Crop reflectance monitoring as a tool for water stress detection in greenhouses: A review

Plant responses to drought stress are complex due to various mechanisms of drought avoidance and tolerance to maintain growth. Traditional plant phenotyping methods are labor-intensive, time-consuming, and subjective. Plant phenotyping by integrating kinetic chlorophyll fluorescence with multicolor fluorescence imaging can acquire plant morphological, physiological, and pathological traits related to photosynthesis as well as its secondary metabolites, which will provide a new means to promote the progress of breeding for drought tolerant accessions and gain economic benefit for global agriculture production. Combination of kinetic chlorophyll fluorescence and multicolor fluorescence imaging proved to be efficient for the early detection of drought stress responses in the Arabidopsis ecotype Col-0 and one of its most affected mutants called reduced hyperosmolality-induced [Ca2+]i increase 1. Kinetic chlorophyll fluorescence curves were useful for understanding the drought tolerance mechanism of Arabidopsis. Conventional fluorescence parameters provided qualitative information related to drought stress responses in different genotypes, and the corresponding images showed spatial heterogeneities of drought stress responses within the leaf and the canopy levels. Fluorescence parameters selected by sequential forward selection presented high correlations with physiological traits but not morphological traits. The optimal fluorescence traits combined with the support vector machine resulted in good classification accuracies of 93.3 and 99.1% for classifying the control plants from the drought-stressed ones with 3 and 7 days treatments, respectively. The results demonstrated that the combination of kinetic chlorophyll fluorescence and multicolor fluorescence imaging with the machine learning technique was capable of providing comprehensive information of drought stress effects on the photosynthesis and the secondary metabolisms. It is a promising phenotyping technique that allows early detection of plant drought stress.

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Phenotyping of Arabidopsis Drought Stress Response Using Kinetic Chlorophyll Fluorescence and Multicolor Fluorescence Imaging.

to reduced costs and environmental impact. Because the practise provides record trail, enhanced traceability of farm activities can be obtained that consumers and administration increasingly require (Stafford, 2000; Bellon-Maurel et al., 2014). PA is a cyclic system. The steps can be divided into data collection and localisation, data analysis, management decisions on applications, evaluation of management decisions; and then a new cycle starts. Each year, data are stored in a database and are used as historical data for future decisionmaking (Fountas et al., 2006; Gebbers and Adamchuk, 2010). All this large amount of potentially spatio-temporal data gathered using PA applications is leading to the ‘big data’ concept that will require optimized algorithms to extract the hidden knowledge and relations among variables. Modern PA has a rather short history. Its application started over the last twenty-five years, when global positioning systems (GPS) and yield monitors were made available in field crops. Harvesting was mechanised and sensors were placed on harvesting machines to measure the spatial distribution of yield continuously. Applications started in cereals using impact or γ-ray grain flow sensors. When first yield monitors were developed and yield maps were created, it was shown that yield and soil properties varied highly within the field. This fact marked the development of modern PA (Hedley, 2015). However, applications in fruit and vegetables German Society for Horticultural Science

https://scholar.ptuk.edu.ps/bitstream/123456789/92/2/81_2_2.pdf

Applications of precision agriculture in horticultural crops

Effects of drought stress on chlorophyll a fluorescence in two rubber tree clones

Wild relatives of wheat serve as an extraordinary source of variability for breeding programs due to their capabilities to respond to various environmental stresses. Here, we investigated some species possessing a D genome (T. aestivum, Ae. tauschii, Ae. crassa and Ae. cylindrica) in terms of relative water content (RWC), stomatal conductance (Gs), relative chlorophyll content, initial fluorescence (Fo), maximum quantum yield of PSII (Fv/Fm), maximum primary yield of PSII photochemistry (Fv/Fo), as well as shoot fresh and dry biomasses under control and water deficit conditions. Our results revealed that water deficit negatively affected all traits; shoot fresh weight, Gs and RWC showed the highest reduction compared to the control condition. Principal component analysis (PCA) identified two PCs that accounted for 53.36% of the total variation in the water deficit conditions. Correlation analysis and PCA-based biplots showed that stress tolerance index (STI) is significantly associated with Fv/Fm and Fv/Fo under water stress conditions, suggesting that these are the best parameters to evaluate when screening for tolerant samples at the seedling stage. We identified 19 accessions from Ae. crassa and one from Ae. tauschii as the most tolerant samples. In conclusion, Ae. crassa might provide an ideal genetic resource for drought-tolerant wheat breeds.

