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Journal articles on the topic 'Plant diagnosis'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

Guarro, S. "Nuclear power plant diagnosis." Annals of Nuclear Energy 14, no. 6 (January 1987): 325. http://dx.doi.org/10.1016/0306-4549(87)90135-6.

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2

Perez, Ana Picallo, Perez Iribarren E, Apaolaza A, and Sala J. M. "Thermoeconomic Approach to the Diagnosis of A DHW Microcogeneration Plant." Modern Environmental Science and Engineering 2, no. 08 (August 2016): 507–13. http://dx.doi.org/10.15341/mese(2333-2581)/08.02.2016/001.

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3

O'SHIMA, Eiji. "Plant management and failure diagnosis." Journal of the Japan Society for Precision Engineering 57, no. 3 (1991): 413–17. http://dx.doi.org/10.2493/jjspe.57.413.

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4

TAMURA, Ken. "Mecanical Diagnosis in Chemical Plant." Journal of the Society of Materials Science, Japan 66, no. 2 (2017): 193. http://dx.doi.org/10.2472/jsms.66.193.

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5

Miller, S. A., and R. R. Martin. "Molecular Diagnosis of Plant Disease*." Annual Review of Phytopathology 26, no. 1 (September 1988): 409–32. http://dx.doi.org/10.1146/annurev.py.26.090188.002205.

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6

Vevelstad, Merete, Unni Johansen, An-Magritt Haneborg, Marianne Madland Hagesæther, and åse Marit Leere øiestad. "Plant toxin poisoning–a disguised diagnosis?" Toxicologie Analytique et Clinique 34, no. 3 (September 2022): S63. http://dx.doi.org/10.1016/j.toxac.2022.06.082.

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7

Groot, Jan E. "Foliage Plant Diseases: Diagnosis and Control." HortTechnology 8, no. 4 (October 1998): 621c. http://dx.doi.org/10.21273/horttech.8.4.621b.

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8

Poll, Hans Günther, José Carlos Zanutto, and Walter Ponge-Ferreira. "Hydraulic Power Plant Machine Dynamic Diagnosis." Shock and Vibration 13, no. 4-5 (2006): 409–27. http://dx.doi.org/10.1155/2006/203834.

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A method how to perform an entire structural and hydraulic diagnosis of prototype Francis power machines is presented and discussed in this report. Machine diagnosis of Francis units consists on a proper evaluation of acquired mechanical, thermal and hydraulic data obtained in different operating conditions of several rotary and non rotary machine components. Many different physical quantities of a Francis machine such as pressure, strains, vibration related data, water flow, air flow, position of regulating devices and displacements are measured in a synchronized way so that a relation of cause an effect can be developed for each operating condition and help one to understand all phenomena that are involved with such kind of machine. This amount of data needs to be adequately post processed in order to allow correct interpretation of the machine dynamics and finally these data must be compared with the expected calculated data not only to fine tuning the calculation methods but also to accomplish fully understanding of the influence of the water passages on such machines. The way how the power plant owner has to operate its Francis machines, many times also determined by a central dispatcher, has a high influence on the fatigue life time of the machine components. The diagnostic method presented in this report helps one to understand the importance of adequate operation to allow a low maintenance cost for the entire power plant. The method how to acquire these quantities is discussed in details together with the importance of correct sensor balancing, calibration and adequate correlation with the physical quantities. Typical results of the dynamic machine behavior, with adequate interpretation, obtained in recent measurement campaigns of some important hydraulic turbines were presented. The paper highlights the investigation focus of the hydraulic machine behavior and how to tailor the measurement strategy to accomplish all goals. Finally some typical recommendations based on the experience obtained on previous diagnostic reports of Francis turbines are performed in order to allow a better and safe operation of these power plant units.
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9

CHEN, Peng, and Toshio TOYOTA. "Sequential Fuzzy Diagnosis for Plant Machinery." JSME International Journal Series C 46, no. 3 (2003): 1121–29. http://dx.doi.org/10.1299/jsmec.46.1121.

