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Related Experiment Video

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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Published on: February 2, 2019

Edge Computing Driven Data Sensing Strategy in the Entire Crop Lifecycle for Smart Agriculture.

Rihong Zhang¹, Xiaomin Li¹

¹College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China.

Sensors (Basel, Switzerland)

|November 27, 2021

Summary

A new framework combines edge computing and the Internet of Things for smart agriculture. This strategy optimizes crop data sensing across the entire lifecycle, reducing costs and improving data value.

Keywords:

agricultural internet of things crop lifecycle data sensing data value edge computing

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Area of Science:

Smart Agriculture
Agricultural Technology
Data Science

Background:

Effective crop cultivation control relies on high-value data sensing throughout the crop lifecycle.
Current sensing methods suffer from low data value, poor correlation, and high costs.
A key challenge is low-cost, high-value data sensing tailored to crop growth stages.

Purpose of the Study:

To develop a novel data sensing framework and strategy for the entire crop lifecycle in smart agriculture.
To address limitations of existing methods by reducing costs and enhancing sensing data value and correlation.
To enable precise crop cultivation control through accurate, stage-specific data acquisition.

Main Methods:

A framework integrating edge computing and the Internet of Things (IoT) was developed.
A four-phase strategy was proposed: Gath-Geva (GG) fuzzy clustering for growth stage division, Tkagi-Sugneo (T-S) fuzzy neural networks for stage prediction, Deng's grey relational analysis for environmental parameter optimization, and adaptive sensing node methods.
The approach was validated using historical crop growth data and simulation.

Main Results:

The proposed strategy accurately divides and predicts crop growth stages.
Significant reductions in sensing and data collection time and energy consumption were achieved.
The value of sensing data was substantially improved.

Conclusions:

The developed framework and strategy offer an effective solution for low-cost, high-value data sensing in smart agriculture.
The method enhances crop growth stage division and prediction accuracy.
This approach contributes to more efficient and precise crop cultivation management.