Ripening agents can accelerate the ripening of fruits and maintain a similar appearance to naturally ripe fruits, but the fruit flavor and quality will be changed compared to naturally ripe fruits. To find an efficient detection method to distinguish whether crab apples were artificial ripened, the naturally ripe and artificially ripe fruits were detected and analyzed using the electronic nose (e-nose) technique in this study. The fruit quality indexes of samples were determined by the traditional method as a reference. Significant differences were found between naturally ripe and artificially ripe fruits based on the analysis of soluble sugar content, titratable acidity content, sugar–acid ratio, soluble protein content, and soluble solids content. In addition, principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) analyses were performed on the electrical signals generated by the electronic nose sensor, respectively. The results showed that the RF is the best recognition algorithm for distinguishing which crab apples were naturally ripe or artificially ripe; the average recognition accuracy is 98.3%. On the other hand, the prediction models between the e-nose response data and fruit quality indexes were constructed by partial least squares regression (PLSR), which showed that the feature value of e-nose response curves extracted by wavelet transform was highly correlated with the quality indexes of fruits, the determination coefficients (R²) of regression models were higher than 0.91. The results demonstrated that the detection technology with an electronic nose could be used to test whether the fruit of the crab apple was artificially ripe, which is an economical and efficient method.

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