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

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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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  -