Dissertations / Theses on the topic 'Detecting Crop'

Rhizoctonia so/ani is the causative agent of wirestem on Brassica crops. R. so/ani is a species complex comprising genetically. distinct groups known as anastomosis groups (AGs). Knowledge of which AGs are responsible for disease is necessary to formulate appropriate management strategies. Important knowledge about the specific AGs responsible for disease in UK Brassica crops and where in the production chain the fungus became associated with plants was lacking. A survey of UK .f3rassica o/eracea crops was completed to. establish which anastomosis groups of Rhizoctonia so/ani were associated with module raised plants. No R. so/ani was recovered from plants collected directly from UK propagators. R. so/ani was identified from asymptomatic stem bases collected from field crops using classical mycology, suggesting field inoculum is important. Such data suggests UK propagation houses have high standards of hygiene and future research should concentrate on elucidating the field biology of R. so/ani. The pathogenicity of the recoveredi.isolates was demonstrated across a range of crops, including B. o/eracea. However, the isolates were not pathogenic to monocotyledonous species suggesting these may act as effective break crops. Sequence data were generated from ribosomal and ~-tubulin regions and the anastomosis groups of the R. so/ani isolates identified using parsimony and Bayesian based phylogenetic methods. Both methods suggested the majority of isolates recovered from the stem bases of UK B. o/eracea plants belonged to AG2-1 and all but one of the remaining isolates belonged to AG4 HGii. These data are consistent with research from the USA, however this is the first report for UK crops. AG determination using nucleotide sequence information proved more successful and less time consuming than classical mycological approaches. AG2-1 formed three distinct clusters in all analyses suggesting this' subgroup is genetically diverse, a conclusion supported by the problems encountered when investigating AGs using classical mycology. The monophyly of genera Ceratobasidium and Thanatephorus were investigated using constrained analyses, however no firm conclusions could be drawn to accept or reject this hypothesis. Protocols were devel~edto' deteet AG 'and ·spedfic sub~groups in plant and ·soil samples using real-time peR. Soil testing suggested AG2-1 was more frequently detected in samples collected from the upper 10 cm of fields used for B. oleracea production. Such information is consistent with other research and suggests the growth of R. solani may be limited by lack of air filled pores within soil matrixes. The molecular methods were used to investigate the spatial and temporal association of R. solani with field grown B. oleracea plants. The molecular protocols confirmed that R. solani AG2-1 became rapidly associated with a large number of B. oleracea plants. The principle arguments for such an association hinged on identifying the dominant behaviour expressed by the fungus. Rapid colonisation of the root system and stem base would benefit saprophytic behaviour of R. solani as the fu.ngus could capitalise when the crop lifecycle was complete. As a pathogen the benefits of early colonisation are clear and perhaps the reason for low disease levels can be explained by the absence of suitable environmental conditions for disease to progress. A third hypothesis could be that R. solani may form mycorrhizal associations with B. oleracea crops.

<p> For cotton (<i>Gossypium hirsutum</i> L.) to grow and develop normally, plants need to uptake the necessary amount of nutrients and use those nutrients in a beneficial fashion. It is recognized that cotton needs a certain tissue concentration of ions to achieve and maintain growth rates (Siddiqi et al., 1987). One of the most essential and abundant nutrients in cotton is potassium (K), second only by mass to nitrogen (N) (Marschner, 1995; Oosterhuis et al., 2013). Potassium exists in the soil in four separate pools and moves through soil to roots mainly through diffusion (Rengel &amp; Damon, 2008; Samal et al., 2010; Ogaard et al., 2001). Potassium plays a vital role in plant growth and metabolism. </p><p> The objectives of this study were to determine the Michaelis-Menten parameters for the high-affinity transport system (HATS) and low-affinity transport system (LATS) uptake mechanisms of cotton, observe how K is partitioned throughout the cotton plant over a growing season with differing K fertilization rates, and to determine if cultivars differed in values from currently available indices formulated for N-status detection from active sensors. It also set out to determine if these N-sensitive indices were sensitive to leaf K concentration and available K2O in the soil, and to evaluate the role these indices play in predicting yield. It was hypothesized that a high K hydroponic environment would lead to more K uptake by cotton roots, which would lead to an increase in VMAX and KM. It was also hypothesized that with increased K fertilization, there would be greater K uptake and larger shift to reproductive components due to the plant having more than enough K in all other parts enabling it to send more to the reproductive components, and that greater K rates would lead to higher yields across all cultivars. It was believed that normalized difference vegetation index (NDVI) would more accurately predict leaf K, available K2O, and yield than normalized difference red edge (NDRE), that NDVI and NDRE would more accurately determine the K parameters chosen than canopy chlorophyll content index (CCCI), due to the strong influence of the red-edge band in the index and that yield would be most accurately predicted by the CCCI, due to yield being influenced by both chlorophyll content and biomass, and the CCCI involving the red-edge band to reflect chlorophyll content and the near infrared band to detect biomass.</p>

Standoff detection of targets using volatiles is essential when considering substances that are hazardous or dangerous, or for which the presence or location is unknown. For many invasive biological threats, their presence is often not realized until they have begun visibly affecting and spreading through crops or forests. The fungus Raffaelea lauricola is a biothreat vectored by the invasive beetle Xyleborus glabratus, or redbay ambrosia beetle (RAB), whose presence in avocado groves is currently detectable by visual inspection. Once visually identified, the affected trees must be removed and destroyed to protect those remaining trees. However, if the fungus is identified via standoff volatile detection, there is anecdotal evidence that it can be treated with propiconazole and saved from progression to the fatal laurel wilt disease. As a result of the rapid spread of R. lauricola and the quick death of trees, early detection through standoff methods is essential. The only current method of pre-symptomatic identification is canine detection. Canines are sensitive and selective biological detectors that can trace odors to their source, despite the presence of a variety of background odors. The present research evaluated the volatile organic compounds (VOCs) of the laurel wilt disease and R. lauricola using headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Additionally, a new method for odor collection and presentation to trained detection canines was developed. Knowledge of the disease and standoff volatile detection capabilities are improved using this information.

In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods.

