Review of Machine Learning Model Applications in Precision Agriculture

DOI

10.2991/978-94-6463-136-4_81How to use a DOI?

Keywords

Machine learning; Crop yield; Disease detection; Support Vector Machine; K-Means Clustering; ANN; CNN; Precision agriculture

Abstract

Over the past two decades, modern agriculture has made significant advancements. The methods used in farming have changed from conventional ways to digital technologies as a result of significant technology improvement. Advances in machine learning and artificial intelligence are being applied in this discipline to reevaluate farming practices in order to meet the demands of an expanding population. Throughout the entire cycle of planting, growing, and harvesting, machine learning is prevalent. It starts with the planting of a seed in the ground, goes through soil preparation, seed breeding, crop health monitoring, measuring water feed and concludes with the harvest being picked up by robots by using computer vision techniques.

For crop selection, yield prediction, soil classification, weather forecasting, irrigation system, fertilizer prescription, disease prediction, and determining the minimal support price, machine learning models are developed in the field of precision agriculture. In this article we will cover the different categories of precision agriculture applications and use of machine learning models in those different categories. Various models in precision agriculture include Artificial Neural Networks, Support Vector Machines (SVMs), Convolution Neural Networks (CNN), Random Forest (RF), K-Nearest Neighbor (KNN), K-Means Clustering. The ultimate solution to issues in agriculture rests in the efficient application of Machine Learning (ML). ML can bring about a paradigm change in nations like India where agriculture is the main source of employment. Since most Indian rural areas have adopted digitalization, ML and AI-related applications are gradually emerging in this sector.

Copyright

© 2023 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  - Patil Sagar Baburao
AU  - R. B. Kulkarni
AU  - Pramod A. Kharade
AU  - Suchita S. Patil
PY  - 2023
DA  - 2023/05/01
TI  - Review of Machine Learning Model Applications in Precision Agriculture
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 916
EP  - 930
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_81
DO  - 10.2991/978-94-6463-136-4_81
ID  - Baburao2023
ER  -