Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions

• URL: http://arxiv.org/abs/2405.17465v2
• Date: Tue, 18 Mar 2025 17:32:09 GMT
• Title: Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions
• Authors: Aashu Katharria, Kanchan Rajwar, Millie Pant, Juan D. Velásquez, Václav Snášel, Kusum Deep,
• Abstract summary: Review focuses on how machine learning (ML) techniques, combined with multi-source data fusion, enhance precision agriculture by improving predictive accuracy and decision-making.<n>This review bridges the gap between AI research and agricultural applications, offering a roadmap for researchers, industry professionals, and policymakers to harness information fusion and ML for advancing precision agriculture.
• Score: 6.060623947643556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning (ML) is a rapidly evolving technology with expanding applications across various fields. This paper presents a comprehensive survey of recent ML applications in agriculture for sustainability and efficiency. Existing reviews mainly focus on narrow subdomains or lack a fusion-driven perspectives. This study provides a combined analysis of ML applications in agriculture, structured around five key objectives: (i) Analyzing ML techniques across pre-harvesting, harvesting, and post-harvesting phases. (ii) Demonstrating how ML can be used with agricultural data and data fusion. (iii) Conducting a bibliometric and statistical analysis to reveal research trends and activity. (iv) Investigating real-world case studies of leading artificial intelligence (AI)-driven agricultural companies that use different types of multisensors and multisource data. (v) Compiling publicly available datasets to support ML model training. Going beyond existing previous reviews, this review focuses on how machine learning (ML) techniques, combined with multi-source data fusion (integrating remote sensing, IoT, and climate analytics), enhance precision agriculture by improving predictive accuracy and decision-making. Case studies and statistical insights illustrate the evolving landscape of AI driven smart farming, while future research directions also discusses challenges associated with data fusion for heterogeneous datasets. This review bridges the gap between AI research and agricultural applications, offering a roadmap for researchers, industry professionals, and policymakers to harness information fusion and ML for advancing precision agriculture.

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