Aflatoxin M1 (AFM1) is frequently found in milk and dairy products. It is a metabolite formed in cows from aflatoxin B1 (AFB1), contained in animal feeds. In cheese production AFM1 distributes between curds and whey. In this study, cows were fed 64 µg/AFB1/d for the high treatment, and 5 µg/AFB1/d for the low treatment, to obtain milk contaminated with AFM1 over the 0.5 µg/L and under 0.05 µg/L restrictions, respectively. Cheese was manufactured with milk contaminated with AFM1 at 0.8 and 0.03 ìg/kg by the higher and lower treatment, respectively. Two commercial cheeses were elaborated: a hard-aged cheese (cheddar cheese) and soft high moisture cheese (fresco cheese) to evaluate whether the cheese type had any impact on AFM1 analysis. AFM1 was extracted from cheese using immunoaffinity columns. Analyses were carried out by using high pressure liquid chromatography (HPLC) as the reference method and fluorometry as a method of validation. Analysis was by 2-way fixed factor analyses. AFM1 was detected in all samples by both methods of analysis. There were no detectable statistical differences between cheese types (P>0.05). AFM1 content was significantly different between the high and low concentration of AFB1 used to make the cheese type (P<0.01). Our regression model shows a linear relationship between fluorometry and HPLC methods; R2 = 0.9141 from cheddar cheese and R2 = 0.9141 from fresco cheese. There were no statistical differences between methods of analysis (P>0.05). Carryover of AFM1 in cheese detected by fluorometry in cheddar cheese was 163% and 80% for high and low treatments, respectively, and in fresco cheese was 119 and 133 for high and low treatments, respectively. These carryovers are below that reported in the literature. Results suggest that fluorometry is a simple and reliable AFM1 detection method for screening samples of complex matrices such as cheese.

Ergovaline is produced by the endophyte Neotyphodium coenophialum (Morgan-Jones and Gams) in tall fescue (Schedonorus arundinacea (Schreb.) Dumort. = Festuca arundinacea Schreb.) and is blamed for a multitude of costly livestock disorders. Testing of pastures is common in both research and on farm situations. Since ergovaline is known to be unstable and affected by many variables, the objective of this study was to determine the effect of sample handling and storage on the stability of this compound. Homogeneous milled tall fescue sub-samples were analyzed for ergovaline concentration using HPLC after a range of sample handling procedures or storage. Ergovaline was unstable in milled material after 24 hours in storage, regardless of temperature. The decrease in ergovaline after 24 hours ranged from 17 to 60%. These results show that tall fescue sample handling and storage have a significant effect on ergovaline concentrations. In conclusion, accurate laboratory analysis of ergovaline content may require that samples be transported immediately to the laboratory on ice for immediate analysis. Most laboratories are not equipped for same day analysis, therefore researchers and producers should acknowledge that laboratory ergovaline results may be lower than the actual content in the field.

Dans le contexte agricole actuel, il est nécessaire de réduire l’utilisation des produits phytosanitaires contre les mauvaises herbes. Le désherbage localisé présente une alternative prometteuse pour limiter les coûts et l’impact environnemental. Cependant, la localisation automatique des adventices n’est pas une tâche facile car elle présente plusieurs défis scientifiques et technologiques. L’objectif de cette thèse est de proposer des méthodes de traitement d’images et d’intelligence artificielle pour la localisation des adventices en grandes cultures. Dans ce cadre, nous avons abordé deux problématiques, la détection des rangées de culture et la détection des adventices. Deux méthodes ont été proposées pour la détection des rangées de culture. La première méthode combine la transformée de Hough et l’algorithme de regroupement linéaire itératif SLIC. La deuxième, quant à elle, utilise une approche totalement nouvelle basée sur l’apprentissage profond. Ces deux méthodes ont été utilisées pour détecter les adventices inter-rang et celles qui sont en contact avec les rangées de culture. Pour tendre vers une meilleur efficacité, deux nouvelles méthodes de détection d’adventices par apprentissage machine, entièrement automatiques ont été développées. L’originalité de ces méthodes est que l’apprentissage est effectué sur des données annotées automatiquement. La première méthode est basée sur l’apprentissage profond tandis que la seconde génère des modèles à partir de descripteurs profonds et un classifieur à classe unique. Les résultats obtenus sur des données réelles montrent l’intérêt des approches proposées<br>In the current agricultural context, there is a need to reduce the use of pesticides for weed control. Localized weed control presents a promising option to limit costs and environmental impact. However, automatic weed detection is not an easy task and presents several scientific and technological challenges. The objective of this thesis is to propose image processing and artificial intelligence methods for weed detection in field crops. Within this framework, we addressed two issues, crop row detection and weed detection. Two methods were proposed for crop row detection. The first method combines the Hough transform and the simple linear iterative clustering SLIC. The second one uses a completely new approach using deep learning. Both methods were used to detect inter-row weeds and weeds in contact with crop rows. To achieve greater efficiency, two new fully automatic machine learning weed detection methods have been developed. The originality of these methods is that learning is carried out on automatically annotated data. The first method is based on deep learning while the second method generates models from deep features and one-lass classifier. The results obtained on real data show the interest of the proposed approaches

Characteristics of quantitative resistance in pea (Pisum sativum L.) to Erysiphe pisi DC, the pathogen causing powdery mildew, were investigated. Cultivars and seedlines of pea expressing quantitative resistance to E. pisi were identified and evaluated, by measuring the amounts of pathogen present on plant surfaces in field and glasshouse experiments. Disease severity on cv. Quantum was intermediate when compared with that on cv. Bolero (susceptible) and cv. Resal (resistant) in a field experiment. In glasshouse experiments, two groups of cultivars, one with a high degree of resistance and the other with nil to low degrees of resistance to E. pisi, were identified. This indicated either that a different mechanism of resistance applied in the two groups, or that there has been no previous selection for intermediate resistance. Several other cultivars expressing quantitative resistance were identified in a field experiment. Quantitative resistance in Quantum did not affect germination of E. pisi conidia, but reduced infection efficiency of conidia on this cultivar compared with cv. Pania (susceptible). Other epidemiological characteristics of quantitative resistance expression in Quantum relative to Pania were a 33% reduction in total conidium production and a 16% increase in time to maximum daily conidium production, both expressed on a colony area basis. In Bolero, the total conidium production was reduced relative to Pania, but the time to maximum spore production on a colony area basis was shorter. There were no differences between the cultivars in pathogen colony size or numbers of haustoria produced by the pathogen. Electron microscope studies suggested that haustoria in Quantum plants were smaller and less lobed than those in Pania plants and the surface area to volume ratios of the lobes and haustorial bodies were larger in Pania than in Quantum. The progress in time and spread in space of E. pisi was measured in field plots of cultivars Quantum, Pania and Bolero as disease severity (proportion of leaf area infected). Division of leaves (nodes) into three different age groups (young, medium, old) was necessary because of large variability in disease severity within plants. Disease severity on leaves at young nodes was less than 4% until the final assessment at 35 days after inoculation (dai). Exponential disease progress curves were fitted for leaves at medium nodes. Mean disease severity on medium nodes 12 dai was greatest (P<0.001) on Bolero and Pania (9.3 and 6.8% of leaf area infected respectively), and least on Quantum (1.6%). The mean disease relative growth rate was greatest (P<0.001) for Quantum, but was delayed compared to Pania and Bolero. Gompertz growth curves were fitted to disease progress data for leaves at old nodes. The asymptote was 78.2% of leaf area infected on Quantum, significantly lower (P<0.001) than on Bolero or Pania, which reached 100%. The point of inflection on Quantum occurred 22.8 dai, later (P<0.001) than on Pania (18.8 dai) and Bolero (18.3 dai), and the mean disease severity at the point of inflection was 28.8% for Quantum, less (P<0.00l) than on Pania (38.9%) or Bolero (38.5%). The average daily rates of increase in disease severity did not differ between the cultivars. Disease progress on Quantum was delayed compared with Pania and Bolero. Disease gradients from inoculum foci to 12 m were detected at early stages of the epidemic but the effects of background inoculum and the rate of disease progress were greater than the focus effect. Gradients flattened with time as the disease epidemic intensified, which was evident from the large isopathic rates (between 2.2 and 4.0 m d⁻¹) Some epidemiological variables expressed in controlled environments (low infection efficiency, low maximum daily spore production and long time to maximum spore production) that characterised quantitative resistance in Quantum were correlated with disease progress and spread in the field. These findings could be utilised in pea breeding programmes to identify parent lines from which quantitatively resistant progeny could be selected.

