Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques

Abstract

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.

Plant phenotyping relevance

植物フェノタイピングと機械学習を統合した水分状態識別法を開発・検証しており、フェノタイプ抽出とモデル性能評価が研究の中心である。

Code and data availability

The article describes phenotypic trait extraction from LemnaTec Scanalyzer 3D images and machine learning models (RF, NN, SVM) built in R, but no public phenotype dataset, image repository, author code, or trained model deposit is mentioned. MetaboAnalyst is only a generic third-party web tool, and the license/ORCID/Mt

No evidence-backed public reproduction asset is currently recorded.