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10

Xue, Wangyu, Xiu Li, and Biqing Huang. "Health diagnosis of nuclear power plant." International Journal of Advanced Robotic Systems 16, no. 5 (September 1, 2019): 172988141988065. http://dx.doi.org/10.1177/1729881419880654.

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At present, nuclear power plant is developing rapidly, and its application has been involved in many aspects including life, military, industry and many other important fields, bringing benefits to people’s life. However, the nuclear power plant has a relatively special structure. Once a safety accident occurs, the consequences will be unimaginable, and the cost of its operation and maintenance will be relatively high. Therefore, how to effectively diagnose the health status of the nuclear power plant is an urgent problem to be solved. On the above-mentioned research background, we need to study nuclear power plant health diagnosis method. Considering the characteristic of the nuclear power plant system and special failure mode, both the safety and economy, a health condition diagnosis method based on analytic hierarchy process and fuzzy comprehensive evaluation method is proposed for the structural characteristics and functional characteristics of nuclear power plants. According to the special failure mode and complex system structure of nuclear power plant, the evaluation index system based on failure mode is constructed by laying the system using the hierarchical analysis method, and the system is scored by fuzzy comprehensive evaluation method. The health status makes a coarse-grained diagnosis and provides a reference for the development of the operation and maintenance strategy.
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11

SEKIAI, Takaaki, Toru KAWANO, and Masahiro MURAKAMI. "Plant Diagnosis System for Power Plants." Journal of the Society of Mechanical Engineers 118, no. 1163 (2015): 624–27. http://dx.doi.org/10.1299/jsmemag.118.1163_624.

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12

Flood, Julie. "Plant Pathogen Detection and Disease Diagnosis." Phytochemistry 62, no. 5 (March 2003): 813. http://dx.doi.org/10.1016/s0031-9422(02)00609-x.

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13

Szczepaniak, P. S. "Neural Diagnosis of Complex Power Plant." IFAC Proceedings Volumes 28, no. 10 (July 1995): 871–76. http://dx.doi.org/10.1016/s1474-6670(17)51630-4.

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14

Fox, Roland. "Plant Pathogen Detection and Disease Diagnosis." Plant Pathology 47, no. 5 (October 1998): 681–82. http://dx.doi.org/10.1046/j.1365-3059.1998.00296.x.

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15

Wang, Huaqing, Peng Chen, and Shuming Wang. "Intelligent diagnosis methods for plant machinery." Frontiers of Mechanical Engineering in China 5, no. 1 (November 25, 2009): 118–24. http://dx.doi.org/10.1007/s11465-009-0084-z.

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16

Fenu, Gianni, and Francesca Maridina Malloci. "DiaMOS Plant: A Dataset for Diagnosis and Monitoring Plant Disease." Agronomy 11, no. 11 (October 21, 2021): 2107. http://dx.doi.org/10.3390/agronomy11112107.

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The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Datasets are the fuel for the development of these technologies. In this paper, we release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant, consisting of 3505 images of pear fruit and leaves affected by four diseases. In addition, we perform a comparative analysis of existing literature datasets designed for the classification and recognition of leaf diseases, highlighting the main features that maximize the value and information content of the collected data. This study provides guidelines that will be useful to the research community in the context of the selection and construction of datasets.
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17

Stowell, L. J., and W. D. Gelernter. "DIAGNOSIS OFTURFGRASSDISEASES." Annual Review of Phytopathology 39, no. 1 (September 2001): 135–55. http://dx.doi.org/10.1146/annurev.phyto.39.1.135.

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18

Maruyama, Nobuyuki. "Plant allergen component useful in clinical diagnosis." Nihon Shoni Arerugi Gakkaishi. The Japanese Journal of Pediatric Allergy and Clinical Immunology 29, no. 1 (2015): 34–40. http://dx.doi.org/10.3388/jspaci.29.34.

