During the overwintering production of lettuce in solar greenhouses, there exist a short duration of sunlight and low light intensity, which are detrimental to the growth and development of lettuce. Supplemental lighting is an effective solution to this issue. This study aims to explore the influence of adding different wavelengths of red light to white LEDs for supplemental lighting on the growth and photosynthesis of lettuce leaves in solar greenhouses. Four experimental zones were established, namely white LED + 630 nm (T1), white LED + 660 nm (T2), white LED + 690 nm (T3), and no supplemental lighting (CK). The results indicate that supplemental lighting significantly increased the plant height, leaf area, biomass, and root indices. The chlorophyll content measurements showed higher photosynthetic pigment levels in the treated plants, enhancing the net photosynthesis rate (Pn). Thus, the combination of red and white light provides a more comprehensive spectrum and enhances the photosynthetic capacity of plant leaves. Simultaneously, under supplemental lighting, the plant fluorescence parameters Y(II), Fv/Fm, qP, and ETR were significantly elevated. It was found from the chlorophyll fluorescence frequency distribution graph that the leftward shift in Y(II) in the control group (CK) indicated that it was in a state of weak light stress, but supplemental lighting effectively ameliorated this stress environment. Among the types of supplemental lighting, the combination of white LEDs with 660 nm red light provides the most significant improvement in the growth and photosynthetic characteristics of lettuce under winter greenhouse conditions, and this combination holds great application potential in winter greenhouse lettuce production.

Tomato (Solanum lycopersicum L. cv. Zhongza NO. 9) was used as the experimental material to investigate the effects of nocturnal LED supplemental light with the photosynthetic photon flux density (PPFD) of 100, 200, 300 μmol·m⁻²·s⁻¹, and the light time of 1, 2 h on the seedling quality in a controlled environment, with seedlings without nocturnal supplemental lighting serving as the control. The results demonstrate that an increase in PPFD at night progressively enhances the plant height and leaf number of tomato seedlings, while stem diameter and leaf area initially increase and subsequently decrease. Although light time and light period-of-time at night did not significantly affect seedling morphology, PPFD and light time notably influenced chlorophyll content and net photosynthetic rate. An optimal lighting energy amount at night augmented photosynthetic capacity. However, excessive PPFD induced photoinhibition in the leaves. Additionally, appropriate nocturnal LED supplemental lighting significantly improved the antioxidant capacity of the seedlings, increased proline content, reduced malondialdehyde content, and bolstered the self-protection mechanisms of the seedlings against nocturnal light stress. Both the PPFD and light time at night promoted biomass accumulation in tomato seedlings. Specifically, when supplemental lighting was applied for 2 h at an intensity of 200 μmol·m⁻²·s⁻¹, both the fresh and dry weights of the shoot and root significantly increased, and the seedling health index was highest. Therefore, appropriate nocturnal LED supplemental lighting positively impacts the health index and photosynthate accumulation of tomato seedlings, but controlling PPFD is essential to avoid photoinhibition.

High-throughput measurements of phenotypic parameters in plants generate substantial data, significantly improving agricultural production optimization and breeding efficiency. However, these measurements face several challenges, including environmental variability, sample heterogeneity, and complex data processing. This study presents a method applicable to stem and leaf segmentation and parameter extraction during the tomato seedling stage, utilizing three-dimensional point clouds. Focusing on tomato seedlings, data was captured using a depth camera to create point cloud models. The RANSAC, region-growing, and greedy projection triangulation algorithms were employed to extract phenotypic parameters such as plant height, stem thickness, leaf area, and leaf inclination angle. The results showed strong correlations, with coefficients of determination for manually measured parameters versus extracted 3D point cloud parameters being 0.920, 0.725, 0.905, and 0.917, respectively. The root-mean-square errors were 0.643, 0.168, 1.921, and 4.513, with absolute percentage errors of 3.804%, 5.052%, 5.509%, and 7.332%. These findings highlight a robust relationship between manual measurements and the extracted parameters, establishing a technical foundation for high-throughput automated phenotypic parameter extraction in tomato seedlings.

