Nutrient Difficient Disease Detection Technologies for Crops in Korea: A review

Seung-Min Baek1Yong-Joo Kim1,2Jeong-Hoon Jang3*


This paper reviews and summarizes some of the non-invasive techniques used to detect crop diseases. His two main categories of non-invasive monitoring of crop diseases are (1) spectroscopy and imaging techniques, and (2) volatile organic compound profiling-based techniques for recognizing crop diseases. Spectroscopy and imaging techniques include fluorescence spectroscopy, visible-IR spectroscopy, fluorescence imaging, and hyperspectral imaging. Disease detection based on the VOC profile involves the use of electronic nasal or GC-MS-based volatile metabolite analysis emitted from healthy and diseased crops as tools to identify the disease. Some of the challenges in these technologies are (1) the impact of background data on the resulting profile or data, (2) technology optimization for specific crop diseases, and (3) continuous automated monitoring. Technology automation. Crop diseases under real-world field conditions. This review suggests that these disease detection methods show excellent potential for the ability to accurately detect crop diseases. Spectroscopic and imaging techniques can be integrated with autonomous agricultural vehicles for reliable, real-time crop disease detection to achieve superior crop disease control and control.



Crop diseases constitute a major threat in agricultural production systems that deteriorate yield quality and quantity at production, storage, and transportation level. At farm level, reports on yield losses, due to crop diseases, are very common. Furthermore, crop diseases pose significant risks to food security at a global scale. Timely identification of crop diseases is a key aspect for efficient management. Crop diseases may be provoked by various kinds of bacteria, fungi, pests, viruses, and other agents. Disease symptoms, namely the physical evidence of the presence of pathogens and the changes in the crops’ phenotype, may consist of leaf and fruit spots, wilting and color change, curling of leaves, etc. Historically, disease detection was conducted by expert agronomists, by performing field scouting. However, this process is time-consuming and solely based on visual inspection. Recent technological advances have made commercially available sensing systems able to identify diseased crops before the symptoms become visible.

Reliable diagnosis of diseases and pests in the early stages of crop production is highly desirable to reduce significant production and economic losses. The main purpose of crop pest diagnosis is to assess crop health and determine the cause of the disease. However, one of the major challenges is the difficulty in determining the physical, chemical, and biological changes in crops during the asymptomatic stage of infection. Another challenge is that it is difficult to work in a timely and economical manner. Crop diseases result in significant production and economic losses to agriculture around the world. Monitoring the health of crops and trees and detecting diseases is important for sustainable agriculture. As far as we know, there are no commercially available sensors that evaluate the condition of trees in real time. Scouting is the most widely used mechanism for monitoring tree stress today. This is a costly, labor-intensive and time-consuming process. Molecular techniques such as the polymerase chain reaction are used to identify crop diseases that require detailed sampling and processing procedures. Initial information for crop health and disease detection should facilitate disease management using appropriate management strategies such as pesticide application, fungicide application, and vector control through disease-specific chemical application.

There is a need for fast and reliable diagnostic methods that can be used in the field to detect crop diseases at the asymptomatic stage. Indirect methods that rely on imaging techniques and the profile of VOCs emitted from abundant crops can meet these needs. For example, for early detection of stress, spectroscopic imaging techniques used both in the field and in cultivated greenhouse crops show satisfactory classification accuracy. However, some changes and improvements are still needed, including temporary consequences. Biosensors that use phage display and biophotonics have been reported to detect infections immediately, but must be corrected, improved, and properly validated before being used in the field. Therefore, the purpose of this study is to organize, examine and compare the characteristics of the three methods for detecting crop diseases.

Crop Disease Technologies

In order to solve the problems of these crop diseases, various detection techniques have been developed. Figure 1 shows a representative crop disease detection technique. Disease detection techniques are largely divided into three types: Molecular techniques, Imaging and spectroscopic techniques, and VOCs profiling-based techniques. Molecular techniques are direct detection methods, including polymerase chain reaction (PCR), fluorescence in-situ hybridization (FISH) and serological technologies such as enzyme-linked immunosorbent assay (ELISA). Spectroscopic and imaging techniques are unique disease monitoring methods used to detect disease and stress caused by a variety of crop and tree factors. Current research activities are directed towards the development of such techniques for creating practical tools for large-scale real-time disease monitoring under field conditions. Various spectroscopic and imaging techniques have been studied to detect symptomatic and asymptomatic crop diseases. Several methods include fluorescence imaging (Bravo et al., 2004; Chaerle et al., 2007), multispectral imaging (Qin et al., 2009), Infrared spectroscopy (Spinelli et al., 2006; Purcell et al., 2009) Visible and Multiband Spectroscopy (Yang et al., 2007; Delalieux et al., 2007; Chen et al., 2008). VOCs profiling-based techniques are largely divided into electronic nose system and GC-MS. Typical indirect methods detect morphological changes, transpiration rate changes, and volatile organic compound (VOC) profiles. These correspond to fluorescence imaging, hyperspectral technology, and gas chromatography-mass spectrometry (GC-MS) technology.