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19

Baudoin, A. B. A. M. "A Laboratory Miniproject in Plant Disease Diagnosis." American Biology Teacher 48, no. 7 (October 1, 1986): 413–19. http://dx.doi.org/10.2307/4448355.

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20

Bakr, Mahmoud, Sayed Abdel-Gaber, Mona Nasr, and Maryam Hazman. "DenseNet Based Model for Plant Diseases Diagnosis." European Journal of Electrical Engineering and Computer Science 6, no. 5 (September 21, 2022): 1–9. http://dx.doi.org/10.24018/ejece.2022.6.5.458.

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The biggest threat to the safety of food is plant diseases. They have the ability to dramatically lower the quantity and quality of agricultural products. Recognizing plant diseases is the biggest issue in the agricultural industries. Convolutional Neural Networks (CNN) are effective in solving image classification problems in computer vision. Numerous deep learning architectures have been used to diagnose plant diseases. This study presents a transfer learning-based model for identifying diseases in plant leaves. In this paper, a CNN classifier based on transfer learning model called DenseNet201 are proposed. An analysis of four deep learning models (VGG16, Inception V3, ResNet152V2, and DenseNet201) done to see which one can detect plant diseases with the greatest degree of accuracy. Web based application developed for plant disease diagnosing from defected leaf image and the proposed model which identify the disease and give the recommended treatment. The used images dataset contains 28310 leaves photos of 3 crops, tomato, potato and pepper divided into 15 different classes, 9 disorders and one healthy class for tomato, 2 disorders and one healthy class for potato and 1 disorder and one healthy for pepper. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.44% and validation accuracy of 98.70%.
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21

Guadarrama, Lili, Carlos Paredes, and Omar Mercado. "Plant Disease Diagnosis in the Visible Spectrum." Applied Sciences 12, no. 4 (February 20, 2022): 2199. http://dx.doi.org/10.3390/app12042199.

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A simple and robust methodology for plant disease diagnosis using images in the visible spectrum of plants, even in uncontrolled environments, is presented for possible use in mobile applications. This strategy is divided into two main parts: on the one hand, the segmentation of the plant, and on the other hand, the identification of color associated with diseases. Gaussian mixture models and probabilistic saliency segmentation are used to accurately segment the plant from the background of an image, and HSV thresholds are used in order to achieve the identification and quantification of the colors associated with the diseases. Proper identification of the colors associated with diseases of interest combined with adequate segmentation of the plant and the background produces a robust diagnosis in a wide range of scenarios.
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22

SHANMUGARAJ, G., G. RAMYA, D. SOWMIYA, and S. VINOTHINI. "DIAGNOSIS OF PLANT DISEASE USING DEEP LEARNING." i-manager’s Journal on Pattern Recognition 8, no. 1 (2021): 25. http://dx.doi.org/10.26634/jpr.8.1.18228.

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23

Chavan, Dr M. S. "Diagnosis of Plant Diseases using Neural Network." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (June 30, 2019): 1051–54. http://dx.doi.org/10.22214/ijraset.2019.6180.

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24

Sharif, M. A., and R. I. Grosvenor. "Process plant condition monitoring and fault diagnosis." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 212, no. 1 (February 1, 1998): 13–30. http://dx.doi.org/10.1243/0954408981529268.

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The paper reviews current research work in the field of process condition monitoring and fault diagnosis. The review compares and contrasts the applicability and efficiency of different techniques, and concentrates on methods which monitor the main process variables. From the wide range of methods and process variables, temperature, flowrate and liquid level are used here in comparing the limitations and applications of each method. The scope of the paper ranges from basic, well established techniques to the latest reported monitoring strategies for each of the process variables. Furthermore, the different methods of fault diagnosis deemed to be relevant in process plant are reviewed. The detection of the internal leakage in the control valves and motor faults are discussed in detail, as examples of the monitoring of vital process plant components. The paper then outlines areas of future work, such as the development of a user friendly interface. This interface is based on state transition diagrams (STDs) as well as on the use of a knowledge based system (KBS) to model and diagnose faults in vital process plant components such as control valves.
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25

Tocatlidou, A., H. C. Passam, A. B. Sideridis, and C. P. Yialouris. "Reasoning under uncertainty for plant disease diagnosis." Expert Systems 19, no. 1 (February 2002): 46–52. http://dx.doi.org/10.1111/1468-0394.00188.

