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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Crop recommendation system using gradient boosting algorithm |
| Authors Name: | E Sai Sathvik , Dr. G Rosline Nesa Kumari , M Sarath Chandra , S Pradeep Reddy , S Kalyan Raju |
| Unique Id: | IJSDR2404121 |
| Published In: | Volume 9 Issue 4, April-2024 |
| Abstract: | Abstract-Crop Recommendation System (CRS) leveraging the Gradient Boosting algorithm to enhance precision in agricultural decision-making. With the evolving challenges in agriculture, traditional methods of crop selection often struggle to account for the dynamic interactions among environmental variables. Gradient Boosting Algorithm which is known of its strong feature for solving diversified and complex problems wins over as a reliable and adaptable method for developing exact and dynamic crop recommendations. Introduction of the study includes a review on relevance of literature that shows us the evolutionary part of machine learning in agriculture and its significance in application of crop recommendation systems through accomplishment with the help of Gradient Boosting algorithms. Where the practical part of the scientific method is given more attention, the paper dwells on the critical stages of data acquisition and preprocessing including the consideration of plant properties, the history of inputs methods, clay data and weather information. After this, the Gradient Boosting algorithm is expounded on, exposing which enhancing mechanisms are implemented, basic principles of ensemble learning techniques and the best model configuration. CRS's efficacy is revealed by many different sets of case studies from agrarian areas, in which it shows its ability to generate particular solutions depending on local departments. In the side-wise comparison of the algorithm with other machine-learning models, the former stands out in terms of predictive prowess. The paper winds up discussing the scalability issues by recognizing constraints, challenges, and possible options for future research, indicating that the mentioned CRS based on Gradient Boosting is a potential instrument for systematic crop decision, output maximization, and ecologically friendly agriculture. |
| Keywords: | Decision Trees, CRS, Gradient boosting algorithm, Machine Learning. |
| Cite Article: | "Crop recommendation system using gradient boosting algorithm", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 4, page no.851 - 859, April-2024, Available :http://www.ijsdr.org/papers/IJSDR2404121.pdf |
| Downloads: | 000338173 |
| Publication Details: | Published Paper ID: IJSDR2404121 Registration ID:210822 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: 851 - 859 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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