Prediction of major crop yields of Tamilnadu using K-means and Modified KNN

Suresh, Aswathy; Kumar, P. Ganesh; Ramalatha, M.

doi:10.1109/cesys.2018.8723956

Cited by 43 publications

(10 citation statements)

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“…The proximity of data points is shown in the decision surface, that is, hyperplane support vectors. Suresh et al (2018) examined soil profiles in conjunction with Global Positioning System-based technologies for soil identifications. The kmeans method is applied for soil classification.…”

Section: Literature Reviewmentioning

confidence: 99%

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Predicting the suitable fertilizer for crop based on soil and environmental factors using various feature selection techniques with classifiers

et al. 2022

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Agriculture is an essential part of human life. The crop productivity is based on the soil and environmental factors. Different crops are cultivated in different areas. Nowadays, the crop productivity level is affected by the climate change and diseases in the crops. Due to this pest infestation, the crop growth is heavily affected. To overcome this problem, the right fertilizer for a particular crop has to be chosen and fertilizer helps farmers to improve the crop productivity rate. This process can be done by using various machine learning techniques. In this work, various features selection techniques with classifiers used to predict the suitable fertilizer for a crop. The experimental results show that recursive feature elimination along the proposed Heterogeneous Stacked Ensemble classifier gives better prediction rate than other methods.

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Section: Literature Reviewmentioning

confidence: 99%

“…Suresh et al (2018) examined soil profiles in conjunction with Global Positioning System‐based technologies for soil identifications. The k‐means method is applied for soil classification.…”

Section: Introductionmentioning

confidence: 99%

Predicting the suitable fertilizer for crop based on soil and environmental factors using various feature selection techniques with classifiers

et al. 2022

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“…Almost all techniques adopt deep learning or machine learning to predict the overall crop productivity 20 . Deep neural networks like CNN (convolutional neural network), 21 RNN (recurrent neural network), 22 KNN (K‐nearest neighbor), 23 RBFNN (residual basis function neural network), 24 multitask learning 25 and other combined methods like SVM (support vector machine)‐LSTM‐RNN, 26 CNN‐RNN 27 are used by various researchers to predict the crop yield. Several types of research work for crop yield prediction based on data mining are also introduced, such as eXtensible Crop Yield Prediction Framework (XCYPF) 28 and artificial neural network (ANN) with cascade and Elman back‐propagations 29 .…”

Section: Introductionmentioning

confidence: 99%

Hybrid bidirectional long short term memory with black widow optimization for crop yield prediction using data mining

Joseph

Ramasamy

2023

Concurrency and Computation

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SummaryCrop yield prediction is highly significant in the agricultural sector. It helps to understand the growth rate of major food crops and identify measures to improve the overall yield. The article proposes a hybrid strategy called bidirectional long short term memory with black widow optimization (Bi‐LSTM‐BWO) for predicting the annual yield produced with improved accuracy. Initially, data augmentation is performed for the collected dataset to increase the size of the dataset and to reduce the data scarcity problem. Then, the dataset is preprocessed to improve the data's quality and remove the noise and irrelevant information. The data is cleaned, transformed, and discretized in the preprocessing stage using various techniques. Then, the preprocessed data is clustered using an enhanced K‐means clustering technique. To enhance the clustering technique, the proposed technique utilized the rain optimization algorithm that automatically computes the initial centroids to improve the clustering outcome. Finally, the prediction process is performed using the proposed Bi‐LSTM‐BWO prediction scheme. The proposed prediction strategy efficiently predicts the annual yield with a high accuracy rate and minimizes loss. The proposed technique achieves a 99.18%, 99.81% and 99.01% accuracy rates for the summer, autumn and winter yield prediction, respectively.

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“…Under unsupervised learning, this is one of the oldest and most common clustering algorithms. It divides the dataset into partitions based on the dataset's mean value and processes iteratively until no more partitions are available [1][2][3]. This survey studies the problems of and solutions to partitionbased clustering, and more specifically the widely used k-means algorithm, which has been listed among the top 10 clustering algorithms for data analysis [4].…”

Section: Introductionmentioning

confidence: 99%

Application of K-means Clustering Algorithm in Rice Production of Tamil Nadu, India

Abirami

Selvi

Kannan

et al. 2022

IJECC

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Aim: (i) To Cluster the Rice data using K-Means clustering algorithm. (ii) To helps the study of crop yield prediction. Study Design: K-Means clustering technique is one of the most common exploratory data analysis used to get an intuition about the structure of the data. Place and Duration of Study: Time Series crop data were collected from the season and crop report, Directorate of Economics and Statistics, Chennai for the period of 2015-2020. Methodology: The machine learning algorithm of big data analytics method such as K-means clustering algorithm helps to predict the paddy yield accurately in Tamil Nadu. The performance of the technique is examined through the determinable value of k by Elbow method and Silhouette method which helps in the crop yield prediction. Results: The observed results show that there is a positive relationship between area, production, area under irrigation, minimum temperature, and relative humidity and a close negative relationship with moisture and wind speed. Additionally, two clusters were identified with cluster 2 having the highest mean value, followed by 1. The identification of the highest mean clusters will guide farmers on where best to concentrate on when planting their crops in ordered to improve productivity and crop yield. Conclusion: This study reveals a scalable, simple, and reduced method for correctly assessing rice production over a large area using publicly released multi-source data, which may have been used to calculate crop production in areas with rarely observed data and all around the world.

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Prediction of major crop yields of Tamilnadu using K-means and Modified KNN

Cited by 43 publications

References 0 publications

Predicting the suitable fertilizer for crop based on soil and environmental factors using various feature selection techniques with classifiers

Predicting the suitable fertilizer for crop based on soil and environmental factors using various feature selection techniques with classifiers

Hybrid bidirectional long short term memory with black widow optimization for crop yield prediction using data mining

Application of K-means Clustering Algorithm in Rice Production of Tamil Nadu, India

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