60 research outputs found

    Past and future of plant stress detection: an overview from remote sensing to Positron Emission Tomography

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    Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters) and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture

    Classification of water stress in cultured Sunagoke moss using deep learning

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    Water stress greatly determines plant yield as it affects plant metabolism, photosynthesis rate, chlorophyll content index, number of leaves, physiological, biochemical compound, and vegetative growth. The research aimed to detect and classify water stress of cultured Sunagoke moss into several categories i.e. dry, semi-dry, wet, and soak by using a low-cost commercial visible light camera combined with a deep learning model. Cultured Sunagoke moss is a commercial product which has the potential use as rooftop-greening and wall-greening material. This research compared the performance of four convolutional neural network models, such as SqueezeNet, GoogLeNet, ResNet50, and AlexNet. The best convolutional neural network model according to the training and validation result was ResNet50 with RMSProp optimizer, 30 epoch, and 128 mini-batch size; this also gained an accuracy rate at 87.50%. However, the best result of the convolutional neural network model on data testing using confusion matrices on different data sample was ResNet50 with Adam optimizer, 30 epoch, 128 mini-batch size, and average testing accuracy of 94.15%. It can be concluded that based on the overall results, convolutional neural network model seems promising as a smart irrigation system that real-time, non-destructive, rapid, and precise method when controlling water stress of plants

    A Cost-effective Multispectral Sensor System for Leaf-Level Physiological Traits

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    With the concern of the global population to reach 9 billion by 2050, ensuring global food security is a prime challenge for the research community. One potential way to tackle this challenge is sustainable intensification; making plant phenotyping a high throughput may go a long way in this respect. Among several other plant phenotyping schemes, leaf-level plant phenotyping needs to be implemented on a large scale using existing technologies. Leaf-level chemical traits, especially macronutrients and water content are important indicators to determine crop’s health. Leaf nitrogen (N) level, is one of the critical macronutrients that carries a lot of worthwhile nutrient information for classifying the plant’s health. Hence, the non-invasive leaf’s N measurement is an innovative technique for monitoring the plant’s health. Several techniques have tried to establish a correlation between the leaf’s chlorophyll content and the N level. However, a recent study showed that the correlation between chlorophyll content and leaf’s N level is profoundly affected by environmental factors. Moreover, it is also mentioned that when the N fertilization is high, chlorophyll becomes saturated. As a result, determining the high levels of N in plants becomes difficult. Moreover, plants need an optimum level of phosphorus (P) for their healthy growth. However, the existing leaf-level P status monitoring methods are expensive, limiting their deployment for the farmers of low resourceful countries. The aim of this thesis is to develop a low-cost, portable, lightweight, multifunctional, and quick-read multispectral sensor system to sense N, P, and water in leaves non-invasively. The proposed system has been developed based on two reflectance-based multispectral sensors (visible and near-infrared (NIR)). In addition, the proposed device can capture the reflectance data at 12 different wavelengths (six for each sensor). By deploying state of the art machine learning algorithms, the spectroscopic information is modeled and validated to predict that nutrient status. A total of five experiments were conducted including four on the greenhouse-controlled environment and one in the field. Within these five, three experiments were dedicated for N sensing, one for water estimation, and one for P status determination. In the first experiment, spectral data were collected from 87 leaves of canola plants, subjected to varying levels of N fertilization. The second experiment was performed on 1008 leaves from 42 canola cultivars, which were subjected to low and high N levels, used in the field experiment. The K-Nearest Neighbors (KNN) algorithm was employed to model the reflectance data. The trained model shows an average accuracy of 88.4% on the test set for the first experiment and 79.2% for the second experiment. In the third and fourth experiments, spectral data were collected from 121 leaves for N and 186 for water experiments respectively; and Rational Quadratic Gaussian Process Regression (GPR) algorithm is applied to correlate the reflectance data with actual N and water content. By performing 5-fold cross-validation, the N estimation shows a coefficient of determination (R^2) of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola shows an R^2 of 18.02%, corn of 68.41%, soybean of 46.38%, and wheat of 64.58%. Finally, the fifth experiment was conducted on 267 leaf samples subjected to four levels of P treatments, and KNN exhibits the best accuracy, on the test set, of about 71.2%, 73.5%, and 67.7% for corn, soybean, and wheat, respectively. Overall, the result concludes that the proposed cost-effective sensing system can be viable in determining leaf N and P status/content. However, further investigation is needed to improve the water estimation results using the proposed device. Moreover, the utility of the device to estimate other nutrients as well as other crops has great potential for future research

    Candidate genes for stress response in silver fir (Abies alba Mill.)