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26

MAKI, Ryunosuke, Takuya NISHIMURA, Liuyang SONG, Zhiqiang LIAO, Hou JINYAMA, Mitsushi YAMASHITA, and Koji KITO. "Study on Plant Inspection and Diagnosis Robot." Proceedings of the Symposium on Evaluation and Diagnosis 2016.15 (2016): 207. http://dx.doi.org/10.1299/jsmesed.2016.15.207.

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27

TAKAHASHI, Yoshiyuki. "Studies on Serological Diagnosis of Plant Viruses." Japanese Journal of Phytopathology 57, no. 3 (1991): 300. http://dx.doi.org/10.3186/jjphytopath.57.300.

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28

Kleimenov, A. V., and G. L. Gendel'. "Ecological Performance from Diagnosis of Petroleum Plant." Chemical and Petroleum Engineering 40, no. 5/6 (May 2004): 304–6. http://dx.doi.org/10.1023/b:cape.0000039673.64490.da.

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29

Shoukat Choudhury, M. A. A., Vinay Kariwala, Nina F. Thornhill, Hisato Douke, Sirish L. Shah, Haruo Takada, and J. Fraser Forbes. "Detection and Diagnosis of Plant-Wide Oscillations." Canadian Journal of Chemical Engineering 85, no. 2 (May 19, 2008): 208–19. http://dx.doi.org/10.1002/cjce.5450850209.

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30

Uppal, Ashima, Mahaveer Singh Naruka Hai, and Gaurav Tewari. "Classification Models for Plant Diseases Diagnosis: A Review." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 11 (November 30, 2022): 91–106. http://dx.doi.org/10.17762/ijritcc.v10i11.5785.

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Plants are important source of our life. Crop production in a good figure and good quality is important to us. The diagnosis of a disease in a plant can be manual or automatic. But manual detection of disease in a plant is not always correct as sometimes it can be not be seen by naked eyes so an automatic method of detection of plant diseases should be there. It can make use of various artificial intelligence based or machine learning based methods. It is a tedious task as it needs to be identified in earlier stage so that it will not affect the entire crop. Disease affects all species of plant, both cultivated and wild. Plant disease occurrence and infection severity vary seasonally, regarding the environmental circumstances, the kinds of crops cultivated, and the existence of the pathogen. This review attempts to provide an exhaustive review of various plant diseases and its types, various methods to diagnose plant diseases and various classification models used so as to help researchers to identify the areas of scope where plant pathology can be improved.
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31

Karjalainen, Reijo, Leo Rouhiainen, and Hans Söderlund. "Diagnosis of plant viruses by nucleic acid hybridization." Agricultural and Food Science 59, no. 3 (July 1, 1987): 179–91. http://dx.doi.org/10.23986/afsci.72262.

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Nucleic acid hybridization is a powerful technique for the diagnosis of many plant viruses not easily detected by serological techniques. It is particularly effective in the detection of viruses occurring in low amount in plant tissue, viruses that are poor immunogens or contain satellites. Molecular probes with desired specificities can be prepared by recombinant DNA techniques for large scale use. cDNA probes of potato virus X(PVX) RNA were made by molecular cloning, and the clones were 32P labelled by nick translation. Hybridization of cDNA to PVX RNA revealed 1 ng of purified virus in 2 µl spots dried onto nitrocellulose filter. Infected samples of crude leaf extracts were easily detected by hybridization, while probes did not react with healthy leaf samples. Nucleic acid hybridization research aims at replacing radiometric probes with nonradioactive methods involving enzymes which are directly or indirectly coupled to the probe and whose presence is observed with the aid of a colour changing substrate. Hybridization assay formats that can easily be automatized are under development. Sandwich hybridization is a simple test format developed for analyzing unpurified biological material, and it appears to be a powerful tool for microbial diagnostics. Sensitivity can be improved by using detection systems in which the specific activity of the probe is increased. Procedures such as ’polymerase chain reaction’, in which the amount of detectable nucleic acid sequences can be increased, are promising alternatives for increasing sensitivity. It is concluded that even if probe-based assays are in their infancy, they will no doubt develop towards such easy use as have immunological test kits.
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32

