ArticleScientific reports2017
Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Hyperspectral imaging meets 3D Gaussian Splatting: A novel approach beyond 3D plant morphology.Plant phenomics (Washington, D.C.) · 2026Article
- Drought tolerance classification using unmanned aerial systems based on RGB and multispectral data.Frontiers in plant science · 2026Article
- Temporal phenotypic variation of spinach root traits and its relation to shoot performance.Scientific reports · 2024Article
- Artificial intelligence in the water domain: Opportunities for responsible use.The Science of the total environment · 2021Review
- Roles of DNA Methylation in Cold Priming in Tartary Buckwheat.Frontiers in plant science · 2020Article
- A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted Muskmelon.Sensors (Basel, Switzerland) · 2019Article
- Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning forSensors (Basel, Switzerland) · 2019Article
- Non-destructive monitoring of netted muskmelon quality based on its external phenotype using Random Forest.PloS one · 2019Article
- Crop Phenomics: Current Status and Perspectives.Frontiers in plant science · 2019Review
- Optical Flow-Based Analysis of the Relationships between Leaf Wilting and Stem Diameter Variations in Tomato Plants.Plant phenomics (Washington, D.C.) · 2019Article
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Authors and funding
5 authors.
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Abstract
Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.
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