ArticleFrontiers in plant science2024
Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- Intelligent Gas Sensors: From Mechanism to Applications.Sensors (Basel, Switzerland) · 2025Review
- Development of a Low-Cost Sensor System for Accurate Soil Assessment and Biological Activity Profiling.Micromachines · 2024Article
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
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Abstract
The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean. This experiment was conducted for 22 days, observing the stages of plant growth during this period. This chamber is embedded with relative humidity [RH (%)], temperature (°C), and CO
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Registered trials
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