La bacteriose a e. Chrysanthemi pv. Dianthicola (echr) facteur limitant de la production du dahlia est transmise par la multiplication vegetative. Afin de proposer une methode de diagnostic plus precise que la detection visuelle, les methodes immunoenzymatiques ont ete etudiees et adaptees pour la detection d'echr dans les tissus du dahlia. La methode das-elisa (double antibody sandwich) est evaluee par rapport aux methodes de diagnostic de reference (isolement et immunofluorescence). Son utilisation, pour l'analyse sanitaire du materiel de propagation vis-a-vis d'echr seul et associe eventuellemnt a la mosaique du dahlia (damy), est etudiee en vue d'une selection sanitaire. Les etudes effectuees pour optimiser les reactifs, pour determiner les parametres pouvant modifier la reaction antigene-anticorps

Mefenoxam has been a premier compound for Phytophthora disease control in the nursery industry for 30 years. The primary objectives of this research were to examine whether Phytophthora species have developed resistance to this compound and to investigate fungicide resistance management strategies. Phytophthora nicotianae, a destructive pathogen of numerous herbaceous and some woody ornamental plants, was used as a model system. P. cinnamomi, a major pathogen of a wide range of tree species and shrub plants, was also included for comparison. Twenty-six isolates of P. nicotianae were highly resistant to mefenoxam with a mean EC50 value of 326.5 µg/ml while the remaining 70 were sensitive with an EC50 of <0.01 µg/ml (Label rate: 0.08µg/ml). All resistant isolates were recovered from herbaceous annuals and irrigation water in 3 Virginia nurseries. Resistant isolates were compared with sensitive ones using seedlings of Lupinus â Russell Hybridsâ in the absence of mefenoxam for relative competitive ability. Resistant isolates out-competed sensitive ones within 3 to 6 sporulation cycles. Resistant isolates exhibited greater infection rate and higher sporulation ability than sensitive ones. No mefenoxam resistant isolates were identified in P. cinnamomi. All 65 isolates of P. cinnamomi were sensitive to mefenoxam with an EC50 of < 0.04 ï ­g/ml. Attempts to generate mutants with high resistance to mefenoxam through UV mutagenesis and mycelial adaptation were not successful. However, there were significant reductions in sensitivity to mefenoxam; those slightly resistant mutants carried fitness penalties, which may explain why P. cinnamomi remains sensitive to mefenoxam. The effect of propamocarb hydrochloride on different growth stages of Phytophthora nicotianae was evaluated in search for an alternative fungicide. Propamocarb greatly inhibited sporangium production, zoospore motility, germination and infection. However, it has little inhibition of mycelial growth and infections. Propamocarb can be used as an alternative fungicide to mefenoxam where mefenoxam resistance has become problematic. However, it must be used preventively; i.e. before infections occur. The genetic inheritance of mefenoxam resistance in P. nicotianae was studied using F1 progenies of a cross between resistant and sensitive isolates. The F1 progenies segregated for mefenoxam resistance in ratio of 1R:1S, indicating the mefenoxam resistance is controlled by a single dominant gene. One RAPD marker putatively linked to resistant locus in repulsion phase was obtained by bulked segregant analysis and was converted to the SCAR marker. This marker is capable of differentiating mefenoxam resistant populations from sensitive populations included in this study.<br>Ph. D.

Precision agriculture requires automated systems for weed detection as weeds compete with the crop for water, nutrients, and light. The purpose of this study is to investigate the use of machine learning methods to classify weeds/crops in agriculture. Statistical methods, support vector machines, convolutional neural networks (CNNs) are introduced, investigated and optimized as classifiers to provide high accuracy at high vehicular speed for weed detection. Initially, Support Vector Machine (SVM) algorithms are developed for weed-crop discrimination and their accuracies are compared with a conventional data-aggregation method based on the evaluation of discrete Normalised Difference Vegetation Indices (NDVIs) at two different wavelengths. The results of this work show that the discrimination performance of the Gaussian kernel SVM algorithm, with either raw reflected intensities or NDVI values being used as inputs, provides better discrimination accuracy than the conventional discrete NDVI-based aggregation algorithm. Then, we investigate a fast statistical method for CNN parameter optimization, which can be applied in many CNN applications and provides more explainable results. This study specifically applies Taguchi based experimental designs for network optimization in a basic network, a simplified inception network and a simplified Resnet network, and conducts a comparison analysis to assess their respective performance and then to select the hyper parameters and networks that facilitate faster training and provide better accuracy. Results show that, for all investigated CNN architectures, there is a measurable improvement in accuracy in comparison with un-optimized CNNs, and that the Inception network yields the highest improvement (~ 6%) in accuracy compared to simple CNN (~ 5%) and Resnet CNN counterparts (~ 2%). Aimed at achieving weed-crop classification in real-time at high speeds, while maintaining high accuracy, the algorithms are uploaded on both a small embedded NVIDIA Jetson TX1 board for real-time precision agricultural applications, and a larger high throughput GeForce GTX 1080Ti board for aerial crop analysis applications. Experimental results show that for a simplified CNN algorithm implemented on a Jetson TX1 board, an improvement in detection speed of thirty times (60 km/hr) can be achieved by using spectral reflectance data rather than imaging data. Furthermore, with an Inception algorithm implemented on a GeForce GTX 1080Ti board for aerial weed detection, an improvement in detection speed of 11 times (~2300 km/hr) can be achieved, while maintaining an adequate detection accuracy above 80%. These high speeds are attained by reducing the data size, choosing spectral components with high information contents at lower resolution, pre-processing efficiently, optimizing the deep learning networks through the use of simplified faster networks for feature detection and classification, and optimizing computational power with available power and embedded resources, to identify the best fit hardware platforms.