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    The aim of this thesis was the identification and analysis of candidate genes for stress response in silver fir (Abies alba Mill.). This ecologically and economically important forest tree species is native to many mountainous regions of Europe but little is known about its ecological characteristics. Silver fir populations were heavily transformed by human activity, which results in a mismatch between past and current distribution. Recent studies suggest that silver fir can occupy warmer and dryer climates than it currently does. However, the species also suffered considerably during the 1970s and 1980s, including foliar damage, radial growth depression and local diebacks in Germany. This is attributed mainly to the peak in air pollution during this period, especially sulfur dioxide (SO2), which seems to heavily increase drought sensitivity in silver fir. The combination of both stressors, SO2 and drought events, negatively affected silver fir even in regions where drought is usually not a problem. In the context of anthropogenic global climate change that will very likely lead to an increase in temperature in Europe and to more extreme events such as severe drought periods, the question arises, how silver fir will cope with these environmental changes. Given the speed of the predicted changes and the increasing landscape fragmentation, silver fir might not be able to evade it via seed dispersal. As a sessile organism, the only other option is adaptation, which will likely draw from standing genetic variation. To successfully predict the fate of silver fir, especially in the face of global climate change, and to potentially manage populations based on such predictions, the genetic architecture of silver fir in the context of such important stressors as drought and air pollution has to be understood. There exist, however, little genomic resources for silver fir and conifers in general. This is due to their large and complex genomes and the long generational cycle, which makes conifers typical nonmodel species. As such, methods for the identification of the genetic basis of stress response are effectively limited to a candidate gene approach. The candidate gene approach includes the identification of functional candidate genes by measuring differential gene expression between a stressed and a control group. In the context of this thesis, the water content of silver fir seedlings was monitored in a laboratory using a novel terahertz spectroscopy setup. One group of seedlings was regularly irrigated while the other group was drought stressed. Continually measuring the water content allowed to harvest needles from both groups at a time when the water status was comparable between the individuals within each group. A differential expression analysis between the needles from both groups then revealed 296 genes that were significantly up- or down-regulated in response to drought stress. Of those genes, approximately 45% have not been previously described in any organism and are potentially unique to silver fir or conifers in general. However, since only needles of seedlings were analyzed at a specific level of drought stress, the results are limited in scope to the source material and stress intensity and cannot be directly applied to silver fir or drought stress in general. Also, this approach implies a cause-effect relationship between gene expression and a specific level of drought stress. Thus, it is very important that confounding factors are excluded from the experiment. Chlorophyll content in the needles, for example, might change over the course of the monitoring period due to the drought treatment. To test if the chlorophyll content could potentially influence the terahertz signal, chlorophyll was extracted from silver fir needles, in the course of this thesis, and different concentrations were measured using terahertz spectroscopy, showing that chlorophyll content does not influence terahertz monitoring. Another aspect of the candidate gene approach involves the variation within a polymorphic gene and its potential association with the variation in a phenotypic trait. Since the growth depression period of silver fir in the 1970s and 1980s was mostly influenced by the combination of air pollution and drought, in the context of this thesis, genetic variation, in the form of single nucleotide polymorphims (SNPs) in pre-selected genes, was associated with tree-ring derived phenotypes for individual trees in the Bavarian Forest National Park. These so called ’dendrophenotypes’ were measures for resistance, resilience and recovery during the depression period, as well as the drought year 1976. Using general linear models and feature selection techniques based on the machine learning algorithm random forest, 15 out of 103 polymorphic candidate genes for trait variation could be identified. Since the associated dendrophenotyes are potentially adaptively relevant, the variation in this candidate genes could influence the stress coping capability of individual trees. However, this approach is of an observational nature and thus, cause-effect relationships cannot be derived from this type of experiment. The identified SNPs might be the causal variant or physically close to the true causal variant or it might just be a spurious correlation. Further, reliance on advanced statistical techniques can be troublesome, as could be demonstrated in the course of this thesis for a random forest based feature selection technique, developed for genetic association studies in conifers. Replicating this study and evaluating the algorithm, non-uniqueness of the results could be demonstrated, which not only hinders biological interpretation but can severely negatively influence downstream analyses, such as tests for interaction between SNPs. In conclusion, this thesis presents new techniques to add to the current methodology for candidate gene selection and analysis in the stress response of the non-model organism silver fir and other conifer species. Both approaches should be combined, for example by drawing polymorphic candidate genes for trait variation from the pool of functional candidate genes to ensure the involvement of the studied genes in the variation of the trait of interest. Further, the results of this thesis add to the growing molecular resources in silver fir and thereby, hopefully, contribute to the successful prediction and management of this important forest tree species in the face of rapidly changing environmental conditions