Molecular techniques of crop disease detection

In recent years, molecular technology for detecting crop diseases has been well established. Using molecular methods to diagnose crop diseases offers many advantages to the diagnostician over traditional methods. For example, they can enable the identification of morphologically similar species. Molecular tools not only improve the effectiveness, accuracy and speed of diagnosis, but their common technical foundation is especially when resources are limited and traditional skills are difficult to maintain. It offers additional benefits. The sensitivity of molecular technology is the minimum amount of microorganisms that can be detected in a sample.

Lopez et al. (2003) reported that the sensitivity of molecular techniques for detecting bacteria ranges from 10 to 106 colony forming units /mL. Molecular techniques commonly used to detect disease are ELISA and PCR (PCR and real-time PCR). Other molecular technologies include immunofluorescence (IF), flow cytometry, fluorescence in situ hybridization (FISH), and DNA microarrays. Disease detection by ELISA involves injecting a protein (antigen) from a microorganism associated with a crop disease into an animal that produces antibodies to that antigen. These antibodies are extracted from the animal's body and used for antigen detection with fluorescent dyes and enzymes. In the presence of disease-causing microorganisms (antigens), the sample fluoresces, confirming the presence of disease in a particular crop. The presence of specific bands in gel electrophoresis confirms the presence of organisms responsible for crop diseases. There are many studies on disease detection using molecular technology. Efforts are underway to improve the efficiency of these technologies.

Table 1. Some studies on crop disease detection using molecular techniques.

Spectroscopic and imaging techniques for disease detection

Recent developments in agricultural technology have lead to a demand for a new era of automated non-destructive methods of crop disease detection. It is desirable that the crop disease detection tool should be rapid, specific to a particular disease, and sensitive for detection at the early onset of the symptoms (Lopez et al., 2003). The spectroscopic and imaging techniques are unique disease monitoring methods that have been used to detect diseases and stress due to various factors, in crops and trees. Current research activities are towards the development of such technologies to create a practical tool for a large-scale real-time disease monitoring under field conditions. Various spectroscopic and imaging techniques have been studied for the detection of symptomatic and asymptomatic crop diseases. Some the methods are: fluorescence imaging (Bravo et al., 2004; Moshou et al., 2005; Chaerle et al., 2007), multispectral or hyperspectral imaging (Moshou et al., 2004; Shafri and Hamdan, 2009; Qin et al., 2009), infrared spectroscopy (Spinelli et al., 2006; Purcell et al., 2009), fluorescence spectroscopy (Marcassa et al., 2006; Belasque et al., 2008; Lins et al., 2009), visible/multiband spectroscopy (Yang et al., 2007; Delalieux et al., 2007; Chen et al., 2008), and nuclear magnetic resonance (NMR) spectroscopy (Choi et al., 2004). Hahn (2009) reviewed multiple methods (sensors and algorithms) for pathogen detection, with special emphasis on postharvest diseases.

Table 2. Some studies on crop disease detection using spectroscopic techniques.

As fluorescence spectroscopy, visible and infrared spectroscopy are used as a fast, non-destructive, and cost-effective method for detecting crop diseases. This is a rapidly evolving technology used in a variety of applications (Ramon et al., 2002; Delwiche and Graybosch, 2002). Studies have also been conducted on the detection of stress, damage and disease in crops using this technique (Spinelli et al., 2006; Naidu et al., 2009). The visible and infrared regions of the electromagnetic spectrum are known to provide the greatest information on the physiological stress levels of crops (Muhammed, 2005; Xu et al., 2007) and these wavelengths specific to disease. You can use some of the bands. To discover crop diseases (West et al., 2003), before symptoms are visible. Visible spectroscopy is commonly used in combination with infrared spectroscopy to detect crop diseases (Bravo et al., 2003; Huang et al., 2004; Larsolle and Muhammed, 2007).

Table 3. Some studies on crop disease detection using imaging techniques.

