The aim of this study was to describe the sigmoidal growth behaviour of a lettuce canopy using three nonlinear models. Gompertz, Logistic and grey Verhulst growth models were established for the top projected canopy area (TPCA), top projected canopy perimeter (TPCP) and plant height (PH), which were measured by two machine vision views and 3D point clouds data. Satisfactory growth curve fitting was obtained using two evaluation criteria: the coefficient of determination (R²) and the mean absolute percentage error (MAPE). The grey Verhulst models produced a better fit for the growth of TPCA and TPCP, with higher R² ($R_{TPCA}^2=0.9097, R_{TPCP}^2=0.8536$) and lower MAPE ($MAP{E}_{TPCA}^{}=0.0284, MAP{E}_{TPCP}^{}=0.0794$) values, whereas the Logistic model produced a better fit for changes in PH ($R_{PH}^2=0.8991,MAP{E}_{PH}^{}=0.0344$). The maximum growth rate point and the beginning and end points of the rapid growth stage were determined by calculating the second and third derivatives of the models, permitting a more detailed description of their sigmoidal behaviour. The initial growth stage was 1–5.5 days, and the rapid growth stage lasted from 5.6 to 26.2 days. After 26.3 days, lettuce entered the senescent stage. These inflections and critical points can be used to gain a better understanding of the growth behaviour of lettuce, thereby helping researchers or agricultural extension agents to promote growth, determine the optimal harvest period and plan commercial production.

Lettuce is a commercially significant leafy vegetable worldwide. Potassium (K) is an essential macronutrient for lettuce growth and development and significantly impacts its metabolites. Biomarkers that are indicative of variations in the K status of lettuce before the occurrence of biophysical changes (e.g., leaf or canopy morphological, textural and color features), can be adopted to determine the early K status of lettuce. To understand the effect of low K on diverse metabolites, we examined the metabolic response of lettuce in a closed cultivation room under controlled conditions. The evaluation was based on large-scale untargeted metabolomics assay of the K group using liquid chromatography-mass spectrometry. Data were analyzed with a fold-change (FC) analysis, t-test, and orthogonal partial least square-discriminant analysis. Fifty-two metabolites were classified into two groups by the FC, p, and the variable importance in projection (VIP). Low K led to an increment in 40 metabolites (FC > 2, p < 0.05, VIP > 1) and a decrease in 12 metabolites (FC < 0.5, p < 0.05, VIP > 1). Ten pathways were significantly enriched with metabolic biomarkers. In light of the complex interactive relationships among N, P, and K, the top five biomarkers were screened further by VIP > 4.00. Adenosine, FA 18:1+3O, uridine, cis-aconitate, and D(-)-gulono-gamma-lactone showed an increase in low-K stress samples, and may be considered potential metabolic biomarkers. This study validates the impact of low K on lettuce metabolism, and identifies biomarkers that can be used to monitor the K status in lettuce.

Cucumber xylem and phloem sap is a key link in nutrient distribution, transportation and signal transduction of cucumber plants; however, the metabolic response mechanism of cucumber xylem and phloem sap under phosphorus stress has not been clearly revealed. In this study, gas chromatography-mass spectrometry (GC-MS) combined with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to analyze the metabolites in cucumber xylem and phloem sap under different phosphorus stress. A total of 22 differential metabolites were screened from xylem and phloem sap, respectively. Through the analysis of metabolic pathways of differential metabolites, four and three key metabolic pathways were screened, respectively. The results showed that compared with the normal phosphorus level, the content of most amino acids in the key metabolic pathway increased in xylem but decreased in phloem both under low and high phosphorus stress levels. The contents of sucrose and glucose in phloem glycolysis pathway showed a positive correlation with the change of phosphorus nutrient levels. The tricarboxylic acid cycle was promoted in xylem and phloem of cucumber under low and high phosphorus nutrient levels, and the contents of malic acid and citric acid increased significantly. This study provided abundant biochemical information for the metabolic response and regulation strategies of cucumber xylem and phloem under phosphorus stress, and is committed to looking for more sensitive markers to evaluate the supply level of phosphorus nutrients in cucumber.

As the main working part of a combine harvester, the cleaning device affects the cleaning performance of the machine. The simulation of flow fields in a cleaning chamber has become an important part of the design. Currently, post-processing analyses of flow field simulation still rely on the researchers’ experience, so it is difficult to obtain information from post-processing automatically. The experience of researchers is difficult to describe and disseminate. This paper studied an intelligent method to analyze simulation result data which is based on the object detection algorithm and the reasoning mechanism. YOLOv8, one of the deep learning object detection algorithms, was selected to identify key-point data from the flow field in a cleaning chamber. First, the training dataset was constructed via scatter plot drawing, data enhancement, random screening, and other technologies. Then, the flow field in the cleaning chamber was divided into six key areas by identifying the key points of the flow field. And, an analysis of the reasonable wind velocity in the areas was conducted, and the cleaning results of the grain were obtained by using the reasoning mechanism based on rules and examples. Finally, a system based on the above method was established in Python 3.10 software. With the help of the method and the system in this paper, the flow field characteristics in a cleaning chamber and the effects of wind on the cleaning effect can be obtained automatically if the physical properties of the crop, the geometric parameters of the cleaning chamber, and the working parameters of the machine are given.