis of Soil Health Classifiers Using Data Analytics Tools and Techniques for Best Model and Tool Selection

Vispute

Saini

2022

Int. J. Onl. Eng.

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“…The author describes as indicated by the ebb and flow water emergency and spend in excess of 94 percent of water in farming the motorized water system frameworks, and changed the genuine plant water estimation are required it is encourage to foresee precipitation in the developing season [24]. The author describes viable precipitation assumes a colossal job in the development of harvests in farming during all phases of yield development [25]. The author describes Irregularity in atmosphere systems of precipitation is a wellspring of living components and non-living component stresses in horticultural frameworks around the world [26].…”

Section: Literature Foundationmentioning

confidence: 99%

Novel Methods for Crop Prediction based on Influencing Parameters in Indian Regions using Data Mining

Joseph,

Manohar

2020

IJEAT

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Agriculture plays a crucial role for the production of food in Indian regions. Indian regions mainly produces crops like rice, wheat, maize and many other types of crop. There are several factors required for the productivity of any harvest, but we know that soil, climate, pesticides, Fertilizers and ground water is most influencing essential factor for the productivity of any harvest. Let us consider soil which is the key element as it provides nutrients for proper development and growth of crops. Secondly, climate is also having major role in agriculture as crop growth depends on rainfall, humidity, temperature etc. Thirdly, Pesticides is widely used to control pest and prevents the damage of crops. Fourthly, Fertilizers can improve the quality of crops. Finally, ground water which will enrich nutrients in soil. The current preparation centers around different information mining procedures utilized in various regions of India and anticipate future harvest along with reasonable information mining procedure saw during the period(1920-2019).The parameters considered for the examination were soil, atmosphere, water thickness, pesticides and composts and Crop informational collection. The Classification calculations utilized in preparation were Adaptive boosting classification, Excess tree classification, neural based classification, Multiple Process classification, Decision making classification, K-closest neighbors, Bayesian theory classification, decision Forest classification, support group machine, and Randomized Gradient Classification. By using the techniques mentioned above we can improve the harvest prediction using information mining techniques which in turn help the farmers to take better decisions in future and it can be used in other technologies like image analyzing etc. The Experimental results show predicted crop, suitable algorithm and algorithm accuracy in that particular state of India respectively.

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“…Abishek.B, et.al (2017) presented that it is very difficult to predict the effective rainfall and crop water. There are certain factors such as temperature and humidity that need to be known in order to provide a meticulous and scrupulous analysis [17]. In order to define the effective amount of rainfall that has been within a designated region in simple manner, several factors such as humidity, groundwater, and temperature have been considered.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Wheat Production using Naïve Bayes Classifier

Kaur¹,

Kalsi²

2019

IJCA

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Data mining is defined as the process in which useful information is extracted from the raw data. In order to acquire essential knowledge it is essential to extract large amount of data. This process of extraction is also known as misnomer. Currently in every field, large amount of data is present and analyzing whole data is very difficult as well as it consumes a lot of time. The prediction analysis is most useful type of data which is performed today. To perform the prediction analysis the patterns needs to generate from the dataset with the machine learning. The prediction analysis can be done by gathering historical information to generate future trends. So, the knowledge of what has happened previously is used to provide the best valuation of what will happen in future with predictive analysis. Crop production analysis is one of the applications of prediction analysis. In this research work, the Naïve Bayes classifier is applied for the wheat production prediction. The Naïve Bayes classifier is compared with SVM and KNN. The Naïve Bayes performs well for the wheat production analysis.

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Prediction of effective rainfall and crop water needs using data mining techniques

Cited by 19 publications

References 0 publications

Performance Analysis of Soil Health Classifiers Using Data Analytics Tools and Techniques for Best Model and Tool Selection

Performance Analysis of Soil Health Classifiers Using Data Analytics Tools and Techniques for Best Model and Tool Selection

Novel Methods for Crop Prediction based on Influencing Parameters in Indian Regions using Data Mining

Analysis of Wheat Production using Naïve Bayes Classifier

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