Profiling of crop volatile organic compounds for disease detection

The volatile organic compounds (VOCs) released from crops and trees account for about two-thirds of the total VOC emissions present in the atmosphere (Guenther, 1997). There are many factors that affect the volatile metabolic profile of a crop or tree. VOCs emitted by crops depend on a variety of physicochemical factors such as humidity, temperature, light, soil conditions, fertilization, and biological factors such as crop growth and development stages, the presence of insects and other herbs. (Vallat et al., 2005; Vuorinen et al., 2007). Physicochemical factors directly or indirectly affect the physiological state of the crop, thereby affecting the VOC profile of the crop. Volatile substances in these crops affect the relationship between crops and other organisms, including pathogens (Vuorinen et al., 2007). For example, acetaldehyde released from the leaves of young poplar trees is controlled by the transfer of ethanol to the leaves by transpiration (Kreuzwieser et et. Et al. 2001).

The electronic nose system consists of a series of gas sensors that react with a variety of organic compounds. Because each sensor has a specific sensitivity, the sensitivity of a set of sensors can be used to identify different compounds present in the atmosphere. Electronic nose systems are used in a variety of applications. They have been used to determine food quality (Evans et al., 2000; Di Natale et al., 2001; Zhang et al., 2008a, b)., 2001; Dragonieri et al., 2007), Detection of microorganisms in food (Falasconi et al., 2005; Rajamäki et al., 2006; Balasubramanian et al., 2008; Concina et al., 2009). Applications of electronic nasal systems for identifying crop diseases are a relatively new area of application.

Table 4. Some studies on crop disease detection using electronic nose system.

GC-MS is a commonly used technique for qualitative and quantitative analysis of volatile metabolites released from crops / trees under a variety of environmental and physiological conditions. GC-MS studies were performed to assess changes in volatile substances caused by bacterial or fungal infections in various foods (Table 5). Prithiviraj etc. (2004) Bacterial species (Erwinia carotovora causes Erwinia carotovora) and fungal species (Fusarium oxysporum and roots) using HAPSITE, a commercially available portable GC-MS device. We evaluated the variability of volatiles released from the bulbs of onions infected with Pectobacterium carotoides (Botrytis allii). This study showed that 25 volatile compounds released from onions (out of 59 consistently detected compounds) could be used to identify the disease based on VOC profiling. No statistical analysis was performed to determine the discriminating ability of the algorithm in classifying VOC profiles for disease detection, but model development and software development were recommended for this purpose.

A similar study of potato tubers inoculated with Erwinia carotovora subsp. carotovora, E. carotovora subsp. Atroseptica, Pythium ultimum, Phytophthora infestans, or Fusarium sambucinum using solid-phase microextraction (SPME) fibers and GC flame ionization detectors (FIDs) have shown potential for VOC profiling for disease detection (Kushalappa). et al., 2002). The amount of volatiles increased with increasing severity of the disease. The BPNN model was applied to classify volatile metabolite profiles for disease. The gas retention time (GC function) of the volatile compounds was used as input data and the two hidden layers were used for mutual validation. Mutual validation probabilities (using BPNN) were> 67% (67-75%) in all groups except potato tubers infected with Phytophthora. Unlike other studies, this study did not identify the specific compound that caused the VOC peak in the FID.

Table 5. Some studies on crop disease detection using GC-MS.


An overall comparison of the three key technologies is summarized in Table 6. Recent literature reports support the idea that volatile profiling and changes in spectral reflectance can be used for non-invasive field monitoring of crop diseases. Crops and trees release volatile organic compounds (VOCs) as a result of metabolic activity that occurs in buds, leaves, flowers and fruits. The VOC profile of each crop varies greatly depending on physiological conditions and species. Various factors affect the VOC profile of a particular crop or tree. This includes changes in the metabolism of crops that are affected by changes in the environment, the age of the crop, the stage of crop development, the stress of the crop, and the presence of the disease. One of the biggest problems when using crop airframe metabolites as an indicator of the presence of crop diseases is the natural change in VOC profile among crop species. Fluctuations in VOCs released by crops can hide changes due to stress and illness. Therefore, unlike VOCs produced by environmental and nutritional stress, it is necessary to identify various volatile biomarkers that are specific to a particular crop or disease. Real-world applications require the development of robust and reliable systems for real-time monitoring of crop diseases. Similar to the VOC profile of crops, environmental conditions affect the spectral reflectance of an object (Griffin and Burke, 2003). Therefore, you need to identify the appropriate approach to solving this problem. A possible way to overcome this problem is to identify the wavelength range or exponent that is not only sensitive to the disease of a particular crop, but is least affected by changes in environmental conditions. Autonomous robots can integrate imaging and VOC profiling techniques. This is because these technologies are well established for other industrial application areas. Once well established for specific disease detection applications, such methods can be integrated with autonomous agricultural vehicles to monitor crop diseases in real time.

