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

P. S., Maya Gopal and R., Bhargavi (2019) Performance Evaluation of Best Feature Subsets for Crop Yield Prediction Using Machine Learning Algorithms. Applied Artificial Intelligence, 33 (7). pp. 621-642. ISSN 0883-9514

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Abstract

The rapid innovations and liberalized market economy in agriculture demand accuracy in Crop Yield Prediction (CYP). In accurate prediction, machine learning (ML) algorithms and the selected features play a major role. The performance of any ML algorithm may improve with the utilization of a distinct set of features in the same training dataset. This research work evaluates the most needed features for accurate CYP. The ML algorithms, namely, Artificial Neural Network, Support Vector Regression, K-Nearest Neighbour and Random Forest (RF) are proposed for better accuracy. Agricultural dataset consists of 745 instances; 70% of data are randomly selected and are used to train the model and 30% are used for testing the model to assess the predictive ability. The results show that the RF algorithm reaches the highest accuracy by means of its error analysis values for all the distinct feature subsets using the same training agricultural data.

Item Type: Article
Subjects: Souths Book > Computer Science
Depositing User: Unnamed user with email support@southsbook.com
Date Deposited: 19 Jun 2023 10:28
Last Modified: 19 Jun 2024 12:37
URI: http://research.europeanlibrarypress.com/id/eprint/1234

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