Urban farming in indoor settings: Nitrate limits compliance check of leafy green vegetables under LED lighting

Graf, Martine; Stangl, Rosemarie; Hood‐Nowotny, Rebecca; Kodym, A.

doi:10.17660/ejhs.2020/85.5.3

Cited by 3 publications

(1 citation statement)

References 57 publications

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“…Enhancing red and green spectral ranges led to decreased nitrate levels. Related to green spectral range, this result confirms the observations of Gräf et al (2020) [38], presenting lower nitrate levels in cabbage, pak choi, and rocket using RGB treatment instead of RB treatment. As a possible explanation, results of Bian et al ( 2018) indicated a promotion of nitrate reductase and nitrite reductase-related gene expression, as well as higher activities of enzymes involved in nitrate assimilation [39].…”

Section: Discussionsupporting

confidence: 90%

Effect of Light Intensity and Spectra on Inorganic Constituents in Vietnamese Coriander (Persicaria odorata (Lour.) Soják)

et al. 2023

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With the aim of optimizing resources in regional production of nutritive valuable leafy vegetables, this study was conducted to obtain more knowledge regarding the interdependencies between light conditions and accumulation of inorganic constituents. The test plant, P. odorata, was cultivated in a climate chamber with fluorescent tubes as the main light source and daylight integrals (DLI) varying between 4.68 and 9.06 mol·m−2·day−1. The average DLI in greenhouse experiments was 41.55 mol·m−2·day−1. Light conditions were modified using additional LEDs (443, 515, and 629 nm) or by covering the plants with photoselective plastic films, leading to a lower DLI and modified light spectrum, especially by reducing the green spectral range. Contents of nitrate, phosphorus, potassium, magnesium, calcium, and iron, biomass accumulation, and water content were analyzed. In terms of particular light modifications, additional green (515 nm) and red (629 nm) LEDs showed reducing effects on nitrate content at both cultivation locations. Other inorganic constituents were affected differently depending on cultivation location. However, the calculation of average partial correlation coefficients enabled a more general statement. Increasing DLI correlated positively with contents of magnesium, nitrate, and potassium but negatively with contents of calcium and iron. Additionally, nitrate content correlated positively with the spectral range of 651–700 nm but negatively with the R:FR ratio. Consequently, a general recommendation related to the light conditions is not possible, as inorganic constituents were affected differently. Nevertheless, as the nitrate content in leafy vegetables is of high concern and was reduced by higher shares of green or red light, this might be one way to regulate nitrate content in leafy vegetables.

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Section: Discussionsupporting

confidence: 90%

Effect of Light Intensity and Spectra on Inorganic Constituents in Vietnamese Coriander (Persicaria odorata (Lour.) Soják)

et al. 2023

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Novel strategies for controlling nitrite content in prepared dishes: Current status, potential benefits, limitations and future challenges

Tang

Zhang

Law

et al. 2023

Food Research International

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Indoor Plant Soil-Plant Analysis Development (SPAD) Prediction Based on Multispectral Indices and Soil Electroconductivity: A Deep Learning Approach

Radočaj,

Rapčan,

Jurišić

2023

Horticulturae

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Leaf Soil-Plant Analysis Development (SPAD) prediction is a crucial measure of plant health and is essential for optimizing indoor plant management. The deep learning methods offer advanced tools for precise evaluations but their adaptation to the heterogeneous indoor plant ecosystem presents distinct challenges. This study assesses how accurately deep neural network (DNN) predicts SPAD values in leaves on indoor plants when compared to well-established machine learning techniques, including Random Forest (RF) and Extreme Gradient Boosting (XGB). The covariates for prediction were based on low-cost multispectral and soil electro-conductivity (EC) sensors, enabling a non-destructive sensing approach. The study also strongly emphasized multicollinearity analysis quantified by the Variance Inflation Factor (VIF) and two independent indices, as well as its effect on prediction accuracy using deep and machine learning methods. DNN resulted in higher accuracy to RF and XGB, also performing better using filtered data after multicollinearity analysis based on the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) (R2 = 0.589, RMSE = 11.68, MAE = 9.52) in comparison to using all input covariates (R2 = 0.476, RMSE = 12.90, MAE = 10.94). Overall, DNN was proven as a more accurate prediction method than the conventional machine learning approach for the prediction of leaf SPAD values in indoor plants, despite using heterogenous plant types and input covariates.

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Urban farming in indoor settings: Nitrate limits compliance check of leafy green vegetables under LED lighting

Cited by 3 publications

References 57 publications

Effect of Light Intensity and Spectra on Inorganic Constituents in Vietnamese Coriander (Persicaria odorata (Lour.) Soják)

Effect of Light Intensity and Spectra on Inorganic Constituents in Vietnamese Coriander (Persicaria odorata (Lour.) Soják)

Novel strategies for controlling nitrite content in prepared dishes: Current status, potential benefits, limitations and future challenges

Indoor Plant Soil-Plant Analysis Development (SPAD) Prediction Based on Multispectral Indices and Soil Electroconductivity: A Deep Learning Approach

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