This paper reviews and summarizes some of the non-invasive techniques used to detect crop diseases. The two main categories of non-invasive monitoring of crop diseases are (1) spectroscopy and imaging techniques and (2) basic volatile organic compound profiling techniques for recognizing crop diseases. Spectroscopy and imaging techniques include fluorescence spectroscopy, visible infrared spectroscopy, fluorescence imaging and hyperspectral imaging. Disease detection based on VOC profiles involves using analysis of electronic nasal or GC-MS-based volatile metabolites released from healthy and diseased crops as a tool for identifying diseases. It will be. I would. Some of the challenges of these technologies are influenced by (1) the background data of the resulting profile data, (2) optimization of the technology for specific crop diseases and (3) continuous automation. Monitor Technology automation. Crop diseases under actual field conditions. This study shows that methods for detecting these diseases have excellent potential for the ability to accurately detect crop diseases. Spectroscopic and imaging technologies can be integrated with autonomous agricultural vehicles to achieve superior crop disease control and control through reliable real-time crop disease detection.

Table 6. Comparison of various types of crop disease detection.


This work was supported by the Industrial Strategic Technology Development Program (20003975, Development of intelligent 30kw crawler-based traveling platform for multi-purpose farming) funded by the Ministry of Trade, Industry & Energy (Ml, Korea).


1 Aleixos, N., Blasco, J., Navarrón, F., Moltó, E., 2002. Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Computers and Electronics in Agriculture 33 (2), 121–137.  

2 Alvarez, A.M., 2004. Integrated approaches for detection of plant pathogenic bacteria and diagnosis of bacterial diseases. Annual Review of Plant Pathology 42, 339–366.  

3 Anwar Haq, M., Collin, H.A., Tomsett, A.B., Jones, M.G., 2003. Detection of Sclerotium cepivorum within onion plants using PCR primers. Physiological and Molecular Plant Pathology 62 (3), 185–189.  

4 Balasubramanian, S., Panigrahi, S., Logue, C.M., Doetkott, C., Marchello, M., Sherwood, J.S., 2008. Independent component analysis-processed electronic nose data for predicting Salmonella typhimurium populations in contaminated beef. Food Control 19 (3), 236–246.  

5 Belasque, L., Gasparoto, M.C.G., Marcassa, L.G., 2008. Detection of mechanical and disease stresses in citrus plants by fluorescence spectroscopy. Applied Optics 47 (11), 1922–1926.  

6 Bertolini, E., Penyalver, R., García, A., Quesada, J.M., Cambra, M., Olmos, A., López, M.M., 2003. Highly sensitive detection of Pseudomonas savastanoi pv. savastanoi in asymptomatic olive plants by nested-PCR in a single closed tube. Journal of Microbiological Methods 52, 261–266.  

7 Blasco, J., Alexios, N., Gómez, J., Moltó, E., 2007. Citrus sorting by identification of the most common defects using multispectral computer vision. Journal of Food Engineering 83 (3), 384–393.  

8 Bravo, C., Moshou, D., West, J., McCartney, A., Ramon, H., 2003. Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering 84 (2), 137–145.  

9 Bravo, C., Moshou, D., Oberti, R., West, J., McCartney, A., Bodria, L., Ramon, H., 2004. Foliar disease detection in the field using optical sensor fusion. Agricultural Engineering International: the CIGR Journal of Scientific Research and Development, Manuscript FP 04 008, Vol. VI. December 2004.  

10 Cerovic, Z.G., Samson, G., Morales, F., Tremblay, N., Moya, I., 1999. Ultravioletinduced fluorescence for plant monitoring: present state and prospects. Agronomie 19, 543–578. Cevallos-Cevallos, J.M., Rouseff, R., Reyes-De-Corcuera, J.I., 2009. Untargeted metabolite analysis of healthy and Huanglongbing-infected orange leaves by CE-DAD. Electrophoresis 30, 1–8.  

11 Chaerle, L., Caeneghem, W.V., Messens, E., Lamber, H., Van Montagu, M., Van Der Straeten, D., 1999. Presymptomatic visualization of plant–virus interactions by thermography. Nature Biotechnology 17, 813–816.    

