Lighting conditions affect the growth and glucosinolate contents of Chinese kale leaves grown in an aeroponic plant factory

Chowdhury, M. A. H.; Gulandaz, Md Ashrafuzzaman; Islam, Sumaiya; Reza, Nasim; Ali, Mohammod; Islam, Nafiul; Park, Sang Un; Chung, Soo Young

doi:10.1007/s13580-022-00472-0

Cited by 5 publications

(1 citation statement)

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“…Blue light promotes the development of vegetative leaves as well as the contents of antioxidant compounds and the total glucosinolates accumulation, while red light wavelengths encourage budding and flowering. Alongside blue light, red light wavebands are considered one of the most important for photosynthesis and biomass growth [39][40][41]. A plant-specific balanced combination of red and blue light for optimal results is always essential.…”

Section: Introductionmentioning

confidence: 99%

Leaf Area Prediction of Pennywort Plants Grown in a Plant Factory Using Image Processing and an Artificial Neural Network

Reza,

Chowdhury,

Islam

et al. 2023

Horticulturae

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The leaf is a primary part of a plant, and examining the leaf area is crucial in understanding growth and plant physiology. Accurately estimating leaf area is key to this understanding. This study proposed a methodology for the non-destructive estimation of leaf area in pennywort plants using image processing and an artificial neural network (ANN) model. The image processing method involved a series of steps, including grayscale conversion, histogram equalization, binary masking, and region filling, achieving an accuracy of around 96.6%. The ANN model, trained with 70% of a dataset, exhibited high correlations of 97.1% in training and 96.6% in testing phases, with leaf length and width significantly impacting the model output. A comparative analysis revealed the superior performance of the ANN model over the image processing method, demonstrating higher R2 values (>0.99) and lower errors. Furthermore, it showed the impact of diverse LED light combinations and nutrient levels (electrical conductivity, EC) on pennywort plant growth, indicating that the R70:B30 LED light ratio with nutrient level 2 (2.0 dS·m−1) fostered the most favorable growth for pennywort plants. The non-destructive nature, simplicity, and speed of the ANN model in estimating leaf area based on easily obtainable measurements of length and width render it an accessible and accurate tool for plant growth assessment in controlled environments. This approach offers opportunities for future studies, tracking changes in leaf areas under varied growth conditions without harming the plant, thus enhancing precision in research.

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Section: Introductionmentioning

confidence: 99%