Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Agro-Insight: Recommendation System Using Machine Learning

Authors
Shaik Salma1, M. Asha Priyadarshini2, P. Sri Manaswini3, *, P. Sahil Kumar3, P. Prathyusha3, S. Ganesh3
1Assistant Professor, Department of CSE, Vignan’s Lara Institute of Technology & Science, Vadlamudi, Guntur, Andhra Pradesh, India
2Associate Professor, Department of CSE, Vignan’s Lara Institute of Technology & Science, Science, Vadlamudi, Guntur, Andhra Pradesh, India
3UG Final Year, Department of CSE, Vignan’s Lara Institute of Technology & Science, Science, Vadlamudi, Guntur, Andhra Pradesh, India
*Corresponding author.
Corresponding Author
P. Sri Manaswini
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_79How to use a DOI?
Keywords
Crop Recommendation; Fertilizer Recommendation; Machine Learning; Random Forest; Logistic Regression; Naive Bayes; SVM; Decision Tree; KNN; Bagging; Gradient Boosting; Extra Trees; Sustainability; Arid Land; Agricultural Productivity; Food Security
Abstract

Optimizing crop and fertilizer recommendations is paramount for productivity and sustainability in agriculture sector. Traditionally reliant on labor-intensive expert knowledge, this process now shifts towards automation with machine learning techniques. Our study on the existing system includes Random Forest, Logistic Regression, Naive Bayes, SVM, Decision Tree, KNN, Bagging, extra trees and Gradient Boosting algorithms to optimize crop and fertilizer. Recommendations for arid lands. Proposed method used Random Forest classifier for prediction of crops and Decision Tree classifier for prediction of fertilizer. By considering soil composition and climate evaluation, we achieved consistent accuracy rates exceeding 90%, with the highest at 99%. This approach has the potential to revolutionize crop recommendation system and fertilizer recommendations, benefiting farmers by enhancing yields and sustainability. Integrating cutting-edge technology like machine learning into agricultural practices addresses the needs for increased production while ensuring environment sustainability and food security.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
10.2991/978-94-6463-471-6_79
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_79How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shaik Salma
AU  - M. Asha Priyadarshini
AU  - P. Sri Manaswini
AU  - P. Sahil Kumar
AU  - P. Prathyusha
AU  - S. Ganesh
PY  - 2024
DA  - 2024/07/30
TI  - Agro-Insight: Recommendation System Using Machine Learning
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 824
EP  - 834
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_79
DO  - 10.2991/978-94-6463-471-6_79
ID  - Salma2024
ER  -