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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

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DRFC: An efficient cloud-based feature reduction and clustering algorithm for agricultural product and remote-sensing

Xiao Fu¹, Yuanyuan Xu²

¹College of Fine arts and Design, Heze University, Heze, China.

|March 25, 2026

Summary

Dynamic Resource Flow Control (DRFC) offers a scalable, resource-efficient solution for agricultural image analysis. This algorithm efficiently handles diverse imagery, outperforming deep learning models in constrained environments.

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Area of Science:

Agricultural Science
Computer Science
Data Science

Background:

Digital agriculture requires scalable solutions for diverse image types.
Deep learning models are accurate but resource-intensive for cloud deployment.

Purpose of the Study:

To introduce Dynamic Resource Flow Control (DRFC), an efficient algorithm for agricultural image analytics.
To address the limitations of deep learning models in resource-constrained environments.

Main Methods:

DRFC merges lightweight dimensionality reduction with active resource flow management.
The algorithm dynamically allocates cloud resources for heterogeneous agricultural imagery.
Tested on Fruits-360 and USDA CDL/BigEarthNet datasets for product and crop-level analysis.

Main Results:

DRFC achieved 97.8% accuracy and 97.4% F1-score on Fruits-360.
On USDA CDL/BigEarthNet, DRFC reached 92.6% accuracy with a 91.3 macro-F1 score.
DRFC demonstrated 2-3x greater efficiency in runtime and memory usage compared to baseline algorithms.

Conclusions:

DRFC is a scalable and computationally efficient solution for cloud-based agricultural image analytics.
The algorithm is particularly effective when large deep learning models are infeasible due to resource limitations.