A high efficiency CO2 concentration interval optimization method for lettuce growth.

The decline in carbon fertilization effects has shifted scientific focus toward the efficient and suitable regulation of CO2 concentration ([CO2]) for plant growth. In this study, the rapid A/CO2 response curve (RAC) data of lettuce were analyzed statistically under nine photosynthetic photon flux densities (PPFDs) and four temperatures. An efficient CO2 supplementation interval acquisition method based on the frequency distribution characteristics of RACs was proposed. The characteristic subsections of jumping were obtained depending on the frequency distribution of RACs. The cumulative contribution rate (CCR) of the characteristic subsections were >97 %, which showed the efficiency of the method. Additionally, U-chord curvature theory was used to simultaneously obtain the optimal regulated [CO2] for the same RAC curves, and the results showed that the [CO2] obtained by U-chord length were all within the interval obtained by the method, which proved the rationality of the method. The [CO2] interval supplement improved the daily CO2 exchange rate by 20.27 % and 21.64 % at 150 and 200 µmol·m-2·s-1, and increased the lettuce fresh biomass by 26.78 % at 150 µmol·m-2·s-1. Based on the interval of [CO2] efficient utilization regulation at various temperatures and PPFDs, a genetic algorithm-support vector regression model was built with R2 of the model was >0.84 and the root mean square error was <35.2256 µmol·mol-1. In conclusion, the [CO2] interval obtained by this method has a positive effect on lettuce growth. This work provides a new method for obtaining high-efficiency supplementary concentration of CO2 during the growth of lettuce.

Modification of the effect of maturity variation on nondestructive detection of apple quality based on the compensation model.

In this study, the effect of maturity variation on the prediction of the soluble solids content (SSC) and firmness of apples was determined using visible and near-infrared spectroscopy. In 2018, 520 apples from six ripening stages were collected. The single maturity model and multi-maturity model of SSC and firmness were established using partial least-squares regression. Apples at the same and different maturity stages were used to verify the developed model. Whereas the single maturity model was affected by maturity variation, the multi-maturity model could accurately predict the SSC and firmness of apples at different maturity stages. The multi-maturity model developed based on six maturity calibration sets had the best predictive performance. The root mean square error of prediction (RMSEP) of SSC and firmness was 0.614-0.802 °Brix and 0.402-0.650 kg/cm2, respectively. The long-term performance of the optimal multi-maturity model was evaluated using validation sets. The predictive performance was decreased and the RMSEP increased when the model was used to predict the SSC and firmness of apples in different seasons. The predictive performance of the model was improved after slope/bias (S/B) correction, and the RMSEP of SSC and firmness decreased to 0.405-0.587°Brix and 0.518-0.628 kg/cm2 respectively. Overall, the multi-maturity model eliminated the effect of maturity variation, and the multi-maturity model coupled with S/B correction permitted the rapid and accurate detection of the SSC and firmness of apples at different maturity stages and in different seasons.

Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion.

The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in the experiment are preprocessed by Gaussian filtering, binarization, and morphological methods. Then, a total of 40 features are extracted, including size and shape features, HSV and RGB color features, and texture features. Through BP neural network, extreme learning machine (ELM), and support vector machine (SVM) models, the importance of color, texture, and fusion features for the classification of the main roots of Panax notoginseng is verified. Among the three models, the SVM model performs the best, achieving an accuracy of 92.037% on the prediction set. Next, iterative retaining information variables (IRIVs), variable iterative space shrinkage approach (VISSA), and stepwise regression analysis (SRA) are used to reduce the dimension of all the features. Finally, a traditional machine learning SVM model based on feature selection and a deep learning model based on semantic segmentation are established. With the model size of only 125 kb and the training time of 3.4 s, the IRIV-SVM model achieves an accuracy of 95.370% on the test set, so IRIV-SVM is selected as the main root classification model for Panax notoginseng. After being optimized by the gray wolf optimizer, the IRIV-GWO-SVM model achieves the highest classification accuracy of 98.704% on the test set. The study results of this paper provide a basis for developing online classification methods of Panax notoginseng with different grades in actual production.

Photosynthetic rate prediction model of newborn leaves verified by core fluorescence parameters.

Due to the imperfect development of the photosynthetic apparatus of the newborn leaves of the canopy, the photosynthesis ability is insufficient, and the photosynthesis intensity is not only related to the external environmental factors, but also significantly related to the internal mechanism characteristics of the leaves. Light suppression and even light destruction are likely to occur when there is too much external light. Therefore, focus on the newborn leaves of the canopy, the accurate construction of photosynthetic rate prediction model based on environmental factor analysis and fluorescence mechanism characteristic analysis has become a key problem to be solved in facility agriculture. According to the above problems, a photosynthetic rate prediction model of newborn leaves in canopy of cucumber was proposed. The multi-factorial experiment was designed to obtain the multi-slice large-sample data of photosynthetic and fluorescence of newborn leaves. The correlation analysis method was used to obtain the main environmental impact factors as model inputs, and core chlorophyll fluorescence parameters was used for auxiliary verification. The best modeling method PSO-BP neural network was used to construct the newborn leaf photosynthetic rate prediction model. The validation results show that the net photosynthetic rate under different environmental factors of cucumber canopy leaves can be accurately predicted. The coefficient of determination between the measured values and the predicted values of photosynthetic rate was 0.9947 and the root mean square error was 0.8787. Meanwhile, combined with the core fluorescence parameters to assist the verification, it was found that the fluorescence parameters can accurately characterize crop photosynthesis. Therefore, this study is of great significance for improving the precision of light environment regulation for new leaf of facility crops.

Effects of MreB paralogs on poly-γ-glutamic acid synthesis and cell morphology in Bacillus amyloliquefaciens.

Actin-like MreB paralogs play important roles in cell shape maintenance, cell wall synthesis and the regulation of the D,L-endopeptidases, CwlO and LytE. The gram-positive bacteria, Bacillus amyloliquefaciens LL3, is a poly-γ-glutamic acid (γ-PGA) producing strain that contains three MreB paralogs: MreB, Mbl and MreBH. In B. amyloliquefaciens, CwlO and LytE can degrade γ-PGA. In this study, we aimed to test the hypothesis that modulating transcript levels of MreB paralogs would alter the synthesis and degradation of γ-PGA. The results showed that overexpression or inhibition of MreB, Mbl or MreBH had distinct effects on cell morphology and the molecular weight of the γ-PGA products. In fermentation medium, cells of mreB inhibition mutant were 50.2% longer than LL3, and the γ-PGA titer increased by 55.7%. However, changing the expression level of mbl showed only slight effects on the morphology, γ-PGA molecular weight and titer. In the mreBH inhibition mutant, γ-PGA production and its molecular weight increased by 56.7% and 19.4%, respectively. These results confirmed our hypothesis that suppressing the expression of MreB paralogs might reduce γ-PGA degradation, and that improving the cell size could strengthen γ-PGA synthesis. This is the first report of enhanced γ-PGA production via suppression of actin-like MreB paralogs.