12 Chaerle, L., Van Der Straeten, D., 2000. Imaging techniques and the early detection of plant stress. Trends in Plant Science 5 (11), 495–501.  

13 Chaerle, L., De Boever, F., Van Montagu, M., Van Der Straeten, D., 2001. Thermographic visualization of cell death in tobacco and Arabidopsis. Plant, Cell and Environment 24 (1), 15–25.  

14 Chaerle, L., Hulsen, K., Hermans, C., Strasser, R.J., Valcke, R., Höfte, M., Van Der Straeten, D., 2003. Robotized time-lapse imaging to assess in-plant uptake of phenylurea herbicides and their microbial degradation. Physiologia Plantarium 118, 613–619.  

15 Chaerle, L., Hagenbeek, D., De Bruyne, E., Valcke, R., Van Der Straeten, D., 2004. Thermal and chlorophyll-fluorescence imaging distinguish plant–pathogen interactions at an early stage. Plant and Cell Physiology 45, 887–896.    

16 Chaerle, L., Lenk, S., Hagenbeek, D., Buschmann, C., Van Der Straeten, D., 2007. Multicolor fluorescence imaging for early detection of the hypersensitive reaction to tobacco mosaic virus. Journal of Plant Physiology 164 (3), 253–262.    

17 Chen, B.,Wang, K., Li, S.,Wang, J., Bai, J., Xiao, C., Lai, J., 2008. Spectrum characteristics of cotton canopy infected with verticillium wilt and inversion of severity level. In IFIP International Federation for Information Processing, Volume 259; Computer and Computing Technologies in Agriculture, vol. 2, Daoliang Li, Springer, Boston, pp. 1169–1180.  

18 Choi, Y.H., Tapias, E.C., Kim, H.K., Lefeber, A.W.M., Erkelens, C., Verhoeven, J.T.J., Brzin, J., Zel, J., Verpoorte, R., 2004. Metabolic discrimination of Catharanthus roseus leaves infected by phytoplasma using 1H-NMR spectroscopy and multivariate data analysis. Plant Physiology 135, 2398–2410.      

19 Concina, I., Falasconi, M., Gobbi, E., Bianchi, F., Musci, M., Mattarozzi, M., Pardo, M., Mangia, A., Careri, M., Sberveglieri, G., 2009. Early detection of microbial contamination in processed tomatoes by electronic nose. Food Control 20 (10), 873–880.  

20 Costa, G., Noferini, M., Fiori, G., Spinelli, F., 2007. Innovative application of nondestructive techniques for fruit quality and disease diagnosis. Acta Horticulturae 753 (1), 275–282. Das, A.K., 2004. Rapid detection of Candidatus Liberibacter asiaticus, the bacterium associated with citrus Huanglongbing (Greening) disease using PCR. Current Science 87 (9), 1183–1185.  

21 Delalieux, S., van Aardt, J., Keulemans, W., Schrevens, E., Coppin, P., 2007. Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications. European Journal of Agronomy 27 (1), 130–143.  

22 Delwiche, S.R., Kim, M.S., 2000. Hyperspectral imaging for detection of scab in wheat. Proceedings of SPIE 4203, 13–20.  

23 Delwiche, S.R., Graybosch, R.A., 2002. Identification of waxy wheat by near-infrared reflectance spectroscopy. Journal of Cereal Science 35 (1), 29–38.  

24 Di Natale, C., Macagnano, A., Martinelli, E., Paolesse, R., Proietti, E., D’Amico, A., 2001. The evaluation of quality of post-harvest oranges and apples by means of an electronic nose. Sensors and Actuators B: Chemical 78 (1–3), 26–31.  

25 Dragonieri, S., Schot, R., Mertens, B.J.A., Le Cessie, S., Gauw, S.A., Spanevello, A., Resta, O., Willard, N.P., Vink, T.J., Rabe, K.F., Bel, E.H., Sterk, P.J., 2007. An electronic nose in the discrimination of patients with asthma and controls. Journal of Allergy and Clinical Immunology 120 (4), 856–862.  

26 Dudareva, N., Negre, F., Nagegowda, D.A., Orlova, I., 2006. Plant volatiles: recent advances and future perspectives. Critical Reviews in Plant Sciences 25, 417–440.  

27 ElMasry, G., Wang, N., Vigneault, C., Qiao, J., ElSayed, A., 2008. Early detection of apple bruises on different background colors using hyperspectral imaging. LWT Food Science and Technology 41 (2), 337–345.  