Early identification and control of insect pests and diseases is a key aspect of profitable crop production, especially for high input, high value horticultural crops. Remote sensing approaches using sensor technologies to detect insect pests and diseases have been previously demonstrated in a range of field crops and were researched in this project as a tool for plant health monitoring in chilli crops. A methodology for image capture using a multispectral camera mounted on an Unmanned Aerial Vehicle (UAV) and image processing based on distribution of individual pixel values in collected images was developed. This methodology was demonstrated to be as effective as manual crop scouting in early detection of insect pest and disease affected plants within a crop but could be automated to significantly reduce the cost of crop health monitoring. Initial method development trials demonstrated that detectable changes in NDVI, but not temperature changes measured using a thermal camera, occurred on leaves affected by bacterial spot disease before obvious visible symptoms were apparent. Bacterial spot (Xanthomonas euvesicatoria (Xeu)) is a ubiquitous disease infecting field-grown chilli crops, particularly during warm and humid conditions, and symptoms of infection were not apparent until about 7 days after inoculation of leaves with the pathogen. The age of inoculated leaves did not significantly affect the rate of change of NDVI. Non-inoculated leaves tended to have a lower NDVI value on plants with a greater number of inoculated leaves than on plants with none or few inoculated leaves. iii Aphids Myzus persicae (Sulzer) cause significant damage to chilli crops both directly via feeding on the host plant and indirectly as vectors for virus transmission. Reflectance data, obtained by multispectral, hyperspectral and thermal sensors, showed that the reflectance of aphid infested leaves in near infrared wavelengths decreased with time as the aphid population infesting a leaf increased. Remote sensing data acquired from low-altitude UAV flights deliver high spectral and spatial resolutions, with sufficient pixels representing individual leaf reflectance to allow detection of changes occurring when disease infection or insect pest infestation first occurs in part of a plant. This capacity for detection at early infection/infestation stage is crucial for effective management in high value horticultural crops. Conventional remote sensing approaches may detect changes occurring at a whole plant or region within a crop but lack the resolution capacity to readily detect changes at the sub-plant level. A five-band multispectral camera (MicaSense, RedEdge) and a low-altitude (15m) airborne platform provided adequate data, recording changes in reflectance imagery. The effectiveness of multispectral imagery decreased as flight altitude increased. The project has demonstrated that early identification of insect pest and pathogen-induced plant stress in chilli crops can be achieved using a methodology that can be automated to deliver a low-cost strategy for horticultural producers

Remote sensing - the acquisition of information about an object or phenomenon without making physical contact with the object - is applied in a multitude of different areas, ranging from agriculture, forestry, cartography, hydrology, geology, meteorology, aerial traffic control, among many others. Regarding agriculture, an example of application of this information is regarding crop detection, to monitor existing crops easily and help in the region’s strategic planning. In any of these areas, there is always an ongoing search for better methods that allow us to obtain better results. For over forty years, the Landsat program has utilized satellites to collect spectral information from Earth’s surface, creating a historical archive unmatched in quality, detail, coverage, and length. The most recent one was launched on February 11, 2013, having a number of improvements regarding its predecessors. This project aims to compare classification methods in Portugal’s Ribatejo region, specifically regarding crop detection. The state of the art algorithms will be used in this region and their performance will be analyzed.

碩士<br>國立宜蘭大學<br>生物機電工程學系碩士班<br>100<br>The objective of this research is to use Chlorophyll Fluorescence imaging as a reverse process examination to observe the spraying of pesticide and high-humidity and high-temperature on plants, working with a visual process to identify and analyze disease spots (black dots in an image) in a non-destructive way. The main program of the disease spot recognition was written by using graphical language LabVIEW software. The visual imaging procedure was created through the Vision Assistant program, and was combined with the main program for research use. In this research, using 430~470 nm excitation light source and a 684±10 nm band filter with near-infrared camera was used to acquire Chlorophyll Fluorescence image of the plant; the appearance of the crop leaf surface can be fully shown by this procedure. Before taking the image, the processing time in the dark and spot recognition image processing stages must be discovered. By using proper open, Gaussian, exponential, median filter, local threshold, removal of border objects, and then processing in the dark for 20 minutes, the success rate of disease spot identification on the Da-ge cabbage and the Toyonoka strawberry seedling can reach to 80% or more. The rate of similarity between the real spot area and recognized spot area in the detection system on the Da-ge Cabbage is 86.07 ±9.79 %, and 85.92 ±8.46 % on the Toyonoka Strawberry. Spraying of pesticide and high-humidity high-temperature test results: after spraying pesticide, spots start to emerge on the Da-ge cabbage after 22 hours with a size of 0.11 mm². The spots on the Toyonoka strawberry emerged after 16 hours with a size of 0.50 mm². Chemical spots grow bigger according to the time that has been given; after 72 hours, the size of the spots on the Da-ge cabbage turned into 0.66 mm², and the spots on the Toyonoka strawberry turned into 1.32 mm². Under high-humidity and high-temperature conditions, the Da-ge cabbage disease spots start to emerge after 64 hours with a size of 0.11 mm², and 52 hours with a size of 0.11 mm² on the Toyonoka strawberry. disease spots grow bigger according to the time that has been given; after 96 hours, the Disease spots on the Da-ge cabbage grow into a size of 1.10 mm², and to 6.00 mm² on the Toyonoka strawberry. This study deveeops the disease spot detection system of plants, and furthermore, to develop the potential of the portable spot detection system. Therefore, an examination of plants against spots can be made instantly in order.

