Using Rapid Chlorophyll Fluorescence Transients to Classify Vitis Genotypes

Chlorophyll Fluorescence on the Fast Timescale

Chlorophyll fluorescence is a rapid and noninvasive tool used for probing the activity of photosynthesis that can be used in vivo and in the field. It is highly relevant to the demands of high-throughput crop phenotyping and can be automated or manually applied. In this chapter, we describe protocols and advice for making fast timescale fluorescence measurements using handheld equipment in the laboratory or in the field in the context of phenotyping. While interpretation of some measured parameters requires caution for the purpose of identifying underlying mechanisms, we demonstrate this technique is appropriate for some applications where convenience, rapidity, and sensitivity are required.

Dendrobium candidum quality detection in both food and medicine agricultural product: Policy, status, and prospective

Dendrobium candidum (DC) is an agricultural product for both food and medicine. It has a variety of beneficial effects on the human body with antioxidant, anti-inflammatory, antitumor, enhancing immune function, and other pharmacological activities. Due to less natural distribution, harsh growth conditions, slow growth, low reproduction rate, and excessive logging, wild DC has been seriously damaged and listed as an endangered herbal medicine variety in China. At present, the quality of DC was uneven in the market, so it is very necessary to detect its quality. This article summarized the methods of DC quality detection with traditional and rapid nondestructive, and it also expounded the correlation between DC quality factor and endophytes, which provides a theoretical basis for a variety of rapid detection methods in macromolecules. At last, this article put forward a variety of rapid nondestructive detection methods based on the emission spectrum. In view of the complexity of molecular structure, the quality correlation established by spectral analysis was greatly affected by varieties and environment. We discussed the possibility of DC quality detection based on the molecular dynamic calculation and simulation mechanism. Also, a multimodal fusion method was proposed to detect the quality. The literature review suggests that it is very necessary to understand the structure performance relationship, kinetic properties, and reaction characteristics of chemical substances at the molecular level by means of molecular chemical calculation and simulation, to detect a certain substance more accurately. At the same time, several modes are combined to form complementarity, eliminate ambiguity, and uncertainty and fuse the information of multiple modes to obtain more accurate judgment results.

End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses

Herbicides and heavy metals are hazardous substances of environmental pollution, resulting in plant stress and harming humans and animals. Identification of stress types can help trace stress sources, manage plant growth, and improve stress-resistant breeding. In this research, hyperspectral imaging (HSI) and chlorophyll fluorescence imaging (Chl-FI) were adopted to identify the rice plants under two types of herbicide stresses (butachlor (DCA) and quinclorac (ELK)) and two types of heavy metal stresses (cadmium (Cd) and copper (Cu)). Visible/near-infrared spectra of leaves (L-VIS/NIR) and stems (S-VIS/NIR) extracted from HSI and chlorophyll fluorescence kinetic curves of leaves (L-Chl-FKC) and stems (S-Chl-FKC) extracted from Chl-FI were fused to establish the models to detect the stress of the hazardous substances. Novel end-to-end deep fusion models were proposed for low-level, middle-level, and high-level information fusion to improve identification accuracy. Results showed that the high-level fusion-based convolutional neural network (CNN) models reached the highest detection accuracy (97.7%), outperforming the models using a single data source (<94.7%). Furthermore, the proposed end-to-end deep fusion models required a much simpler training procedure than the conventional two-stage deep learning fusion. This research provided an efficient alternative for plant stress phenotyping, including identifying plant stresses caused by hazardous substances of environmental pollution.

Machine learning methods for assessing photosynthetic activity: environmental monitoring applications

Monitoring of the photosynthetic activity of natural and artificial biocenoses is of crucial importance. Photosynthesis is the basis for the existence of life on Earth, and a decrease in primary photosynthetic production due to anthropogenic influences can have catastrophic consequences. Currently, great efforts are being made to create technologies that allow continuous monitoring of the state of the photosynthetic apparatus of terrestrial plants and microalgae. There are several sources of information suitable for assessing photosynthetic activity, including gas exchange and optical (reflectance and fluorescence) measurements. The advent of inexpensive optical sensors makes it possible to collect data locally (manually or using autonomous sea and land stations) and globally (using aircraft and satellite imaging). In this review, we consider machine learning methods proposed for determining the functional parameters of photosynthesis based on local and remote optical measurements (hyperspectral imaging, solar-induced chlorophyll fluorescence, local chlorophyll fluorescence imaging, and various techniques of fast and delayed chlorophyll fluorescence induction). These include classical and novel (such as Partial Least Squares) regression methods, unsupervised cluster analysis techniques, various classification methods (support vector machine, random forest, etc.) and artificial neural networks (multilayer perceptron, long short-term memory, etc.). Special aspects of time-series analysis are considered. Applicability of particular information sources and mathematical methods for assessment of water quality and prediction of algal blooms, for estimation of primary productivity of biocenoses, stress tolerance of agricultural plants, etc. is discussed.