28 Evans, P., Persaud, K.C., McNeish, A.S., Sneath, R.W., Hobson, N., Magan, N., 2000. Evaluation of a radial basis function neural network for the determination of wheat quality from electronic nose data. Sensors and Actuators B: Chemical 69 (3), 348–358.  

29 Ewen, R.J., Jones, P.R.H., Ratcliffe, N.M., Spencer-Phillips, P.T.N., 2004. Identification by gas chromatography-mass spectrometry of the volatile organic compounds emitted from the wood-rotting fungi Serpula lacrymans and Coniophora puteana, and from Pinus sylvestris timber. Mycology Research 108 (7), 806– 814.  

30 Falasconi, M., Gobbi, E., Pardo, M., Della Torre, M., Bresciani, A., Sberveglieri, G., 2005. Detection of toxigenic strains of Fusarium verticillioides in corn by electronic olfactory system. Sensors and Actuators B: Chemical 108 (1–2), 250–257.    

31 Fang, Y., Xu, L.H., Tian, W.X., Huai, Y., Yu, S.H., Lou, M.M., Xie, G.L., 2009. Real-time fluorescence PCR method for detection of Burkholderia glumae from rice. Rice Science 16 (2), 157–160.  

32 Gardner, J.W., Shin, H.W., Hines, E.L., 2000. An electronic nose system to diagnose illness. Sensors and Actuators B: Chemical 70 (1–3), 19–24.  

33 Gomez, A.H., He, Y., Garcia Pereira, A., 2006. Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR spectroscopy techniques. Journal of Food Engineering 77 (3), 313–319.  

34 Goodman, B.A., Williamson, B., Chudek, A., 1992. Non-invasive observation of the development of fungal infection in fruit. Protoplasma 166, 107–109.  

35 Gowen, A.A., O’Donnell, C.P., Cullen, P.J., Downey, G., Frias, J.M., 2007. Hyperspectral imaging—an emerging process analytical tool for food quality and safety control. Trends in Food Science & Technology 18 (12), 590–598.  

36 Graeff, S., Link, J., Claupein, W., 2006. Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements. Central European Journal of Biology 1, 275–288.  

37 Griffin, M.K., Burke, H.K., 2003. Compensation of hyperspectral data for atmospheric effects. Lincoln Laboratory Journal 14 (1), 29–54.  

38 Guenther, A., 1997. Seasonal and spatial variations in natural volatile organic compound emissions. Ecological Applications 7 (1), 34–45.  

39 Guimet, F., 2005. Olive oil characterization using excitation-emission fluorescence spectroscopy and three-way methods of analysis. Ph.D. thesis, Rovira i Virgili University, Spain. Guo, T.T., Guo, L., Wang, X.H., Li, M., 2009. Application of NIR spectroscopy in classification of plant species. In: International Workshop on Education Technology and Computer Science, Wuhan, Hubei, China, vol. 3, pp. 879–883.  

40 Gutiérrez-Aguirre, I., Mehle, N., Delic, D., Gruden, K., Mumford, R., Ravnikar, M., ´ 2009. Real-time quantitative PCR based sensitive detection and genotype discrimination of Pepino mosaic virus. Journal of Virological Methods 162 (1–2), 46–55.    

41 Hadjiloucas, S., Walker, G.C., Bowen, J.W., Zafiropoulos, A., 2009. Propagation of errors from a null balance terahertz reflectometer to a sample’s relative water content. Journal of Physics: Conference Series, Sensors & their Applications XV 178, 012012, 1–5.  

42 Hahn, F., 2009. Actual pathogen detection: Sensors and algorithms—a review. Algorithms 2, 301–338.  

43 Henson, J.M., French, R., 1993. The polymerase chain reaction and plant disease diagnosis. Annual Review of Plant Pathology 31, 81–109.    

44 Huang, J.F., Apan, A., 2006. Detection of Sclerotinia rot disease on celery using hyperspectral data and partial least squares regression. Journal of Spatial Science 51 (2), 129–142.  

45 Huang, M.Y., Huang, W.H., Liu, L.Y., Huang, Y.D., Wang, J.H., Zhao, C.H., Wan, A.M., 2004. Spectral reflectance feature of winter wheat single leaf infested with stripe rust and severity level inversion. Transactions of the CSAE 20 (1), 176–180.  

46 Huang, W., Lamb, D.W., Niu, Z., Zhang, Y., Liu, L., Wang, J., 2007. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture 8, 187–197.  