https://www.mdpi.com/2073-4395/9/9/522/pdf

Effect of Water Deficit Stress on Seedling Biomass and Physio-Chemical Characteristics in Different Species of Wheat Possessing the D Genome

Evaluation of plant seedling water stress using dynamic fluorescence index with blue LED-based fluorescence imaging

Tea is one of the most widely consumed beverage in the world, only second to water. Different from black tea (fully fermented) and green tea (unfermented), partially fermented teas such as Oolong, Bouchong are popular in Taiwan, Japan, China and Asian countries. The quality assurance during the process of partially fermented tea is highly related to the fermentation degree of tea, which is the ratio of total catechins oxidized during tea fermentation. Spectral imaging system was developed in this study to evaluate the degree of tea fermentation, which consisted of lighting chamber, image acquisition modules, cameras, and liquid crystal tunable filter (LCTF, 400-1100 nm). Software programs of the system control and image processing were also developed. The spectra analyses were conducted; calibration models for the evaluation of total catechins were established by MPLSR (modified partial least square regression) and MLR (multiple linear regression) methods. The results showed that r (correlation coefficient), SEC (standard error of calibration), SEV (standard error of validation) were 0.99,4.56 mg/g, 5.67 mg/g for MPLSR model, and 0.99, 5.04 mg/g, 6.50 mg/g for MLR model respectively. Although MPLSR model has better prediction ability than MLR, MLR model has a potential to be integrated with multi-spectral image system to develop an on-line inspection system for monitoring fermentation degree of tea.

Spectral Imaging Approach to Evaluate Degree of Tea Fermentation by Total Catechins

Carbohydrate contents have been demonstrated as indicators for flowering quality of Phalaenopsis plants. In this study,
near infrared reflectance (NIR) spectroscopy was employed for quantitative analysis of carbohydrate contents like
fructose, glucose, sucrose, and starch in Phalaenopsis. The modified partial least squares regression (MPLSR) method
was adopted for spectra analyses of 176 grown plant samples (88 shoots and 88 roots), over the full wavelength range
(FWR, 400 to 2498 nm). For fructose concentrations, the smoothing 1st derivative model can produce the best effect (Rc
= 0.961, SEC = 0.210% DW, SEV = 0.324% DW) in the wavelength ranges of 1400-1600, 1800-2000, and 2200-2300
nm. For glucose concentrations, the smoothing 1st derivative model can produce the best effect (Rc = 0.975, SEC =
0.196% DW, SEV = 0.264% DW) in the wavelength range of 1400-1600, 1800-2000, and 2100-2400 nm. For sucrose
concentrations, the smoothing 1st derivative model can produce the best effect (Rc = 0.961, SEC = 0.237% DW, SEV =
0.322% DW) in the wavelength range of 1300-1400, 1500-1800, 2000-2100, and 2200-2300 nm. For starch
concentrations, the smoothing 1st derivative model can produce the best effect (Rc = 0.873, SEC = 0.697% DW, SEV =
0.774% DW) in the wavelength ranges of 500-700, 1200-1300, 1700-1800, and 2200-2300 nm. This study successfully
developed the calibration models for inspecting concentrations of carbohydrates to predict the flowering quality in
different cultivation environments of Phalaenopsis. The specific wavelengths can be used to predict the quality of
Phalaenopsis flowers and thus to adjust cultivation managements.

Yung-Kun Chuang

Papers

Evaluation of plant seedling water stress using dynamic fluorescence index with blue LED-based fluorescence imaging

Spectral Imaging Approach to Evaluate Degree of Tea Fermentation by Total Catechins

Evaluation of Phalaenopsis flowering quality using near infrared spectroscopy