Best Researcher Award
Cuimin Sun
Guangxi University, China
Cuimin Sun is a researcher affiliated with Guangxi University whose scholarly record spans agricultural and biological sciences, computational prediction, plant nutrient assessment, microfluidics, and micro-extrusion technologies. The available bibliometric profile records 55 documents, 382 citations, and an h-index of 11, providing a quantitative basis for assessing research visibility and continuity. [1]
Abstract
Cuimin Sun’s research profile demonstrates interdisciplinary activity connecting agricultural intelligence, plant nutrient analysis, microfluidic systems, and advanced manufacturing. Recent publications include machine-learning approaches for maize and sugarcane nutrient assessment and engineering studies involving deformable droplets and concentrated silver paste. [2] [3] [4] These works indicate an applied research orientation supported by computational modeling, image-based analysis, and experimental validation.
Keywords
Agricultural sciences; biological sciences; machine learning; plant nutrient prediction; maize; sugarcane; microfluidics; micro-extrusion; image analysis; experimental validation.
Introduction
Research in agricultural and biological sciences increasingly integrates sensing, artificial intelligence, computational modeling, and experimental technologies. Sun’s recent publication record reflects this convergence. The reported studies address both agricultural decision-support problems and engineering methods that can contribute to precision-oriented scientific applications. The combination of quantitative modeling and experimentally grounded investigation provides a multidisciplinary basis for evaluating the researcher’s scholarly contributions.
Research Profile
Sun’s research profile includes artificial intelligence for crop nutrient assessment, particularly through image-based and attention-based neural networks. The SCBI-EfficientNetV2 study addresses regression prediction of nitrogen content in maize leaves, while WT-ResNet investigates nondestructive estimation of nitrogen, phosphorus, and potassium in sugarcane leaves. [3] [4] This work is complemented by studies in microfluidic separation and extrusion behavior, illustrating broader technical engagement with applied modeling and experimental systems.
Research Contributions
- Development of lightweight deep-learning approaches for agricultural nutrient prediction.
- Investigation of nondestructive plant nutrient assessment using leaf imagery.
- Research into centrifugal-field microfluidic separation of deformable droplets.
- Semi-theoretical modeling and experimental validation of extrusion swell behavior in concentrated silver paste.
Publications
Selected recent publications include Semi-Theoretical Modeling and Experimental Validation of the Extrusion Swell Ratio of Highly Concentrated Silver Paste in Micro-Extrusion, published in Micromachines in July 2026. [5] The February 2026 Agronomy article, SCBI-EfficientNetV2, reports a lightweight attention-based approach for maize nitrogen prediction. [3] Other recent studies address sugarcane nutrient prediction and deformable droplet separation in microfluidic systems. [4] [6]
Research Impact
The bibliometric record of 55 documents, 382 citations, and an h-index of 11 indicates an established level of scholarly dissemination and citation activity. [1] The thematic diversity of recent publications further suggests an ability to apply computational and experimental methods across agricultural and engineering contexts. Such breadth is relevant to research environments where interdisciplinary methods are increasingly used to address complex measurement and process-control problems.
Award Suitability
For the Best Researcher Award, Sun’s profile can be evaluated through publication activity, citation indicators, methodological diversity, and relevance to applied scientific research. The combination of agricultural machine learning, nondestructive crop assessment, and experimental engineering research provides evidence of sustained scholarly engagement. The documented metrics and selected publications offer measurable criteria for consideration within a research recognition framework.
Conclusion
Cuimin Sun presents a multidisciplinary research profile associated with Guangxi University and the Agricultural and Biological Sciences subject area. The available record combines measurable scholarly impact with research addressing crop nutrient prediction, artificial intelligence, microfluidics, and advanced manufacturing. These characteristics provide a substantive academic basis for consideration for the Best Researcher Award.
External Links
References
- Elsevier. (n.d.). Scopus author details: Cuimin Sun, Author ID 57155480000. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=57155480000
- Sun, C., et al. (2026). SCBI-EfficientNetV2: A Lightweight Attention-Based Network for Regression Prediction of Nitrogen Content in Maize Leaves. Agronomy, 16(5), 544.
https://doi.org/10.3390/agronomy16050544
- Sun, C., et al. (2025). WT-ResNet: A Non-Destructive Method for Determining the Nitrogen, Phosphorus, and Potassium Content of Sugarcane Leaves Based on Leaf Image. Agriculture, 15(16), 1752.
https://doi.org/10.3390/agriculture15161752
- Sun, C., et al. (2025). An Open-Type Crossflow Microfluidic Chip for Deformable Droplet Separation Driven by a Centrifugal Field. Micromachines, 16(7), 774.
https://doi.org/10.3390/mi16070774
- Sun, C., et al. (2026). Semi-Theoretical Modeling and Experimental Validation of the Extrusion Swell Ratio of Highly Concentrated Silver Paste in Micro-Extrusion. Micromachines, 17(7), 855.
https://doi.org/10.3390/mi17070855
- MDPI. (n.d.). Micromachines and Agriculture publication records. Multidisciplinary Digital Publishing Institute.
