Soybeans are essential for global agriculture as a major source of protein and oil, supporting food security, livestock feed, and renewable energy industries worldwide. Manipulating the light spectrum using LEDs presents a unique opportunity to optimize soybean growth in a controlled environment. In this study, we aimed to grow compact soybeans, accelerate flowering, increase seed production, and evaluate physiological variables. Two soybean varieties (CZ 75 70LL and S16‐14801C) were cultivated from seeds in controlled conditions (27°C/26°C, day/night; 68% relative humidity; 590 μmol mol
−1
CO
2
) in 11 L plastic pots containing peat‐moss substrate. A week after germination, the plants were exposed to one of four light‐spectrum treatments at a photon flux density of 700 μmol m
−2
s
−1
. These treatments had different percent photon flux ratios of blue (B: 400–500 nm), green (G: 500–600 nm), red (R: 600–700 nm), and far‐red (FR: 700–750 nm) wavelengths: (1) 22B:50G:26R:2FR (White; control), (2) 20B:80R, (3) 50B:50R, and (4) 40B:40R:20FR. Both varieties across all treatments reached the harvest stage at 121 ± 4 days, with all flowers being open and producing light‐yellow pollen. Higher levels of blue light in 50B:50R increased leaf transpiration, thereby reducing water‐use efficiency and photosynthetic efficiency. Seed yield assessments indicated that the 40B:40R:20FR treatment increased seed production, rising to 34% in seed number and 37% in seed weight, compared with the white control treatment. Morphological evaluations indicated that plants under 50B:50R and 40B:40R:20FR were 1.7–2.4 and 1.3–1.5‐fold shorter than plants grown under the white treatment. These findings confirm that the light spectrum can be tuned to reach specific objectives and optimize soybean growth and development in controlled environments.
Optimising light quality and photoperiod is a critical approach to improving crop growth, yield, and flowering in controlled-environment agriculture, where artificial lighting serves as the primary energy source for plant development. The present study evaluated the influence of different LED light spectra and photoper...
Obulreddy Sravani, A. Rao, C. Shanmukhi et al.· Journal of Advances in Biolo...· 0 citations
Purple celery (Apium graveolens L.) is a high-value functional vegetable rich in anthocyanins, but achieving a balanced optimization between biomass accumulation and nutritional quality in plant factories remains challenging due to species-specific spectral trade-offs, and current research lacks integrated light recipe...
Light spectral composition is an important environmental factor affecting strawberry growth and fruit production in controlled-environment agriculture. This study evaluated the effects of five light-emitting diode (LED) spectra on photosynthetic performance, vegetative growth, flowering, fruit production, and final bio...
Pornpailin Luengluetham, S. Duangsrisai· Horticulturae· 0 citations
Rice is central to global food security, and plant factories enable precise control of lighting conditions. However, genotype-specific responses to supplemental blue light across the full crop cycle remain poorly understood. Here, Nipponbare and CP511 were grown from transplanting to maturity under a green, red, and fa...
Xin Zhong, Dong Yu, Zong-Geng Li et al.· Agriculture· 0 citations
Background. In controlled-environment vegetable production, photoperiod is a key driver of plant growth and yield. The purpose of the study is to quantify photoperiodic responses in two Chinese leafy cabbage cultivars (Lastochka and Vesnyanka) under the same light spectrum and photon flux density. Materials and methods...
Mikhail D. Romanenko, L. L. Bondareva, I. G. Tarakanov· University proceedings Volga...· 0 citations
Soybean (
Glycine max
) seed composition is crucial for global food security and high-quality animal feed. Historic genetic selection and innovation have led to improved yield potential, but this has concurrently resulted in decreased seed quality. Biological nitrogen fixation is often not sufficient to meet the to...
M. Pandit, Anjan Timilsina, Susma Adhikari et al.· Frontiers in Sustainable Foo...· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.