Non-invasive detection of three internal disorders of fruit of commercial relevance to Queensland horticulture was considered: (i) diffuse browning of apple fruit; (ii) gelling defect of mandarin fruit; and (iii) translucency of pineapple fruit. Visible - short wave near infrared spectroscopy (vis-SWNIRS) is in commercial use for non-invasive field and in line assessment of fruit dry matter and soluble solids content of mango and apple. Some claims exist for commercially available instrumentation for sorting of fruit internal defects, but no assessment of such systems exists in the scientific literature. Four vis-SWNIRS instruments were trialled, varying in optical geometry: (i) the Integrated spectronics’s ‘Nirvana’ handheld instrument, operating with an interactance optical geometry; (ii) a purpose built unit employing a 300W halogen illumination source in a partial transmittance geometry, ‘IDD0’; (iii) the MAF Roda Insight2 unit, employing a 150W halogen lamp and operated in a full transmission geometry, and (iv) the MAF Roda IDD2 unit, employing four near infrared light emitting diodes and operated in a full transmission geometry. A number of reference methods were assessed for scoring level of apple flesh browning, including visual assessment, image analysis (% cross section area affected), chromameter CIE Lab values (L* and a* value) and juice Abs420nm, of which visual scoring on a 5 point scale was recommended. Chlorophyll fluorescence and acoustic resonant frequency was poorly related to extent of defect, and thus these non-invasive techniques are not recommended. Apple flesh browning was best assessed using visible-shortwave NIRS in a transmission optical geometry, with a typical PLSR model R2cv = 0.83 and RMSECV = 0.63 (5 point visual scale). Of different binary (good and defect fruit) classification approaches trialled, the best result was achieved using PLS discriminant analysis (PLS-DA) method, followed by linear discriminant analysis. More than 95% of defect fruit were predicted as defect (true negative rate) at the expense of having 10-20% of good fruit falsely predicted as defect fruit (false negative rate), across six populations. A number of reference methods were also assessed for scoring level of granulation in mandarin fruit, including visual assessment (5 point scale), chromameter CIE Lab (L and colour index values) and % juice recovery, of which visual score and % juice recovery were recommended. Mandarin granulation, indexed by either visual score or % juice recovery, was best non-invasively assessed using vis-SWNIRS in a transmission optical geometry, with a typical PLSR model R2cv = 0.74 and RMSEP = 3.6 (% juice recovery). PLS-DAwas able to predict well for good fruit with up to 87 and 100% of good fruit as good fruit (true positive rate) using the IDD0 and MAF Roda Insight2 units, respectively. Defect fruit were wrongly predicted as good fruit (false positive) using both the machine with best result (97 % true negative rate) obtained with PLS-DA using IDD0 unit. Translucency in pineapple, indexed by either a (5 point) visual score or image analysis was assessed using vis-SWNIR spectroscopy. Typical PLSR calibration results for models developed using the range 700-1000 nm results were modest (R2cv = 0.58, RMSECV = 0.55 on 5 point scale), and prediction results were poor (R2p = 0.41, RMSEP = 0.93. For binary classification, PLS-DA was able to predict 98.7% of good fruit as good (true positive rate) while only 34% of defect fruit were predicted defect (true negative rate) based on visual translucent score. Sorting involves a trade-off between yield and quality. The use of a receiver operating characteristics curve (ROC) and a sorting optimisation curve (SOC) was explored for the comparison of binary classifiers and the optimisation of sorting set point. The need to adjust the sorting set point to maintain a desired quality specification (e.g. % of defect fruit in accepted class) as population mean and spread (SD) for the defect varies is explained. Internal defects of fruit under consideration are well detected and sorted for based under transmission optical geometry with visual defect score as a reference parameter.

碩士<br>國立宜蘭大學<br>生物機電工程學系碩士班<br>102<br>This objective of this research was to establish a portable chlorophyll fluorescence imaging detection system, which could detect the information for the diseased leaf earlier than the naked eyes. In this study, by using the crop fluorescence characteristic experiments, high-temperature stress experiments and outdoor experiments to establish a portable chlorophyll fluorescence imaging detection system efficacy. The main object of the research trial was the Toyonoka Strawberry seedling and it was 15 days old. The test results for fluorescence characteristic shown the excitation wavelength of the health and disease for the Toyonoka Strawberry seedling was 460 nm, the emission wavelength was 680 nm, the spectral range of relative strengths excitation was between 420 nm ~ 490 nm, the spectral range of relative strengths emitted light was between 670 nm ~ 690 nm. In the high-temperature stress experiments, the experimental group, 10 Strawberry seedling, there were five successful inductions of disease spot, and taking one of the shooting results from the portable chlorophyll fluorescence imaging detection system. During one test, the naked eye found disease spot after 112 hours to 128 hours, the detection system found the spots in 96 hours to 112 hours, 16 hours earlier than the naked eye. In this study, by using the erosion, dilation, proper open, Gaussian Blur, Look-up table, edge detection, local threshold and remove the size of the object, successful removal dark treatment which in order to enhance fluorescent image clarity and improve the success rate of the disease spot recognition processing time, under the processing without the dark treatment, there are two kinds of portable chlorophyll fluorescence imaging detection system for the disease spots, the success rates were 70% and 85%, the accuracy rates were 28.6% and 76.5% respectively. In an outdoor experiment, the success rate was 73.3%, and accuracy was 16.6%. This study successfully established a portable chlorophyll fluorescence imaging detection system, which can find disease spot early and detect it at outdoor.

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2009.<br>Title from title screen (site viewed February 25, 2010). PDF text: xvii, 164 p. : ill. (chiefly col.) ; 7 Mb. UMI publication number: AAT 3386611. Includes bibliographical references. Also available in microfilm and microfiche formats.

The ability to detect crop rows and release sensors in large areas to ensure homogeneous coverage is crucial to monitor and increase the yield of crop rows. Aerial robotics in the agriculture field helps to reduce soil compaction. We report a release mechanics system based on image processing for crop row detection, which is essential for field navigation-based machine vision since most plants grow in a row. The release mechanics system is fully automated using embedded hardware and operated from a UAV. Once the crop row is detected, the release mechanics system releases lightweight, flexible multi-sensory devices on top of each plant to monitor the humidity and temperature conditions. The capability to monitor the local environmental conditions of plants can have a high impact on enhancing the plant’s health and in creasing the output of agriculture. The proposed algorithm steps: image acquisition, image processing, and line detection. First, we select the Region of Interest (ROI) from the frame, transform it to grayscale, remove noise, and then skeletonize and remove the background. Next, apply a Hough transform to detect crop rows and filter the lines. Finally, we use the Kalman filter to predict the crop row line in the next frame to improve the performance. This work’s main contribution is the release mechanism integrated with embedded hardware with a high-performance crop row detection algorithm for field navigation. The experimental results show the algorithm’s performance achieved a high accuracy of 90% of images with resolutions of (900x470) the speed reached 2 Frames Per Second (FPS).

