Active control of photosynthetic activities is important in plant physiological study. Although models of plant photosynthesis have been built at different scales, they have not been fully examined for their application in plant growth control. However, we do not have an infrastructure to support such experiments since current plant growth chambers usually use fixed control protocols. In our current paper, an open IoT-based framework is proposed. This framework allows a plant scientist or agricultural engineer, through an application programming interface (API), in a desirable programming language, (1) to gather environmental data and plant physiological responses; (2) to program and execute control algorithms based on their models, and then (3) to implement real-time commands to control environmental factors. A plant growth chamber was developed to demonstrate the concept of the proposed open framework.
Large amounts of antibiotics and microplastics are used in daily life and agricultural production, which affects not only plant growth but also potentially the food safety of vegetables and other plant products. Fast detection of the presence of antibiotics and microplastics in leafy vegetables is of great interest to the public. In this work, a method was developed to detect sulfadiazine and polystyrene, commonly used antibiotics and microplastics, in vegetables by measuring and modeling photosystem II chlorophyll a fluorescence (ChlF) emission from leaves. Chrysanthemum coronarium L., a common beverage and medicinal plant, was used to verify the developed method. Scanning electron microscopy, transmission electron microscopy, and liquid chromatograph-mass spectrometer analysis were used to show the presence of the two pollutants in the samples. The developed kinetic model could describe measured ChlF variations with an average relative error of 0.6%. The model parameters estimated for the chlorophyll a fluorescence induction kinetics curve (OJIP) induction can differentiate the two types of stresses while the commonly used ChlF OJIP induction characteristics cannot. This work provides a concept to detect antibiotic pollutants and microplastic pollutants in vegetables based on ChlF.
Drought significantly constrains higher yield of alfalfa (Medicago sativa L.) in arid and semiarid areas all over the world. This study evaluated the responses of leaf cuticular wax constituents to drought treatment and their relations to gas-exchange indexes across six alfalfa cultivars widely grown in China. Water deficit was imposed by withholding water for 12 d during branching stage. Cuticular waxes on alfalfa leaves were dominated by primary alcohols (41.7-54.2%), alkanes (13.2-26.9%) and terpenes (17.5-28.9%), with small amount of aldehydes (1.4-3.4%) and unknown constituents (4.5-18.4%). Compared to total wax contents, the wax constituents were more sensitive to drought treatment. Drought decreased the contents of primary alcohol and increased alkanes in all cultivars. Alkane homologs, C25, C27, and C29, were all negatively correlated with photosynthetic rate, transpiration rate, stomatal conductance, and leaf water potential. Under drought conditions, both stomatal and nonstomatal factors were involved in controlling water loss from alfalfa leaves. No direct relationship was observed between wax contents and drought resistance among alfalfa cultivars. An increase in alkane content might be more important in improving drought tolerance of alfalfa under water deficit, which might be used as an index for selecting and breeding drought resistant cultivars of alfalfa., Y. Ni ... [et al.]., and Obsahuje bibliografii
Sensing and classification of drought stress levels are very important to agricultural production. In this work, rice drought stress levels were classified based on the commonly used chlorophyll a fluorescence (ChlF) parameter (Fv/Fm), feature data (induction features), and the whole OJIP induction (induction curve) by using a Support Vector Machine (SVM). The classification accuracies were compared with those obtained by the K-Nearest Neighbors (KNN) and the Ensemble model (Ensemble) correspondingly. The results show that the SVM can be used to classify drought stress levels of rice more accurately compared to the KNN and the Ensemble and the classification accuracy (86.7%) for the induction curve as input is higher than the accuracy (43.9%) with Fv/Fm as input and the accuracy (72.7%) with induction features as input. The results imply that the induction curve carries important information on plant physiology. This work provides a method of determining rice drought stress levels based on ChlF.