To assess the influence of drought stress on the growth and nitrogen nutrition status of pakchoi (Brassica campestris ssp. Chinensis L.) at different nitrogen (N) levels, the changes in N accumulation and enzyme activities involved in N assimilation were investigated. The drought was induced by adding polyethylene glycol (PEG) under hydroponic culture conditions. Pakchoi seedlings were exposed to a modified nutrient solution with different nitrogen concentration (N1, N2, and N3 represent 2, 9 and 18 mM NaNO₃, respectively) and osmotic potential (W1, W2 and W3 represent 0, 60 and 120 g·L⁻¹ PEG 6000) in a full factorial, replicated randomized block design. A short time (seven days) of drought stress caused a significant decline in plant water content, transpiration rate, shoot biomass and shoot nitrogen concentration. Increasing N availability considerably alleviate drought stress by increasing the content of total free amino acids in the roots, promoting the acceleration of root biomass accumulation, and improving the activities of nitrate reductase (NR; EC 1.7.1.1) and glutamine synthetase (GS; EC 6.3.1.2) which would reduce moisture limitations. The results suggested that pakchoi supplied with relative higher N had better growth performance under drought stress.

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.

Water management is one of the most important operations in greenhouse baby leaf production. However, growers mainly irrigate the plants based on experience, which generally leads to yield loss, uneven quality, and low water-use efficiency. This study evaluated four evapotranspiration (ET) models, such as Radsum, Penman methods, FAO Penman-Monteith, and Priestley-Taylor, for irrigation strategy by predicting the ET level of greenhouse baby pakchoi [ Brassica rapa L. ssp. chinensis (L.) Hanelt] under different plant densities (72-, 128-, 200-, and 288-plug tray). Among environmental factors, net radiation and photosynthetically active radiation ( PAR ) had the highest correlation with ET, with R ² of 0.93 and 0.94, respectively. Plant growth period was divided into different stages according to canopy development and substrate surface coverage. The corresponding crop coefficient (K _c ) was introduced into ET prediction models. The result shows overestimation of ET _c (crop evapotranspiration) by the Radsum and Penman methods. FAO Penman-Monteith and Priestley-Taylor methods performed the best with R ² ≈0.7 for all planting densities. These two methods are recommended for greenhouse irrigation scheduling in baby pakchoi production.

In recent years, vegetable production has been in the transitional stage from traditional cultivation mode to modern cultivation mode. Therefore, evaluating the actual productivity and economic benefits of different cultivation modes is instructive for leafy vegetable production. This research chose a vegetable production company (Shanghai, China) as a demonstration case to cultivate various leaf vegetables under different cultivation modes (hydroponic, substrate, and soil cultivation). By investigating the yield and quality of vegetables, as well as all production costs and selling prices, we drive a comparative evaluation of comprehensive features and economic benefits of different cultivation modes. The results showed that leafy vegetables cultivated by hydroponic mode gain the highest fresh weight at the early growth stage, but the greatest unit yield (0.98~1.21 kg·m⁻²) emerged under the soil cultivation mode. Vegetables grown under substrate mode had the best comprehensive quality with high soluble solids content and low nitrate content. The unit costs of the soilless cultivation modes (8.26~10.3 CNY·m⁻²) were 3–4-fold higher than that of soil cultivation, mainly due to the high fixed costs and input costs, especially for substrate mode. According to actual prices, the soil cultivation mode had the highest economic benefits as a whole (4.83~8.83 CNY·m⁻²), and only hydroponic cabbage which had comparable yield gained considerable benefits (8.94 CNY·m⁻²). Quality-based pricing scheme can make substrate mode achieve profitability. The pricing scheme that guarantees a 40% revenue is within acceptable limits for consumers. Based on this actual production case, it can be concluded that a low unit yield of hydroponic mode and a high unit cost of substrate mode are the predominant factors affecting their profitability and economic viability.