Performance Evaluation of Best Feature Subsets for Crop Yield Prediction Using Machine Learning Algorithms

Gopal, P S Maya; Bhargavi, R.

doi:10.1080/08839514.2019.1592343

Cited by 107 publications

(30 citation statements)

References 33 publications

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“…Aileen Bahl et al [25], developed a random forest (RF) model with the RFE for improved prediction accuracy. Maya Gopal and Bhargavi [26] analysed the performance of machine learning (ML) algorithms with a variety of feature selection techniques for crop yield prediction. The results showed that the random forest provides higher accuracy than other ML algorithms.…”

Section: Literature Survey and Justification For The Proposed Workmentioning

confidence: 99%

“…Classification is used to predict the class for each record in a dataset. This study uses supervised learning techniques for prediction that handle high-dimensional data, like the k-Nearest Neighbour (kNN) [28], Naïve Bayes (NB) [21], Decision Tree (DT) [28], SVM [29], Random Forest (RF) [26], and Bagging [30]. The kNN makes no assumptions about the data, though data scaling is a must.…”

Section: Literature Survey and Justification For The Proposed Workmentioning

confidence: 99%

“…Feature selection is a major task in data analytic research, where datasets have a large number of attributes [26]. Feature selection is chiefly used because (i) it allows the machine learning algorithm to train faster, (ii) it decreases the complexity of the model, and (iii) it makes interpretation easier [26]. It also maximizes the model's accuracy when choosing the right subset, and prevents overfitting.…”

Section: Feature Selectionmentioning

confidence: 99%

See 2 more Smart Citations

Crop prediction based on soil and environmental characteristics using feature selection techniques

Suruliandi

Mariammal

Raja

2021

Mathematical and Computer Modelling of Dynamical Systems

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Earlier, crop cultivation was undertaken on the basis of farmers' hands-on expertise. However, climate change has begun to affect crop yields badly. Consequently, farmers are unable to choose the right crop/s based on soil and environmental factors, and the process of manually predicting the choice of the right crop/s of land has, more often than not, resulted in failure. Accurate crop prediction results in increased crop production. This is where machine learning playing a crucial role in the area of crop prediction. Crop prediction depends on the soil, geographic and climatic attributes. Selecting appropriate attributes for the right crop/s is an intrinsic part of the prediction undertaken by feature selection techniques. In this work, a comparative study of various wrapper feature selection methods are carried out for crop prediction using classification techniques that suggest the suitable crop/s for land. The experimental results show the Recursive Feature Elimination technique with the Adaptive Bagging classifier outperforms the others.

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Section: Literature Survey and Justification For The Proposed Workmentioning

confidence: 99%

Section: Literature Survey and Justification For The Proposed Workmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Crop prediction based on soil and environmental characteristics using feature selection techniques

Suruliandi

Mariammal

Raja

2021

Mathematical and Computer Modelling of Dynamical Systems

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“…− The proposed scheme offered relatively high accuracy of prediction (RMSE of 1.737 and 2.329 for training and testing data, respectively). [17] Support vector regression, K-nearest neighbor, random forest, and artificial neural network − Used the agricultural dataset to contain 745 cases; 70% of statistics are randomly nominated to train the model and 30% are used for testing the model to evaluate the predictive capability. − Among the four algorithms, random forest offered the best accuracy in prediction.…”

Section: Ref Nomentioning

confidence: 99%

Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu

2021

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Agriculture is the principal basis of livelihood that acts as a mainstay of any country. There are several changes faced by the farmers due to various factors such as water shortage, undefined price owing to demand–supply, weather uncertainties, and inaccurate crop prediction. The prediction of crop yield, notably paddy yield, is an intricate assignment owing to its dependency on several factors such as crop genotype, environmental factors, management practices, and their interactions. Researchers are used to predicting the paddy yield using statistical approaches, but they failed to attain higher accuracy due to several factors. Therefore, machine learning methods such as support vector regression (SVR), general regression neural networks (GRNNs), radial basis functional neural networks (RBFNNs), and back-propagation neural networks (BPNNs) are demonstrated to predict the paddy yield accurately for the Cauvery Delta Zone (CDZ), which lies in the eastern part of Tamil Nadu, South India. The performance of each developed model is examined using assessment metrics such as coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), coefficient of variance (CV), and normalized mean squared error (NMSE). The observed results show that the GRNN algorithm delivers superior evaluation metrics such as R2, RMSE, MAE, MSE, MAPE, CV, and NSME values about 0.9863, 0.2295 and 0.1290, 0.0526, 1.3439, 0.0255, and 0.0136, respectively, which ensures accurate crop yield prediction compared with other methods. Finally, the performance of the GRNN model is compared with other available models from several studies in the literature, and it is found to be high while comparing the prediction accuracy using evaluation metrics.

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“…S & R [29] determined most of the important points for accurate Crop's yield Prediction. For improved accuracy, the algorithm of machine learning namely Support Vector Machine, KNN, Regression, Random Forest and Artificial Neural Network have been proposed.…”

Section: Related Workmentioning

confidence: 99%

Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems

Ismael

Abdulazeez

Hasan

2021

QAJ

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The agriculture importance is not restricted to our daily life; it is also an effective field that enhances the economic growth in any country. Therefore, developing the quality of the crop yields using recent technologies is a crucial procedure to obtain competitive crops. Nowadays, data mining is an emerging research field in agriculture especially in the predicting and analysis of crop yield. This paper focuses on utilizing various data mining classification algorithms to predict the impact of various parameters such as area, season and production on the crop yield quality. The performance of the decision tree, naive Bayes, random forest, support vector machine and K-nearest neighbour is measured and compared to each other. The comparison involves measuring the error values and accuracy. The SVM algorithm achieved the highest accuracy value with 76.82%. while the lowest is achieved by the KNN algorithm with 35.76%. The highest error value was 111.8855 for KNN. Also, the prediction help farmer to increased and improved the income level.

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Performance Evaluation of Best Feature Subsets for Crop Yield Prediction Using Machine Learning Algorithms

Cited by 107 publications

References 33 publications

Crop prediction based on soil and environmental characteristics using feature selection techniques

Crop prediction based on soil and environmental characteristics using feature selection techniques

Exploration of Machine Learning Approaches for Paddy Yield Prediction in Eastern Part of Tamilnadu

Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems

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