Effects of photosynthetic models on the calculation results of photosynthetic response parameters in young Larix principis-rupprechtii Mayr. plantation

Machine Learning-Based Crop Stress Detection in Greenhouses

Greenhouse climate control systems are usually based on greenhouse microclimate settings to exert any control. However, to save energy, water and nutrients, additional parameters related to crop performance and physiology will have to be considered. In addition, detecting crop stress before it is clearly visible by naked eye is an advantage that could aid in microclimate control. In this study, a Machine Learning (ML) model which takes into account microclimate and crop physiological data to detect different types of crop stress was developed and tested. For this purpose, a multi-sensor platform was used to record tomato plant physiological characteristics under different fertigation and air temperature conditions. The innovation of the current model lies in the integration of photosynthesis rate (Ps) values estimated by means of remote sensing using a photochemical reflectance index (PRI). Through this process, the time-series Ps data were combined with crop leaf temperature and microclimate data by means of the ML model. Two different algorithms were evaluated: Gradient Boosting (GB) and MultiLayer perceptron (MLP). Two runs with different structures took place for each algorithm. In RUN 1, there were more feature inputs than the outputs to build a model with high predictive accuracy. However, in order to simplify the process and develop a user-friendly approach, a second, different run was carried out. Thus, in RUN 2, the inputs were fewer than the outputs, and that is why the performance of the model in this case was lower than in the case of RUN 1. Particularly, MLP showed 91% and 83% accuracy in the training sample, and 89% and 82% in testing sample, for RUNs 1 and 2, respectively. GB showed 100% accuracy in the training sample for both runs, and 91% and 83% in testing sample in RUN 1 and RUN 2, respectively. To improve the accuracy of RUN 2, a larger database is required. Both models, however, could easily be incorporated into existing greenhouse climate monitoring and control systems, replacing human experience in detecting greenhouse crop stress conditions.

Functional Traits of Male and Female Leaves of Hippophae tibetana on the Eastern Edge of the Tibetan Plateau and Their Altitudinal Variability

To date, there have been few studies of the functional traits of the dioecious Hippophae tibetana Schlecht leaves, either male or female, in response to ecological factors such as altitude. Elucidating these relationships will establish an important scientific basis for vegetation restoration and reconstruction of the Tibetan Plateau ecosystem. The natural populations of H. tibetana, distributed across three field sites, at 2868 m, 3012 m and 3244 m, in Tianzhu, Gansu, were studied by field survey sampling and laboratory analysis. In particular, the adaptions of leaf functional traits to elevation in these dioecious plants were analyzed. The results show that: (1) there is no “midday depression” of photosynthetic activity in either male or female plants. Over a one-day period, the net photosynthetic rate (Pn) and transpiration rate (Tr) of H. tibetana female plants were higher than those of male plants (p < 0.05). This correlated to the period of vigorous fruit growth in the female plant. The measured Pn and Tr were maximal at the intermediate altitude (3012 m). The light compensation point (LCP) of the leaves of male and female plants were 57.6 and 43.2 μmol·m−2·s−1, respectively, and the light saturation points (LSP) of the leaves were 1857.6 and 1596.8 μmol·m−2·s−1. (2) Altitude had a significant effect on plant and leaf functional traits of male and female H. tibetana (p < 0.05), and no significant difference was noted between plants at the same altitude. The values for leaf area (LA), specific leaf weight (LMA), leaf phosphorus content per unit mass (Pmass) and leaf phosphorus content per unit area (Parea) were also maximal at the intermediate altitude. Leaf nitrogen content per unit area (Narea) and leaf nitrogen content per unit mass (Nmass) increased with altitude. This indicated that the functional traits of male and female plants and leaves of H. tibetana showed a strong “trade-off relationship” with altitude. (3) Pearson correlation analysis showed that there were significant correlations among functional traits of H. tibetana leaves. Redundancy analysis (RDA) showed that soil water content (SWC), altitude (Alt) and soil organic carbon (SOC) had significant effects on the functional traits of H. tibetana leaves (p < 0.05).