ArticleSensors (Basel, Switzerland)2021
Edge Computing Driven Data Sensing Strategy in the Entire Crop Lifecycle for Smart Agriculture.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deterministic Edge-Controlled Precision Fertigation System with Spatial Task Scheduling and Hardware-Software Safety Interlock.Sensors (Basel, Switzerland) · 2026Article
- Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.Scientific reports · 2025Article
- Energy Aware Software Defined Network Model for Communication of Sensors Deployed in Precision Agriculture.Sensors (Basel, Switzerland) · 2023Article
- Study on the influence mechanism of adoption of smart agriculture technology behavior.Scientific reports · 2023Article
- At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives.Sensors (Basel, Switzerland) · 2023Review
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Authors and funding
2 authors.
Funding
Abstract
In the context of smart agriculture, high-value data sensing in the entire crop lifecycle is fundamental for realizing crop cultivation control. However, the existing data sensing methods are deficient regarding the sensing data value, poor data correlation, and high data collection cost. The main problem for data sensing over the entire crop lifecycle is how to sense high-value data according to crop growth stage at a low cost. To solve this problem, a data sensing framework was developed by combining edge computing with the Internet of Things, and a novel data sensing strategy for the entire crop lifecycle is proposed in this paper. The proposed strategy includes four phases. In the first phase, the crop growth stage is divided by Gath-Geva (GG) fuzzy clustering, and the key growth parameters corresponding to the growth stage are extracted. In the second phase, based on the current crop growth information, a prediction method of the current crop growth stage is constructed by using a Tkagi-Sugneo (T-S) fuzzy neural network. In the third phase, based on Deng's grey relational analysis method, the environmental sensing parameters of the corresponding crop growth stage are optimized. In the fourth phase, an adaptive sensing method of sensing nodes with effective sensing area constraints is established. Finally, based on the actual crop growth history data, the whole crop life cycle dataset is established to test the performance and prediction accuracy of the proposed method for crop growth stage division. Based on the historical data, the simulation data sensing environment is established. Then, the proposed algorithm is tested and compared with the traditional algorithms. The comparison results show that the proposed strategy can divide and predict a crop growth cycle with high accuracy. The proposed strategy can significantly reduce the sensing and data collection times and energy consumption and significantly improve the value of sensing data.
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Registered trials
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