47 Karunakaran, C., Jayas, D.S.,White, N.D.G., 2004. Identification of wheat kernels damaged by the red flour beetle using X-ray images. Biosystems Engineering 87 (3), 267–274.  

48 Kim, M.S., Chen, Y.R., Mehl, P.M., 2001. Hyperspectral reflectance and fluorescence imaging system for food quality and safety. Transactions of the ASAE 44 (3), 721–729.  

49 Kim, M.S., Lefcourt, A.M., Chao, K., Chen, Y.R., Kim, I., Chan, D.E., 2002. Multispectral detection of fecal contamination on apples based on hyperspectral imagery: Part I. Application of visible and near–infrared reflectance imaging. Transactions of the ASAE 45 (6), 2027–2037.  

50 Kobayashi, T., Kanda, E., Kitada, K., Ishiguro, K., Torigoe, Y., 2001. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathology 91 (3), 316–323.    

51 Kreuzwieser, J., Harren, F.J.M., Laarhoven, L.J.J., Boamfa, I., Lintel-Hekkertb, S., Scheerera, U., Hüglina, C., Rennenberga, H., 2001. Acetaldehyde emission by the leaves of trees–correlation with physiological and environmental parameters. Physiologia Plantarum 113, 41–49.  

52 Kushalappa, A.C., Lui, L.H., Chen, C.R., Lee, B., 2002. Volatile fingerprinting (SPMEGCFID) to detect and discriminate diseases of potato tubers. Plant Disease 86, 131–137.    

53 Lacava, P.T., Li, W.B., Araújo, W.L., Azevedo, J.L., Hartung, J.S., 2006. Rapid, specific and quantitative assays for the detection of the endophytic bacterium Methylobacterium mesophilicum in plants. Journal of Microbiological Methods 65, 535–541.    

54 Lamkadmi, Z., Esnault, M.A., Le Normand, M., 1996. Characterization of a 23 kDa polypeptide induced by Phoma lingam in Brassica napus leaves. Plant Physiology and Biochemistry 34 (4), 589–598.  

55 Laothawornkitkul, J., Moore, J.P., Taylor, J.E., Possell, M., Gibson, T.D., Hewitt, C.N., Paul, N.D., 2008. Discrimination of plant volatile signatures by an electronic nose: a potential technology for plant pest and disease monitoring. Environmental Science and Technology 42, 8433–8439.    

56 Larsolle, A., Muhammed, H.H., 2007. Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density. Precision Agriculture 8 (1–2), 37–47.  

57 Lee, K.J., Kang, S., Kim, M.S., Noh, S.H., 2005. Hyperspectral imaging for detecting defect on apples. ASABE Paper No. 053075, 2005 ASAE Annual International Meeting, Tampa, FL, 17–20 July, 2005.  

58 Lee, W.S., Ehsani, R., Albrigo, L.G., 2008. Citrus greening disease (Huanglongbing) detection using aerial hyperspectral imaging. In: The Proceedings of the 9th International Conference on Precision Agriculture, July 20–23, 2008, Denver, CO.  

59 Lenk, S., Buschmann, C., 2006. Distribution of UV-shielding of the epidermis of sun and shade leaves of the beech (Fagus sylvatica L.) as monitored by multi-colour fluorescence imaging. Journal of Plant Physiology 163 (12), 1273–1283.    

60 Lenk, S., Chaerle, L., Pfündel, E.E., Langsdorf, G., Hagenbeek, D., Lichtenthaler, H.K., Van Der Straeten, D., Buschmann, C., 2007. Multispectral fluorescence and reflectance imaging at the leaf level and its possible applications. Journal of Experimental Botany 58 (4), 807–814.    

61 Lenthe, J.H., Oerke, E.C., Dehne, H.W., 2007. Digital infrared thermography for monitoring canopy health of wheat. Precision Agriculture 8 (1–2), 15–26.  

62 Li, W., Hartung, J.S., Levy, L., 2006. Quantitative real-time PCR for detection and identification of Candidatus Liberibacter species associated with citrus Huanglongbing. Journal of Microbiological Methods 66 (1), 104–115.    

63 Li, W., Abad, J.A., French-Monar, R.D., Rascoe, J., Wen, A., Gudmestad, N.C., Secor, G.A., Lee, I.M., Duan, Y., Levy, L., 2009a. Multiplex real-time PCR for detection, identification and quantification of ‘Candidatus Liberibacter solanacearum’ in potato plants with zebra chip. Journal of Microbiological Methods 78 (1), 59– 65.    

