To find an electrical conductivity (EC) in the nutrient solution used for pakchoi (Brassica campestris L. ssp. Chinensis) cultivation that optimizes the plant’s physiology, growth, and quality, we conducted an experiment with eight EC treatments (from EC0 to EC9.6) in a hydroponic production system (i.e. soilless culture) under greenhouse condition in Shanghai, China. Plants biomass production, leaf photosynthesis, vegetable quality variables, tissue nitrate and nitrite contents, and antioxidant enzyme activities were measured. The results showed that very high (EC9.6) or low EC (EC0-0.6) treatments clearly decreased plants fresh weight (FW) and dry weight (DW), leaf size, leaf water content, leaf net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), and taste score. Nitrite content, and antioxidant enzyme activities were low in medium EC treatments (EC1.8 and EC2.4), but high in very high or low EC treatments. Leaf relative chlorophyll, ascorbic acid, and nitrate contents increased gradually from low EC to high EC treatments, while crude fiber and soluble sugar contents decreased. Based on growth and quality criteria, the optimal EC treatment would be EC1.8 or EC2.4 for pakchoi in the hydroponic production system. Too high or too low EC would induce nutrient stress, enhance plant antioxidant enzyme activities, and suppress pakchoi growth and quality.

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Purpose To understand which environmental factors influence the distribution and ecological functions of bacteria in agricultural soil. Method A broad range of farmland soils was sampled from 206 locations in Jilin province, China. We used 16S rRNA genebased Illumina HiSeq sequencing to estimated soil bacterial community structure and functions. Result The dominant taxa in terms of abundance were found to be, Actinobacteria, Acidobacteria, Gemmatimonadetes, Chloroflexi, and Proteobacteria. Bacterial communities were dominantly affected by soil pH, whereas soil organic carbon did not have a significant influence on bacterial communities. Soil pH was significantly positively correlated with bacterial operational taxonomic unit abundance and soil bacterial α-diversity (P<0.05) spatially rather than with soil nutrients. Bacterial functions were estimated using FAPROTAX, and the relative abundance of anaerobic and aerobic chemoheterotrophs, and nitrifying bacteria was 27.66%, 26.14%, and 6.87%, respectively, of the total bacterial community. Generally, the results indicate that soil pH is more important than nutrients in shaping bacterial communities in agricultural soils, including their ecological functions and biogeographic distribution.

Lettuce is an important leafy vegetable that represents a significant dietary source of antioxidants and bioactive compounds. However, the levels of metabolites in different lettuce cultivars are poorly characterized. In this study, we used combined GC × GC-TOF/MS and UPLC-IMS-QTOF/MS to detect and relatively quantify metabolites in 30 lettuce cultivars representing large genetic diversity. Comparison with online databases, the published literature, standards as well using collision cross-section values enabled putative identification of 171 metabolites. Sixteen of these 171 metabolites (including phenolic acid derivatives, glycosylated flavonoids, and one iridoid) were present at significantly different levels in leaf and head type lettuces, which suggested the significant metabolomic variations between the leaf and head types of lettuce are related to secondary metabolism. A combination of the results and metabolic network analysis techniques suggested that leaf and head type lettuces contain not only different levels of metabolites but also have significant variations in the corresponding associated metabolic networks. The novel lettuce metabolite library and novel non-targeted metabolomics strategy devised in this study could be used to further characterize metabolic variations between lettuce cultivars or other plants. Moreover, the findings of this study provide important insight into metabolic adaptations due to natural and human selection, which could stimulate further research to potentially improve lettuce quality, yield, and nutritional value.

Monitoring plant nitrogen (N) in a timely way and accurately is critical for precision fertilization. The imaging technology based on visible light is relatively inexpensive and ubiquitous, and open-source analysis tools have proliferated. In this study, texture- and geometry-related phenotyping combined with color properties were investigated for their potential use in evaluating N in pakchoi (Brassica campestris ssp. chinensis L.). Potted pakchoi treated with four levels of N were cultivated in a greenhouse. Their top-view images were acquired using a camera at six growth stages. The corresponding plant N concentration was determined destructively. The quantitative relationships between the nitrogen nutrition index (NNI) and the image-based phenotyping features were established using the following algorithms: random forest (RF), support vector regression (SVR), and neural network (NN). The results showed the full model based on the color, texture, and geometry-related features outperforms the model based on only the color-related feature in predicting the NNI. The RF full model exhibited the most robust performance in both the seedling and harvest stages, reaching prediction accuracies of 0.823 and 0.943, respectively. The high prediction accuracy of the model allows for a low-cost, non-destructive monitoring of N in the field of precision crop management.

Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

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