Welcome to the InfoSci Platform
Reference Hub2
Information Extraction From the Agricultural and Weather Domains Using Deep Learning Approaches

Information Extraction From the Agricultural and Weather Domains Using Deep Learning Approaches

Sunil Kumar, Hanumat Sastry G., Venkatadri Marriboyina
Copyright: © 2022 |Volume: 10 |Issue: 1 |Pages: 12
ISSN: 2166-7160|EISSN: 2166-7179|EISBN13: 9781683182832|DOI: 10.4018/IJSI.293266
Cite Article Cite Article

MLA

Kumar, Sunil, et al. "Information Extraction From the Agricultural and Weather Domains Using Deep Learning Approaches." IJSI vol.10, no.1 2022: pp.1-12. http://doi.org/10.4018/IJSI.293266

APA

Kumar, S., Hanumat Sastry G., & Marriboyina, V. (2022). Information Extraction From the Agricultural and Weather Domains Using Deep Learning Approaches. International Journal of Software Innovation (IJSI), 10(1), 1-12. http://doi.org/10.4018/IJSI.293266

Chicago

Kumar, Sunil, Hanumat Sastry G., and Venkatadri Marriboyina. "Information Extraction From the Agricultural and Weather Domains Using Deep Learning Approaches," International Journal of Software Innovation (IJSI) 10, no.1: 1-12. http://doi.org/10.4018/IJSI.293266

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

India is an agricultural region and the economy of the country depends upon agriculture. Change in climatic parameters (like rainfall, soil, etc) directly affect the growth of crops. This parameter has an unswerving effect on the quantity of food production. Information extraction from the agricultural domain through rainfall prediction has been one of the most challenging issues around the world in recent years because of climatic changes. To evaluate the feasibility of rain by employing some data analytics and machine learning techniques are developed. This paper proposes an enhanced deep learning-based approach known as Deep Regression Network (DRN). The proposed DRN is a 6-layer deep neural network. The proposed algorithm trains and tests on the agricultural corpus, collected from Dehradun (India) region. The experimental outcomes state that the proposed DRN method attained a prediction accuracy approx 86.56%. The comparative analysis shows that the proposed method outperformed existing methods like Ensemble Neural Network, Naïve Bayes, KNN, and Weighted Self-Organizing Map.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.