Iyatomi, Hitoshi. "Image Generation Techniques for Practical Plant Disease Diagnosis." Journal of the Society of Powder Technology, Japan 59, no. 8 (August 10, 2022): 394–99. http://dx.doi.org/10.4164/sptj.59.394.

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33

Abu-Naser, S. S., K. A. Kashkash, and M. Fayyad. "Developing an Expert System for Plant Disease Diagnosis." Journal of Artificial Intelligence 1, no. 2 (June 15, 2008): 78–85. http://dx.doi.org/10.3923/jai.2008.78.85.

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34

Abu-Naser, S. S., K. A. Kashkash, and M. Fayyad. "Developing an Expert System for Plant Disease Diagnosis*." Journal of Artificial Intelligence 3, no. 4 (September 15, 2010): 269–76. http://dx.doi.org/10.3923/jai.2010.269.276.

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35

Takagi, Yoshio. "Maintenance and Diagnosis Techniques for Fossil Power Plant." Journal of the Japan Institute of Metals 66, no. 12 (2002): 1185–91. http://dx.doi.org/10.2320/jinstmet1952.66.12_1185.

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36

Agus, Fahrul, Muh Ihsan, Dyna Marisa Khairina, and Krishna Purnawan Candra. "ESforRPD2: Expert System for Rice Plant Disease Diagnosis." F1000Research 7 (December 6, 2018): 1902. http://dx.doi.org/10.12688/f1000research.16657.1.

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One of the factors causing rice production disturbance in Indonesia is the lack of knowledge of farmers on early symptoms of rice plant diseases. These diseases are increasingly rampant because of the lack of experts. This study aimed to overcome this problem by providing an Expert System that helps farmers to make early diagnosis of rice plant diseases. Data of rice plant pests and diseases in 2016 were taken from Samarinda, East Kalimantan, Indonesia using an in-depth survey, and rice experts from the Department of Food Crops and Horticulture of East Kalimantan Province were recruited for the project. The Expert System for Rice Plant Disease Diagnosis, ESforRPD2, was developed based on the pest and disease experiences of the rice experts, and uses a Waterfall Paradigm and Unified Modelling Language. This Expert System can detect 48 symptoms and 8 types of diseases of rice plants from 16 data tests with an accuracy of 87.5%. ESforRPD2 is available in Indonesian at: http://esforrpd2.blog.unmul.ac.id
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37

Agus, Fahrul, Muh Ihsan, Dyna Marisa Khairina, and Krishna Purnawan Candra. "ESforRPD2: Expert System for Rice Plant Disease Diagnosis." F1000Research 7 (February 21, 2019): 1902. http://dx.doi.org/10.12688/f1000research.16657.2.