    Development of Multifunctional Electrical Impedance Spectroscopy System for Characterization in Plant Phenotyping

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    Plant phenotyping plays an important role for the thorough assessment of plant traits such as growth, development, resistance, physiology, etc. Assessing the nutrients and water contents by obtaining the spectroscopy data is essential for plant characterization, and photosynthesis. The conventional optical methods like visible/near-infrared spectroscopy, hyperspectral or multispectral imaging, and optical tomography have been developed and studied for the assessment of plant nutrition status and water stress. Although there are several advantages of these methods, they have some limitations as to their environmental sensitivity and confounding factors (i.e., light intensity, and color). These methods require large data storage capacity which makes the system expensive, and heavier in weight. In addition, most of these methods are not useful for in situ and rapid measurements. To overcome these limitations a multifrequency electrical measurement method such as electrical impedance spectroscopy (EIS) has been investigated which is found less sensitive to the environmental variables. The physical and chemical changes of the plants can be accurately described by EIS parameters like impedance, resistance, or capacitance. The measurement using EIS is found non-destructive, inexpensive, in situ, and rapid which could be an attractive alternative to the optical methods. An accurate impedance spectroscopy modeling for the characterization of the plants using a multifunctional spectroscopy system is still desired which can overcome the shortcomings of the existing methods. This research work deals with the development of a multifunctional EIS system to increase the robustness in applications for assessing the leaf nitrogen status, leaf water stress, root growth, and root biomass of the plants, and detecting the plant-like organisms such as algae species by measuring impedances in multiple frequencies. The overall research work is divided into three phases. In the first phase, we developed new EIS models for the determination of plant leaf nitrogen concentrations by measuring leaf impedances in the vegetative growth stage. The models were evaluated by the regression analysis in multiple frequencies. EIS sensor is found highly accurate in determining the plant leaf nitrogen status compared to soil plant analysis development (SPAD), and the method using EIS sensor is found cost-effective. In addition, we developed other new EIS models for determining the leaf water contents under different water stress conditions of the plants rapidly and efficiently. Regression analysis was performed, and the models were optimized and evaluated with the measured leaf impedances in multiple frequencies. The EIS sensor is found a low-cost and effective tool in determining the crop leaf water status compared to the other conventional approaches. In the second phase, we investigated whether the EIS sensor can be used to determine the algae species in water. The photosynthetic pigments like Chlorophyll-a concentrations were estimated by measuring impedances of the algae species and the corresponding EIS characteristics were obtained to detect the species. New EIS models were developed and validated with less error by performing regression analysis in multiple frequencies. The models were found accurate, and suitable for the estimation performance. A rapid performance of the sensor is found for measuring Chlorophyll-a as an alternative to the conventional approaches. In the third phase, we investigated whether the developed EIS system can be used for obtaining three-dimensional (3D) images of plant roots. An in situ and rapid electrical impedance tomography (EIT) data acquisition system was developed based on EIS for the further experiments in imaging and assessing the growth of the plant roots. Multifrequency impedance imaging technique was utilized, and the samples were reconstructed with finite element method (FEM) modeling which was carried out using electrical impedance and diffuse optical tomography reconstruction software (EIDORS) in MATLAB. At first, a low-cost, and high-precision EIT system was developed by designing a portable sensor with two layers of electrode array in a cylindrical domain. Different edible plant slices of carrot, radish, and potato along with multiple plant roots were taken in the EIT domain to assess and calibrate the system and their images were reconstructed by mapping conductivity in two-dimensional (2D) and three-dimensional (3D) planes. Later, a novel, dynamic, and adjustable EIT sensor system with three layers of electrode array was designed for developing a portable, cost-effective, and high-speed EIT data acquisition system. A non-invasive 3D imaging of multiple plant roots was made in both water and soil media. A non-destructive evaluation of biomass estimation of tap roots was carried out by measuring impedances using the designed EIT sensor system. A good correlation was found between the biomass and measured impedances of tap roots, and the estimated models for biomass were validated with less error. The developed EIT system is found suitable for in situ measurements and capable of monitoring the growth and estimating the biomass of plant roots. In overall, the estimated results from the measurements using the developed EIS/EIT system were found highly correlated with the ground truth measurements. Therefore, the developed multifunctional EIS system can be used as a low-cost, and effective tool for rapid and in-situ measurements for the characterization in plant phenotyping