Wong Wai Mei.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.<br>Includes bibliographical references (leaves 175-192).<br>Abstracts in English and Chinese.<br>Declaration --- p.ii<br>Acknowledgements --- p.iii<br>Abstract --- p.iv<br>Abbreviation --- p.vi<br>Table of Contents --- p.vii<br>Chapter Chapter 1 --- Introduction --- p.1<br>Chapter Section I --- The Making of Genetically Modified Organisms --- p.2<br>Chapter 1.1 --- Conventional breeding in agriculture --- p.2<br>Chapter 1.2 --- What is genetic engineering? --- p.4<br>Chapter 1.3 --- Plant transformation --- p.5<br>Chapter 1.3.1 --- Agrobacterium-mediated --- p.6<br>Chapter 1.3.2 --- Direct gene transfer --- p.8<br>Chapter 1.3.2.1 --- Microparticle bombardment --- p.8<br>Chapter 1.3.2.2 --- Protoplasts --- p.9<br>Chapter 1.3.3 --- Gene silencing --- p.10<br>Chapter 1.4 --- Examples of genetically modified crops --- p.13<br>Chapter 1.5 --- Foreign genes commonly found in transgenic plants --- p.14<br>Chapter Section II --- Benefits and Environmental Concern of GMOs --- p.17<br>Chapter 2.1 --- Mechanism of GMO --- p.17<br>Chapter 2.1.1 --- Herbicide tolerant crops --- p.18<br>Chapter 2.1.2 --- Insect resistant crops --- p.19<br>Chapter 2.1.3 --- Delayed ripening crops --- p.20<br>Chapter 2.1.4 --- Virus resistant crops --- p.20<br>Chapter 2.2 --- Benefits of GMOs --- p.21<br>Chapter 2.3 --- Impact of GM foods to human health and the environment --- p.22<br>Chapter 2.3.1 --- Human health --- p.22<br>Chapter 2.3.1.1 --- GM potatoes --- p.23<br>Chapter 2.3.1.2 --- CaMV risks? --- p.24<br>Chapter 2.3.1.3 --- Food allergy --- p.25<br>Chapter 2.3.2 --- Environmental concerns --- p.26<br>Chapter 2.3.2.1 --- Horizontal gene transfer --- p.27<br>Chapter 2.3.2.1.1 --- Selectable marker genes --- p.27<br>Chapter 2.3.2.1.2 --- Herbicide resistant genes --- p.29<br>Chapter 2.3.2.1.3 --- Insect resistant genes --- p.29<br>Chapter 2.3.2.2 --- Ecology --- p.30<br>Chapter 2.3.2.2.1 --- Monarch butterfly --- p.30<br>Chapter Section III --- Future developments of GMO --- p.32<br>Chapter 3.1 --- Designer Food and engineered plants --- p.32<br>Chapter 3.1.1 --- Insect resistance --- p.33<br>Chapter 3.1.2 --- Viral resistance --- p.33<br>Chapter 3.1.3 --- Fungal resistance --- p.34<br>Chapter 3.1.4 --- Nutritional quality --- p.34<br>Chapter 3.1.5 --- Modifications of oil composition --- p.35<br>Chapter 3.1.6 --- Medical applications --- p.37<br>Chapter 3.1.7 --- Environmental applications --- p.40<br>Chapter 3.1.7.1 --- Tolerance to high salinity and drought --- p.40<br>Chapter 3.1.7.2 --- Tolerance to frost --- p.41<br>Chapter 3.1.7.3 --- Bioremediation --- p.42<br>Chapter 3.1.7.4 --- Biodegradable products --- p.43<br>Chapter Section IV --- Regulation of GMO --- p.44<br>Chapter 4.1 --- The question of labeling --- p.44<br>Chapter 4.1.1 --- Moral and ethical issues --- p.44<br>Chapter 4.1.2 --- Animal welfare --- p.45<br>Chapter 4.2 --- International practice in GMO labeling --- p.46<br>Chapter 4.2.1 --- United States of America --- p.46<br>Chapter 4.2.2 --- Canada --- p.48<br>Chapter 4.2.3 --- European Union --- p.49<br>Chapter 4.2.4 --- Australia and New Zealand --- p.50<br>Chapter 4.2.5 --- Japan --- p.51<br>Chapter 4.2.6 --- Republic of Korea --- p.52<br>Chapter 4.2.7 --- China --- p.53<br>Chapter 4.2.8 --- Taiwan --- p.53<br>Chapter 4.2.9 --- Hong Kong --- p.54<br>Chapter Section V --- Uses of crops --- p.56<br>Chapter 5.1 --- Uses of crops --- p.56<br>Chapter 5.1.1 --- Soybean --- p.56<br>Chapter 5.1.2 --- Corn --- p.57<br>Chapter 5.1.3 --- Tomato --- p.58<br>Chapter 5.1.4 --- Potato --- p.59<br>Chapter 5.1.5 --- Rice --- p.60<br>Chapter 5.1.6 --- Rapeseed --- p.61<br>Chapter 5.1.7 --- Oil --- p.62<br>Chapter 5.2 --- "Food additives, hormones and flavourings" --- p.63<br>Chapter Chapter 2 --- Materials & Methods --- p.65<br>Chapter 2.1 --- Materials --- p.66<br>Chapter 2.1.1 --- Growth media & agar --- p.66<br>Chapter 2.1.2 --- Reagents for agarose gel electrophoresis --- p.67<br>Chapter 2.1.3 --- Reagents for preparation of competent cells --- p.67<br>Chapter 2.1.4 --- Reagents for measurement of DNA concentration --- p.68<br>Chapter 2.1.4.1 --- Measurement of DNA concentration by PicoGreen --- p.68<br>Chapter 2.1.5 --- Reagents for Southern hybridization --- p.68<br>Chapter 2.2 --- Methods --- p.70<br>Chapter 2.2.1 --- Restriction endonuclease digestion --- p.70<br>Chapter 2.2.2 --- Agarose gel electrophoresis of DNA --- p.70<br>Chapter 2.2.3 --- DNA recovery from agarose gel --- p.71<br>Chapter 2.2.3.1 --- QIAquick® gel extraction --- p.71<br>Chapter 2.2.4 --- Ligation of purified DNA fragment into vector --- p.72<br>Chapter 2.2.5 --- Transformation --- p.72<br>Chapter 2.2.6 --- Rubidium chloride method for making competent cells --- p.12<br>Chapter 2.2.7 --- Plasmid DNA preparation --- p.73<br>Chapter 2.2.7.1 --- Concert Rapid Mini Prep --- p.73<br>Chapter 2.2.7.2 --- QIAprep® Miniprep --- p.74<br>Chapter 2.2.8 --- Extraction of plant genomic DNA --- p.75<br>Chapter 2.2.8.1 --- Qiagen DNeasy´ёØ Plant Mini Kit --- p.75<br>Chapter 2.2.9 --- Southern Hybridization --- p.75<br>Chapter 2.2.9.1 --- Denaturation --- p.76<br>Chapter 2.2.9.2 --- Blot transfer --- p.76<br>Chapter 2.2.9.3 --- Pre-hybridization --- p.77<br>Chapter 2.2.9.4 --- Synthesis of radiolabelled probe --- p.77<br>Chapter 2.2.9.5 --- Hybridization of radiolabelled probe on filter --- p.77<br>Chapter 2.2.9.6. --- Detection of hybridized probes --- p.78<br>Chapter 2.2.10 --- Measurement of DNA concentration --- p.78<br>Chapter 2.2.10.1 --- Determination of DNA on EtBr stained gel --- p.78<br>Chapter 2.2.10.2 --- Determination of DNA by UV spectrophotometer --- p.78<br>Chapter 2.2.10.3 --- Determination of DNA by PicoGreen --- p.79<br>Chapter 2.2.11 --- DNA sequencing --- p.80<br>Chapter 2.2.11.1 --- Automated sequencing by ABI Prism 377 --- p.80<br>Chapter Chapter 3 --- PCR Diagnostics --- p.81<br>Chapter 3.1 --- Applications of PCR to processed foods --- p.82<br>Chapter 3.1.1 --- DNA quality --- p.82<br>Chapter 3.1.2 --- PCR & Multiplex PCR --- p.83<br>Chapter 3.1.3 --- Choice of primers --- p.84<br>Chapter 3.1.4 --- Inhibitors --- p.84<br>Chapter 3.2 --- Materials & Methods --- p.85<br>Chapter 3.2.1 --- Selection of primers --- p.85<br>Chapter 3.2.2 --- Amplification of target sequences --- p.86<br>Chapter 3.2.3 --- Multiple amplification of target sequences --- p.87<br>Chapter 3.3 --- Results --- p.88<br>Chapter 3.4 --- Discussion --- p.93<br>Chapter Chapter 4 --- Quality Control in GMO detection --- p.95<br>Chapter 4.1 --- Standardization of pre- and post- PCR analysis --- p.96<br>Chapter 4.1.1 --- General guidelines --- p.96<br>Chapter 4.1.2 --- UV irradiation --- p.97<br>Chapter 4.1.3 --- Inactivation protocols --- p.93<br>Chapter 4.1.4 --- Positive and negative controls --- p.99<br>Chapter 4.1.5 --- PCR verification --- p.99<br>Chapter 4.1.6 --- Equipment decontamination --- p.100<br>Chapter 4.2 --- Materials & Methods --- p.101<br>Chapter 4.2.1 --- Selection of primers for external control --- p.101<br>Chapter 4.2.2 --- Development of the external control --- p.101<br>Chapter 4.2.3 --- Selection of primers for internal control --- p.103<br>Chapter 4.3 --- Results --- p.104<br>Chapter 4.4 --- Discussion --- p.107<br>Chapter Chapter 5 --- DNA extraction from food samples --- p.110<br>Chapter 5.1 --- Introduction --- p.111<br>Chapter 5.2 --- Reagents and Buffers for DNA extraction from food samples --- p.112<br>Chapter 5.2.1 --- Cetyltrimethylammonium bromide (CTAB) extraction method --- p.112<br>Chapter 5.2.2 --- Organic-based extraction method --- p.113<br>Chapter 5.2.3 --- Potassium acetate/sodium dodecyl sulphate precipitation method --- p.113<br>Chapter 5.2.4 --- Hexane-based extraction method --- p.114<br>Chapter 5.3 --- Weight and names of samples --- p.115<br>Chapter 5.4 --- DNA extraction methods --- p.115<br>Chapter 5.4.1 --- CTAB extraction method --- p.115<br>Chapter 5.4.2 --- Qiagen DNeasy´ёØ plant mini kit --- p.116<br>Chapter 5.4.3 --- Promega Wizard® genomic DNA purification --- p.116<br>Chapter 5.4.4 --- Promega Wizard® Magnetic DNA purification system --- p.117<br>Chapter 5.4.5 --- Promega Wizard® DNA Clean-Up system --- p.118<br>Chapter 5.4.6 --- Qiagen QIAshreddrer´ёØ and QIAamp spin column --- p.119<br>Chapter 5.4.7 --- Chelex-based extraction method --- p.119<br>Chapter 5.4.8 --- Organic-based extraction method --- p.120<br>Chapter 5.4.9 --- Nucleon PhytoPure extraction and purification method --- p.120<br>Chapter 5.4.10 --- Potassium acetate/SDS precipitation method --- p.121<br>Chapter 5.4.11 --- Hexane-based extraction method --- p.122<br>Chapter 5.5 --- Results --- p.123<br>Chapter 5.5.1 --- Comparison of eleven extraction methods --- p.123<br>Chapter 5.5.2 --- Comparison of DNA extraction on selected methods --- p.125<br>Chapter 5.6 --- Discussion --- p.132<br>Chapter Chapter 6 --- Quantitative Analysis --- p.136<br>Chapter 6.1 --- Introduction --- p.137<br>Chapter 6.1.1 --- Chemistry of quantitative PCR --- p.138<br>Chapter 6.1.2 --- PCR system --- p.140<br>Chapter 6.2 --- Materials & Methods --- p.142<br>Chapter 6.2.1 --- Design of primers and probes --- p.142<br>Chapter 6.2.2 --- Methods --- p.145<br>Chapter 6.3 --- Results --- p.146<br>Chapter 6.3.1 --- Selection of primer/probe --- p.146<br>Chapter 6.3.2 --- Primer optimization --- p.149<br>Chapter 6.3.3 --- Quantitative analysis of real samples --- p.158<br>Chapter 6.4 --- Discussion --- p.152<br>Chapter Chapter 7 --- Conclusion --- p.168<br>References --- p.175<br>Appendix --- p.193

