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.

At present, the electronic nose has became a new technology for the rapid detection of pesticides. However, the technique may misidentify them for samples that have not been involved in training. Therefore, a hybrid model based on unsupervised and supervised learning was proposed for the first time in this paper. The model divided the detection process of soil pesticide residues into two steps: (1) an unsupervised machine learning method was used to identify whether the soil was contaminated with pesticides; (2) when the soil was contaminated with pesticides, a supervised classifier was further used to predict the types of pesticides in the soil. The experimental results showed that the model had a recognition accuracy of 99.3% and 99.27% for whether the soil was contaminated with pesticides and the pesticide type of the contaminated soil, respectively, with a detection time of 0.03 s. The results revealed that the proposed hybrid model can quickly and comprehensively reflect the soil information’s status.

Buckwheat microgreens are rich in nutrients and have a unique flavor that is favored by consumers. The light environment is closely related to the growth and development of the plant. In order to study the effects of treatments with different combinations of red and blue light on the edible organ morphology and nutritional quality of buckwheat microgreens, five experimental treatments were designed, with energy ratios of red light to blue light of 5:1 (R5B1), 3:1 (R3B1), 1:1 (R1B1), 1:3 (R1B3) and 1:5 (R1B5), respectively, and a white light treatment used as the control (CK). The results showed that different combination treatments of red and blue light had obvious effects on the growth of buckwheat microgreens. The hypocotyl length and main root length of buckwheat microgreens treated with a high proportion of red light (R5B1) were obviously higher than those of other treatment designs. However, contents of soluble protein, chlorophyll, rutin and total flavonoids in buckwheat microgreens showed an increasing trend with an increase in the proportion of blue light. Considering the fresh weight, dry weight and quality indexes of the edible organ, the combination of red light and blue light with a ratio of 1:1 was most suitable for buckwheat microgreen production. The results could provide a reference for the production of buckwheat microgreens.

The bacterial communities in the rhizosphere soil of plants facilitate the cycling of nutrient elements in the rhizosphere and regulate soil fertility. By analyzing the microecological structure of rhizosphere soil surrounding wild celery, we can provide a basis for the bionic cultivation of wild celery. In this experiment, rhizosphere soil samples from various wild celery varieties in Jilin Province were used as test materials, and high-throughput sequencing was employed to analyze and compare the rhizosphere bacterial community structures of these samples. After screening and removing chimeric sequences, a total of 1,020,108 high-quality sequences were obtained. Species classification results revealed that these bacteria encompassed 60 phyla, 183 classes, 431 orders, 702 families, and 1619 genera. There were certain differences in the composition and structure of bacterial communities among different rhizosphere soil samples. According to the richness indices, the performance order among samples was Tonghua water celery > Linjiang large-leaf celery > Linjiang old mountain celery > Tonghua large-leaf celery > Jiangyuan large-leaf celery > Tonghua old mountain celery > Linjiang water celery > artificially cultivated wild large-leaf celery > Huadian large-leaf celery > Huadian small-leaf celery > Dongfeng water celery > Jiangyuan old mountain celery. Among all bacterial communities, Pseudomonadota (37.79–22.48%) had the highest relative abundance across different regions, followed by Acidobacteriota (17.97–13.51%). RDA analysis indicated that soil pH, available phosphorus, available potassium, and alkali-hydrolyzable nitrogen in the celery rhizosphere were the primary factors influencing changes in bacterial communities. Based on the experimental analysis, it was demonstrated that there were differences in rhizosphere soil bacterial community diversity and composition among Tonghua large-leaf celery, Linjiang large-leaf celery, Jiangyuan large-leaf celery, Huadian large-leaf celery, Tonghua old mountain celery, Linjiang old mountain celery, Jiangyuan old mountain celery, Tonghua water celery, Linjiang water celery, Dongfeng water celery, Huadian small-leaf celery, and artificially cultivated wild large-leaf celery in Jilin Province.

To investigate the effects of varying degrees of shading on the photosynthetic characteristics of Spuriopinnella brachycarpa (Kom.) Kitag. experiments were conducted under five shading treatments: 0% (full sunlight), 20%, 40%, 60%, and 80%. The results demonstrated that shading significantly influenced plant growth and photosynthesis. Plant height, stem diameter, and leaf area in the control group (0% shading) were significantly greater than those in shaded treatments, although a 40% shading treatment notably increased the aboveground harvestable biomass, demonstrating greater potential for economic yield compared to the control. All shading treatments significantly enhanced the net photosynthetic rate of S. brachycarpa compared to the control; however, excessive shading (i.e., 80% shading) led to a reduction in the net photosynthetic rate, falling below that observed under full sunlight. The 40% shading treatment yielded the most substantial improvement in net photosynthetic rate. Shading also significantly enhanced the maximum quantum yield of PSII, facilitating improved use of light energy for photosynthesis while reducing energy dissipation as heat. Additionally, antioxidant activity in shaded plants was markedly higher than in the control, with the highest levels observed under 40% shading. These findings suggest that moderate shading, particularly at 40%, is optimal for improving photosynthetic efficiency, light energy utilization, and antioxidant activity in S. brachycarpa

Particulate matter affects both the light environment and air quality in greenhouses, obstructing normal gas exchange and hindering efficient physiological activities such as photosynthesis. This study focused on young Chinese cabbage (Brassica rapa L. Chinensis Group) in a greenhouse at harvest time, monitoring and comparing hyperspectral information, net photosynthetic rate, and microscopic leaf structure under two conditions: a quantitative artificial particulate matter environment and a healthy environment. Based on microscopic results combined with spectral responses and changes in photosynthetic physiological information, it is believed that particulate matter enters plant cells through stomata. Through retention and transport pathways, it disrupts the membrane structure, organelles, and other components of plant cells, resulting in adverse effects on the plant’s physiological functions. The study analyzed the mechanisms by which particulate matter influences the photosynthesis, spectral characteristics, and physiological responses of young Chinese cabbage. Physiological Reflectance Index (PRI), Modified Chlorophyll Absorption Ratio Index (MCARI), spectral red-edge position (λr), and spectral sensitive bands were used as spectral feature variables. Through cubic polynomial and 24 combinations of spectral preprocessing and modeling methods, an inversion model of spectral features and net photosynthetic rate was established. The optimal combination of spectral preprocessing and modeling methods was finally selected as SG + SD + PLS + MSC, which consists of Savitzky-Golay smooth (SG), second derivative (SD), partial least squares (PLS), and multiplicative scatter correction (MSC). The coefficient of determination (R²) of the model is 0.9513. The results indicate that particulate matter affects plant photosynthesis. The SG + SD + PLS + MSC combination method is relatively advantageous for processing the photosynthetic spectral physiological information of plants under the influence of particulate matter. The results of this study will deepen the understanding of the mechanisms by which particulate matter affects plants and provide a reference for the physiological information inversion of greenhouse vegetables under particulate matter pollution.