Summary of Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions, by Aashu Katharria et al.
Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions
by Aashu Katharria, Kanchan Rajwar, Millie Pant, Juan D. Velásquez, Václav Snášel, Kusum Deep
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive survey of recent machine learning (ML) applications in agriculture for sustainability and efficiency is presented. The study analyzes ML techniques across pre-harvesting, harvesting, and post-harvesting phases, demonstrating its potential with agricultural data and data fusion. A bibliometric and statistical analysis reveals research trends and activity, while real-world case studies of AI-driven agricultural companies using multisensors and multisource data are investigated. Publicly available datasets for ML model training are compiled. The review highlights how ML techniques combined with multi-source data fusion enhance precision agriculture by improving predictive accuracy and decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is being used to make farming more efficient and sustainable. This paper looks at how different machine learning techniques can be used in agriculture, from before harvesting to after harvest. It also shows how combining different types of data can improve the accuracy of predictions and decision-making. The study looks at real-world examples of companies using this technology and provides publicly available datasets that others can use. |
Keywords
* Artificial intelligence * Machine learning * Precision
