Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".
Deadline for manuscript submissions: 20 March 2025 | Viewed by 13
Special Issue Editors
Interests: water quality; hydrology; nutrient management; climate change
Special Issues, Collections and Topics in MDPI journals
Interests: water quality; hydrology; soil erosion; sediment transport
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This journal’s Special Issue entitled “Advanced Agricultural Technologies: Monitoring, Modeling, and Machine Learning Techniques” seeks the most recent works exploring the strategic role of monitoring, modeling, and machine learning (ML) techniques in modern agriculture. The agricultural sector is undergoing a profound transformation with the integration of advanced technologies aimed at enhancing productivity, sustainability, and resilience. Advanced monitoring systems, including IoT sensors and remote sensing technologies, enable real-time data collection on various environmental and crop-specific parameters. These data are instrumental in developing predictive models that simulate and optimize agricultural processes, leading to more precise resource management. Machine learning techniques further enhance these models by analyzing vast datasets to uncover patterns, forecast outcomes, and support decision-making processes. Therefore, this convergence of technologies offers the potential to revolutionize traditional farming practices, driving efficiencies and reducing environmental impacts.
The scope of this Special Issue includes the latest developments in advanced monitoring systems, such as Internet of Things (IoT) sensors, remote sensing technologies, and other innovative tools that enable real-time data collection on various environmental and agricultural parameters. The journal also gives significant focus to the application of various agricultural and hydrological models to simulate agricultural processes, optimize resource use, and enhance decision-making in sustainable agriculture management plans. The journal also places a strong emphasis on the application of machine learning techniques, which can analyze vast datasets to identify patterns, predict outcomes, and support informed decision-making in agriculture.
Dr. Soonho Hwang
Dr. Rabin Bhattarai
Guest Editors
Manuscript Submission Information
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Keywords
- irrigation
- drainage
- nutrient
- water quality
- agricultural modeling
- hydrological modeling
- machine learning
- remote sensing
- uncertainty assessment
- sustainable agriculture
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