Potential Phenotyping Methodologies to Assess Inter- and Intravarietal Variability and to Select Grapevine Genotypes Tolerant to Abiotic Stress

Plant phenotyping is an emerging science that combines multiple methodologies and protocols to measure plant traits (e.g., growth, morphology, architecture, function, and composition) at multiple scales of organization. Manual phenotyping remains as a major bottleneck to the advance of plant and crop breeding. Such constraint fostered the development of high throughput plant phenotyping (HTPP), which is largely based on imaging approaches and automatized data retrieval and processing. Field phenotyping still poses major challenges and the progress of HTPP for field conditions can be relevant to support selection and breeding of grapevine. The aim of this review is to discuss potential and current methods to improve field phenotyping of grapevine to support characterization of inter- and intravarietal diversity. Vitis vinifera has a large genetic diversity that needs characterization, and the availability of methods to support selection of plant material (polyclonal or clonal) able to withstand abiotic stress is paramount. Besides being time consuming, complex and expensive, field experiments are also affected by heterogeneous and uncontrolled climate and soil conditions, mostly due to the large areas of the trials and to the high number of traits to be observed in a number of individuals ranging from hundreds to thousands. Therefore, adequate field experimental design and data gathering methodologies are crucial to obtain reliable data. Some of the major challenges posed to grapevine selection programs for tolerance to water and heat stress are described herein. Useful traits for selection and related field phenotyping methodologies are described and their adequacy for large scale screening is discussed.

Effect of Cameraria ohridella feeding on Aesculus hippocastanum photosynthesis

The complex of invasive species of phytophagous insects that can adapt to novel living conditions is constantly increasing. The ecological effect of their vital activity in the new environment is almost impossible to predict. As a result, invasions undesirable in the economic sense are often observed. The horse -chestnut leaf miner (Cameraria ohridella Deschka & Dimič, 1986, Gracillariidae) is one of these problematic invader species in the introduced range of the horse chestnut (Aesculus hippocastanum (Linnaeus, 1753), Sapindaceae). We studied the effect of C. ohridella on the state of the photosynthetic apparatus in Ae. hippocastanum leaves. Photosynthesis is the one of the processes most vulnerable to stress factors, so information about the state of photosynthetic apparatus in a plant under the influence of phytophage feeding obtained with fluorescence analysis can be significant. The feeding effect of C. ohridella caterpillars on the functional state and activity of Ae. hippocastanum photosynthetic apparatus was studied. We studied critical parameters of chlorophyll fluorescence induction reflecting the effect of a single C. ohridella generation (feeding the caterpillar for the five stages of this species’ development) on the functional links of the photosystem II in Ae. hippocastanum leaves. The data obtained show a decrease in PS II quantum efficiency (inhibition of photosynthetic activity) in the leaves from different parts of the crown; it suggests the destructive effect of C. ohridella caterpillar feeding on Ae. hippocastanum photosynthetic apparatus. Values of all key parameters of chlorophyll fluorescence indication evidence inhibition of photophysical and photochemical processes of photosynthesis and impaired coherence of Calvin cycle reactions. Study of the feeding effect of C. ohridella caterpillars on the efficiency of the main enzyme in the Calvin cycle (which closely correlates with the coefficient of fluorescence induction that characterizes the efficiency of dark photosynthetic processes) showed a significant decrease in its activity in the leaves of both the illuminated and shaded parts of the crown. Our study has shown that the method of chlorophyll fluorescence induction allows one to determine the general state of a plant in an express regime by evaluating the main process of plant life as photosynthesis. Analysis of chlorophyll fluorescence parameters is a powerful and effective tool for determining the effect of phytophages on the plant body. The obtained data allow us to apply the method of analyzing chlorophyll fluorescence induction in practice to establish the physiological state of tree flora in forests and garden farms.