Potassium is one of the indispensable nutrient elements for plant growth, fruit development, and yield. The research and application of potassium nutrition diagnosis technology is the premise of scientific potassium management. However, potassium deficiency in tomato leaves, from vegetative to reproductive growth, is not easy to diagnose. To alleviate this problem, this paper proposes a suitable method of supplying potassium to tomatoes via a nutrient solution and diagnosing potassium abundance and deficiency through diagnosis methods based on ecological morphology, biological accumulation, and the photosynthetic characteristics of tomato plants. The relationship between the ecological morphology and biomass accumulation of tomatoes cultivated in the nutrient solution with potassium supply levels of 1, 4, 8, and 16 mmol/L is also discussed, and the potassium supply in the nutrient solution was studied 21 days after transplanting. The results showed that there was a significant quadratic correlation between the potassium supply in the nutrient solution and plant height and biomass accumulation, respectively. The most suitable level of potassium supply via the nutrient solution was deemed to be 10~13 mmol/L. However, if irreversible damage or severe stress to tomato plants has occurred because of potassium deficiency, there will be serious differences in the growth status of plants, and the diagnosis results will deviate greatly. In addition, the photosynthetic induction characteristics responding to the dark–light conversion of tomato leaves with potassium contents of 0.9%, 2.1%, 3.1%, and 3.3% cultivated with potassium supply amounts of 1, 4, 8, and 16 mmol/L in the nutrient solution were investigated. The results showed that tomato leaves with potassium contents of 3.1% and 3.3% had a more rapid response to dark–light conversion and higher first-order derivatives of net photosynthetic rate compared to those with potassium contents of 0.9% and 2.1%, but the first-order derivative of intercellular CO₂ concentration showed an opposite trend. Additionally, a quadratic correlation between leaf potassium content and CO₂ assimilation during 5 min of photosynthetic induction was established (R² > 0.99). According to this correlation, the suitable leaf potassium content was estimated to be 2.3~2.7%, similar to that of tomatoes cultured in the nutrient solution with a 4~8 mmol/L potassium supply. Therefore, this method can realize the rapid, non-destructive, and real-time detection of potassium content in tomato leaves based on a portable photosynthetic measurement system by establishing the relationship between leaf potassium content and net CO₂ assimilation during the photosynthetic induction period, therefore helping to avoid the irreversible damage caused by potassium deficiency at the later stages of plant cultivation and providing technical support for the precise fertilization of potassium in actual cultivation.

The objective of this work was to monitor the growth status of pepper and provide precise guidance on fertilization through non-destructive detection methods for chlorophyll content based on spectral transmittance. The analysis of the narrower red-edge spectral region (680-760 nm) reduced the requirements for light sources and light detection sensors, and provided a simpler and more accurate method of data acquisition for the process of developing instruments for estimating chlorophyll content in leaves. The red-edge region of spectral transmittance was demonstrated to be closely related to chlorophyll content. Regression models for estimating chlorophyll content with seven different methods were developed using the four red-edge parameters extracted from the red-edge region. The problems of multicollinearity of red-edge parameters and errors in model coefficients were solved by the ridge regression method in the process of building a multivariate regression model. The results indicated that the ridge regression method reduces the errors of the model coefficients and constant terms while improving the detection accuracy, thus the ridge regression model could estimate the leaf chlorophyll content more accurately and repeatedly. Keywords: pepper leaf, chlorophyll content, red-edge parameters, ridge regression DOI: 10.25165/j.ijabe.20221505.7350 Citation: Huang S, Wu Y, Wang Q L, Liu J L, Han Q Y, Wang J F. Estimation of chlorophyll content in pepper leaves using spectral transmittance red-edge parameters. Int J Agric & Biol Eng, 2022; 15(5): 85–90.

To enhance the transplants' growth and reduce energy use efficiency, Eggplant (Solanum melongena L.) transplants (cv. Jingqie 21) were cultivated in a plant factory laboratory under different LED light spectrums. The experimental treatments included white plus blue LED lights (R: B=0.5, WB0.5), white LED lights (R: B=0.9, W0.9), white plus red LED lights (R: B=2.7, WR2.7), white plus red plus UV lights (R: B=3.8, WRUV3.8), and red plus blue plus green LED lights (R: B=5.4, RBG5.4). The transplants were grown for 30 d under a light intensity of 250 μmol/m2·s and a photoperiod of 16 h/d. The morphological indicators and biomass accumulation of eggplant transplants were significantly higher in the W0.9 treatment compared to the other experimental treatments. The photosynthetic quantum yield in the W0.9 treatment exhibited an increase of over 22% compared to that in the WR2.7 treatment. The shoot dry weight of the W0.9 treatment reached (381±41) mg/plant and the leaf area was (113.3±8.9) cm2, indicating a higher health index compared to the other treatments. However, there were no significant differences in the net photosynthetic rate of the leaves among all treatments. The energy yield (EY) of the W0.9 treatment was (37.7±1.8) g/kW·h, which was higher than others. Therefore, considering the high quality of transplants and the maximization of energy use efficiency, the LED light spectrum in the eggplant transplants production was recommended to the white LED light with an R: B ratio of 0.9. Keywords: eggplant transplants, LED light spectrum, growth, energy use efficiency DOI: Citation: Yang H, Wang T, Ji F, Zhou Q, Wang J F. Effects of LED light spectrum on the growth and energy use efficiency of eggplant transplants. Int J Agric & Biol Eng, 2023; 16(3): 23–29.