博士<br>國立中興大學<br>植物病理學系所<br>98<br>The plant pathogenic Xanthomonas can infect several hosts including rice, fruit crop, horticulture crop and vegetable crop. The warm temperature and high humidity climate favor the diseases caused by Xanthomonas. Solanaceous and cruciferous plants are economically important vegetable crops in Taiwan. These xanthomonads associated with solanaceous and cruciferous plants often trigger serious losses. The contaminated seeds are the significant primary inoculum sources in the fields. Development of rapid and accurate detection techniques for these pathogens is critical for managing the diseases by establishing the system of disease-free seeds or transplants. The bacterial spot of tomato and sweet pepper caused by X. euvesicatoria (formerly name X. axonopodis pv. vesicatoria) and X. vesicatoria is an important disease in Taiwan. Strains of bacterial spot Xanthomonas (BSX) on tomato and pepper were reclassified into 4 species including X. euvesicatoria, X. garderni, X. perforans and X. vesicatoria by Jones et al. To understand the components of BSX in Taiwan, BSX strains collected from Taiwan were characterized by biochemical tests and PCR. The results revealed that the BSX strains tested can be characterized into X. euvesicatoria, X. perforans and X. vesicatoria, respectively. No X. garderni strain was found in this study. All strains of X. perforans characterized in this study produced the bacteriocin to inhibit the growth of X. euvesicatoria. In this study, the detection methods for pathogenic Xanthomonas species from solanaceous and cruciferous were developed using genomic suppress subtraction hybridization (SSH), respectively. The specific DNA fragments from X. campestris pv. campestris (Xcc) and X. c. pv. raphani (Xcr) were obtained by SSH and two specific primer pairs (Xcc 2f/2r and Xcr 14f/14r) were designed according to these specific DNA sequences. The primers of Xcc or Xcr can be used for detection and identification of Xcc or Xcr by multiplex PCR and the sensitivity of the two primer set were 10 pg and 100 pg , respectively. When the black rot and bacterial spot naturally infected-leaf tissues or seeds of crucifers were examined, Xcc and Xcr could be detected and identified specifically and simultaneously by the multiplex PCR. The detection technique developed in this study could be used to differentiate the diseases caused by Xcc and Xcr, and it could also be used to detect Xcc and Xcr from naturally infected crucifer seeds. The same strategy was used to obtain the specific DNA fragments from X. vesicatoria (Xv), and a conventional PCR based detection method was developed by the DNA sequences. The DNA sequence of Xv 92-6 was used to design primer pairs (Xv 1f/1r) for detection and identification of X. vesicatoria. A DNA fragment of 174 bp was specifically amplified from strains of X. vesicatoria by PCR with primer pair Xv 1f/1r. The detection sensitivity of primer pair Xv 1f/1r was 50 pg DNA. To increase the detection efficiency for detection of X. vesicatoria, primer pair Xvrt 2f/2r and TaqMan MGB probe pxvrt 1 was designed according tothe sequences of Xv 92-6. With this primer/probe set, strains of X. vesicatoria can be detected at the Ct values about 20. No specific signal was detected from the other tested bacteria when the Ct value at 40. The sensitivity of this method was 500 fg DNA. The primer pair Xvrt 2f/2r and TaqMan MGBprobe pXvrt1 developed in this study can be effectively used for identification and detection of X. vesicatoria. This is the first report of discovery X. perforans in Taiwan. And this is also the first report that the suppress subtraction hybridization was used for development of the PCR based detection techniques for Xanthomonas associated with solanaceous and cruciferous plants. These detection techniques can be effectively used in plant quarantine and seed industry.