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One of the factors causing rice production disturbance in Indonesia is that farmers lack knowledge of early symptoms of rice plant diseases. These diseases are increasingly rampant because of the lack of experts. This study aimed to overcome this problem by providing an Expert System that helps farmers to make an early diagnosis of rice plant diseases. Data of rice plant pests and diseases in 2016 were taken from Samarinda, East Kalimantan, Indonesia using an in-depth survey, and rice experts from the Department of Food Crops and Horticulture of East Kalimantan Province were recruited for the project. The Expert System for Rice Plant Disease Diagnosis, ESforRPD2, was developed based on the pest and disease experiences of the rice experts and uses a Waterfall Paradigm and Unified Modeling Language. This Expert System can detect 48 symptoms and 8 types of diseases of rice plants from 16 data tests with a sensitivity of 87.5%. ESforRPD2 is available in Indonesian at http://esforrpd2.blog.unmul.ac.id
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38

Sindelarova, M. "Chase, A.R.: Foliage Plant Diseases: Diagnosis and Control." Biologia plantarum 44, no. 2 (June 1, 2001): 262. http://dx.doi.org/10.1023/a:1010205005039.

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39

Nezhad, Amir Sanati. "Future of portable devices for plant pathogen diagnosis." Lab Chip 14, no. 16 (2014): 2887–904. http://dx.doi.org/10.1039/c4lc00487f.

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40

Mohammadi-Idghamishi, A., and S. Hashtrudi-Zad. "Hierarchical Fault Diagnosis: Application to an Ozone Plant." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 37, no. 5 (September 2007): 1040–47. http://dx.doi.org/10.1109/tsmcc.2007.900622.

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41

PRAETORIUS, N., and K. D. DUNCAN. "Flow representation of plant processes for fault diagnosis†." Behaviour & Information Technology 10, no. 1 (January 1991): 41–52. http://dx.doi.org/10.1080/01449299108924270.

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42

Walseth, J. Å., B. A. Foss, M. Lind, and O. Øgaard. "Models for Diagnosis - Application to a Fertilizer Plant." IFAC Proceedings Volumes 25, no. 4 (April 1992): 61–66. http://dx.doi.org/10.1016/s1474-6670(17)50217-7.

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43

d'Accadia, Massimo Dentice, and Filippo de Rossi. "Thermoeconomic analysis and diagnosis of a refrigeration plant." Energy Conversion and Management 39, no. 12 (August 1998): 1223–32. http://dx.doi.org/10.1016/s0196-8904(98)00016-8.

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44

Birchler, Bernhard, and Peter Krug. "A plant diagnosis system takes its first steps." World Pumps 1996, no. 356 (May 1996): 54–56. http://dx.doi.org/10.1016/s0262-1762(99)80766-4.

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45

Detroja, K. P., R. D. Gudi, and S. C. Patwardhan. "Plant-wide detection and diagnosis using correspondence analysis." Control Engineering Practice 15, no. 12 (December 2007): 1468–83. http://dx.doi.org/10.1016/j.conengprac.2007.02.007.

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46

Putnam, M. L. "Evaluation of selected methods of plant disease diagnosis." Crop Protection 14, no. 6 (September 1995): 517–25. http://dx.doi.org/10.1016/0261-2194(95)00038-n.

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47

Gertler, J., Q. Luo, K. Anderson, and Xiaowen Fang. "Diagnosis of Plant Failures Using Orthogonal Parity Equations." IFAC Proceedings Volumes 23, no. 8 (August 1990): 361–66. http://dx.doi.org/10.1016/s1474-6670(17)51850-9.

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48

Simani, S., and C. Fantuzzi. "Fault diagnosis in power plant using neural networks." Information Sciences 127, no. 3-4 (August 2000): 125–36. http://dx.doi.org/10.1016/s0020-0255(00)00034-7.

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49

Henson, Joan M., and Roy French. "The Polymerase Chain Reaction and Plant Disease Diagnosis." Annual Review of Phytopathology 31, no. 1 (September 1993): 81–109. http://dx.doi.org/10.1146/annurev.py.31.090193.000501.

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50

Jan, Arif T., Mudsser Azam, Mohiuddin K. Warsi, Arif Ali, and Qazi Mohd Rizwanul Haq. "Technical advancement in plant virus diagnosis – an appraisal." Archives Of Phytopathology And Plant Protection 45, no. 8 (May 2012): 909–21. http://dx.doi.org/10.1080/03235408.2011.599156.

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