    Hyperspectral Modeling of Relative Water Content and Nitrogen Content in Sorghum and Maize

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    Sorghum and maize are two of the most important cereal grains worldwide. They are important industrially, and also serve as staple crops for millions of people across the world. With climate change, increasing frequencies of droughts, and crops being planted on more marginal land, it is important to breed sorghum and maize cultivars that are tolerant to drought and low fertility soils. However, one of the largest constraints to the breeding process is the cycle time between cultivar development and release. Early evaluation of cultivars with increased the ability to maintain water status under drought and increases nitrogen contents under nitrogen stress could be the key to decreasing breeding cycle time. New tools for non-destructive, high throughput phenotyping are needed to evaluate new cultivars. These new tools can also be used for monitoring and management of crops to improve productivity. Hyperspectral imaging holds promise as one tool to improve the speed and accuracy of predicting numerous plant traits including abiotic stress tolerance characteristics. In this thesis, hyperspectral imaging projects were designed to develop and test prediction models for relative water content (RWC) and nitrogen (N) content of sorghum and maize. The first study utilized three different genotypes of sorghum in an automated hyperspectral imaging system in greenhouses at Purdue University. From this study, models were developed for relative water content and nitrogen content using the data from all three genotypes collectively as well as the data from each genotype individually. Models developed using the spectral and morphological features obtained from the hyperspectral images are predictive of both relative water content and nitrogen content. The coefficients of determination (R2) for all graphs comparing the predicted relative water content to the reference relative water content of sorghum averaged 0.90 while the same graphs for maize averaged 0.64. The coefficients of determination for all graphs comparing the predicted nitrogen content to the reference nitrogen content of sorghum averaged 0.85 while the same graphs for maize averaged 0.61. Models built only with the spectral features for sorghum were also predictive of both relative water content and nitrogen content. The coefficients of determination for all graphs comparing the predicted relative water content to the reference relative water content of sorghum averaged 0.91 while the same graphs for nitrogen content in sorghum averaged 0.85. The nitrogen content models developed using the data from the Tx7000 genotype are highly predictive of both Tx7000 and B35 but not highly predictive of Tx623. However, models developed using the data from Tx623 are highly predictive of all three genotypes. Another important finding from this study was that the water and nitrogen signals overlap and the most predictive models are developed from data where water and nitrogen vary continuously. Models to predict one factor that do not account for variation in the other factor are not very accurate. The second experiment utilized hyperspectral imaging to characterize RWC and N content of maize. Models for RWC and N content were developed using spectral and morphological features. The models developed for maize were not as predictive as the models for sorghum but they were still predictive of RWC and N content for the models developed using all six genotypes and the models developed using the data from the individual genotypes. Models built using the four half-sibling genotypes were not more predictive than the models based on all six genotypes. The final portion of this thesis explored predictions across species using both the sorghum and maize data. We found that models developed using only sorghum were not predictive of the maize reference measurements. However, when the sorghum and maize data were combined and used to generate models, both the RWC model and the N content model were highly predictive for both reference measurements

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

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    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

    Biosystems and Food Engineering Research Review 28

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    The Twenty Eighth Annual Research Review describes the ongoing research programme in the School of Biosystems and Food Engineering at University College Dublin over the academic year 2022/23, from the collective research body within the school comprising our academic staff, technical staff, research staff and our early-stage researchers. The research programme covers two main focal areas: Food and Process Engineering as well as Energy and the Environment. Each of these areas is divided into sub-themes as indicated in the Table of Contents, which also includes the name of the research scholar (in bold); the title of the research and the nature of the research programme. The review also highlights the award winners for presentational excellence at the 28th Annual Biosystems and Food Engineering Research Seminar, which was held online in virtual format on Thursday 16th March 2023. The awardees for 2023 are listed in the Appendix A

    Making sense of light: the use of optical spectroscopy techniques in plant sciences and agriculture

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    As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.info:eu-repo/semantics/publishedVersio
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