博士<br>國立清華大學<br>化學系<br>101<br>Development of rapid, sensitive, portable, and low cost biosensors is important and in urgent need in clinic, environmental, and bio-industrial fields for real-time monitoring and diagnosis. In this dissertation, three electrochemical biosensing platforms are designed and demonstrated for the detection of pathogenic Escherichia coli O157, dietary supplement of vitamin H, and biotech product–genetically modified crops. Enterohemorrhagic Escherichia coli O157, a verocytotoxin-producing pathogen, can be deadly because it can induce acute or chronic renal failure. To speed up the clinical diagnosis of related syndromes caused by E. coli O157, we have developed a novel electrochemical genosensor, featuring nanogold-electrodeposited screen-printed electrodes modified with a self-assembled monolayer of thiol-capped ssDNA capture probe, for the detection of the rfbE gene, which is specific to E. coli O157. Based on the mechanism of competition assay and signal amplification through hexaammineruthenium(III) chloride-encapsulated liposomes, the current signal of the released liposomal Ru(NH3)63+ was measured using square wave voltammetry, yielding a sigmoidally shaped dose−response curve whose linear portion was over the range from 1 to 106 fmol. We could detect as little as 0.75 amol of the target rfbE DNA (equivalent to the amount present in 5 μL of a 0.15 pM solution), which is much more sensitive than those reported previously. In the second part of this dissertation, a new immobilization strategy for site orientation of the antibody using thiophene-3-boronic acid (T3BA) has been demonstrated for improving the sensing performance. The immunosensor comprised a nanogold-electrodeposited screen-printed electrode modified with a self-assembled monolayer of T3BA. Anti-biotin (vitamin H) antibody (a glycoprotein) was covalently bound to T3BA through boronic acid–saccharide interactions. The assay functioned based on competition between the analyte biotin and biotin-tagged potassium hexacyanoferrate(II)–encapsulated liposomes. The current signal produced by the released liposomal Fe(CN)64– was measured using square wave voltammetry. In this proof-of-concept study, as confirmed through electrochemical and surface plasmon resonance (SPR) analyses, this T3BA approach (i) enables site orientation of the antibody molecules, (ii) maintains the activity of the captured antibodies, and (iii) significantly improves the assay performance. Finally, in the third part, we constructed a biomolecular logic gate system to digitally identify genetically modified organisms (GMOs). Because of the advances in biotechnology, GMOs have been well developed and commercialized in many countries for the past decade. In order to provide consumers efficient information of genetically modified (GM) food, various countries (e.g. EU) have implemented labeling regulations and safety evaluation systems for management of GM foods. To fulfill the GMO concerning legislation, reliable and sensitive methods to detect GMOs in foods are necessary. We have demonstrated a biomolecular logic gate (circuit) system with computing functionality mimicking the generation process of an event-specific gene and its feasibility in the analysis of GMOs to provide an alternative to the complex procedures commonly involved in the screening of GMOs. Overall, the combination of various assay formats (competitive assay or sandwich assay), detection platforms (electrochemical or colormetric), nanofabrication technique (carbon electrode constructed with nanogold), powerful signal amplifiers (liposome and enzyme) and biologic gate processing (AND gate), we have successfully developed three simple, accurate, and humanized biosensors that exhibit the feasibility of being extended to the point-of-care diagnostics, environmental monitoring, and food safety surveillance.

碩士<br>國立陽明大學<br>生醫光電工程研究所<br>93<br>Acute myocardial infarction (AMI) is one of the most common disease in industrialized countries and now is becoming an serious problem in developing countries. AMI occurs when a coronary blood flow decreases abruptly after a thrombotic occlusion of a coronary artery previously narrowed by atherosclerosis. In this research, a novel fiber-optic biosensor which can quickly diagnose AMI at early stage is developed and tested. In this study, a fiber-optic biosensor which an antibody immobilized on the unclad region of a plastic fiber captures its antigen, and then FITC labeled antibody form <Ab/Ag/FITC-Ab> complex of the sandwich structure. The setup with a laser beam of its wavelength at 488nm which propagates in a multi-mode optic excites the evanescent wave penetrates the surface of the unclad fiber. The evanescent wave enables to extends several hundred nanometers in depth into the dielectric medium near unclad surface and excites the FITC molecules. The emitted fluorescence (520nm) is collected by use of a photomultiplier tube set aside to the reaction chamber, in order to emphasis the fluorescence collection efficiency. In the experiment, a IgG/anti-IgG interaction to measured that the kinetic coefficients, , and are obtained successfully. Then we applied this method to analyze pCRP and mCRP interaction with anti-CRP respectively. The results show that the equilibration dissociated constant of <mCRP/anti-CRP> interaction is few times larger than that of <pCRP/anti-CRP> which represents a specificity of mCRP to anti-CRP experiments.Therefore, this fiber-optic biosensor can compare the method to other techniques, our developed system has rapid, simple, and real-time detection capabilities at the same time. Furthermore, it can be used for clinical application in the specified protein detection on early detection of different diseases. In the mean time, this biosensor can provide the method for protein interaction measurement in real time on new drug development as well.