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

Smart Farming Analytics: Exploring Classifier Diversity and Clustering In Land Suitability Forecasting

Authors
P. Yogendra Prasad1, M. Ramu2, Udatha Sahithi3, *, Vemula Vaishnavi3, P. Harshavardhan3, Malepati Charan Teja3
1Assistant Professor, Computer Science and Systems Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India
2Assistant Professor, Computer Science and Engineering, Annamacharya Institute of Technology & Sciences, Tirupati, India
3UG Scholar, Computer Science and Systems Engineering, Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College), Tirupati, India
*Corresponding author. Email: sahithiu9119@gmail.com
Corresponding Author
Udatha Sahithi
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_36How to use a DOI?
Keywords
Smart farming; classifier diversity; land suitability forecasting; precision agriculture
Abstract

Smart Farming Analytics (SFA) has emerged as a key tool in modern agriculture, transforming traditional farming practices by integrating advanced technologies. This research focuses on improving the accuracy and reliability of land suitability predictions in the field of intelligent agriculture. The study examines the use of classifier diversity and clustering techniques in forecasting model optimization. Various machine learning classifiers are employed to capture the multifaceted nature of land attributes, contributing to a more comprehensive analysis. Additionally, clustering algorithms aid in identifying distinct patterns and trends within the dataset, leading to improved precision in land suitability predictions. The synergy of classifier diversity and clustering not only enhances the predictive capabilities of the models but also provides valuable insights for decision-makers in optimizing resource allocation and crop planning. The results of this study support the development of intelligent farming techniques, promoting effective and sustainable agricultural systems in the face of changing climate and environmental factors.

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
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_36How 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

TY  - CONF
AU  - P. Yogendra Prasad
AU  - M. Ramu
AU  - Udatha Sahithi
AU  - Vemula Vaishnavi
AU  - P. Harshavardhan
AU  - Malepati Charan Teja
PY  - 2024
DA  - 2024/07/30
TI  - Smart Farming Analytics: Exploring Classifier Diversity and Clustering In Land Suitability Forecasting
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 361
EP  - 368
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_36
DO  - 10.2991/978-94-6463-471-6_36
ID  - Prasad2024
ER  -