64 Li, C., Krewer, G., Kays, S. J., 2009b. Blueberry postharvest disease detection using an electronic nose. ASABE Paper No. 096783, ASABE Annual International Meeting, Reno, NV, June 21–June 24, 2009.  

65 Lin, Y.J., Guo, H.R., Chang, Y.H., Kao, M.T., Wang, H.H., Hong, R.I., 2001. Application of the electronic nose for uremia diagnosis. Sensors and Actuators B: Chemical 76 (1–3), 177–180.  

66 Lindenthal, M., Steiner, U., Dehne, H.W., Oerke, E.-C., 2005. Effect of downy mildew development on transpiration of cucumber leaves visualized by digital infrared thermography. Phytopathology 95 (3), 233–240.  

67 Lins, E.C., Belasque Junior, J., Marcassa, L.G., 2009. Detection of citrus canker in citrus plants using laser induced fluorescence spectroscopy. Precision Agriculture 10, 319–330.  

68 López, M.M., Bertolini, E., Olmos, A., Caruso, P., Gorris, M.T., Llop, P., Penyalver, R., Cambra, M., 2003. Innovative tools for detection of plant pathogenic viruses and bacteria. International Microbiology 6, 233–243.    

69 Lu, R., 2003. Detection of bruises on apples using near-infrared hyperspectral imaging. Transactions of the ASAE 46 (2), 523–530.  

70 Lui, L., Vikram, A., Hamzehzarghani, H., Kushalappa, A.C., 2005. Discrimination of three fungal diseases of potato tubers based on volatile metabolic profiles developed using GC/MS. Potato Research 48, 85–96.  

71 Mahesh, S., Manickavasagan, A., Jayas, D.S., Paliwal, J.,White, N.D.G., 2008. Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes. Biosystems Engineering 101 (1), 50–57.  

72 Malthus, T.J., Madeira, A.C., 1993. High resolution spectroradiometry: spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sensing of Environment 45, 107–116.  

73 Marcassa, L.G., Gasparoto, M.C.G., Belasque Junior, J., Lins, E.C., Dias Nunes, F., Bagnato, V.S., 2006. Fluorescence spectroscopy applied to orange trees. Laser Physics 16 (5), 884–888.  

74 Markom, M.A., Md Shakaff, A.Y., Adom, A.H., Ahmad, M.N., Wahyu Hidayat, Abdullah, A.H., Ahmad Fikri, N., 2009. Intelligent electronic nose system for basal stem rot disease detection. Computers and Electronics in Agriculture 66 (2), 140–146.  

75 Mehl, P.M., Chen, Y.R., Kim, M.S., Chan, D.E., 2004. Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. Journal of Food Engineering 61 (1), 67–81.  

76 Minsavage, G.V., Thompson, C.M., Hopkins, D.L., Leite, R.M.V.B.C., Stall, R.E., 1994. Development of a polymerase chain reaction protocol for detection of Xylella fastidiosa in plant tissue. Phytopathology 84, 456–461.  

77 Moalemiyan, M., Vikram, A., Kushalappa, A.C., Yaylayan, V., 2006. Volatile metabolite profiling to detect and discriminate stem-end rot and anthracnose diseases of mango fruits. Plant Pathology 55, 792–802.  

78 Moalemiyan, M., Vikram, A., Kushalappa, A.C., 2007. Detection and discrimination of two fungal diseases of mango (cv. Keitt) fruits based on volatile metabolite profiles using GC/MS. Postharvest Biology and Technology 45, 117–125.  

79 Moshou, D., Bravo, C.,West, J.,Wahlen, S., McCartney, A., Ramon, H., 2004. Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture 44 (3), 173–188.  

80 Moshou, D., Bravo, C., Oberti, R., West, J., Bodria, L., McCartney, A., Ramon, H., 2005. Plant disease detection based on data fusion of hyper-spectral andmulti-spectral fluorescence imaging using Kohonen maps. Real-Time Imaging 11 (2), 75– 83.  

81 Moshou, D., Bravo, C., Wahlen, S., West, J., McCartney, A., De Baerdemaeker, J., Ramon, H., 2006. Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self-organising maps. Precision Agriculture 7 (3), 149–164.  

82 Muhammed, H.H., 2002. Using hyperspectral reflectance data for discrimination between healthy and diseased plants, and determination of damage-level in diseased plants. In: IEEE: Proceedings of the 31st Applied Imagery Pattern Recognition Workshop, pp. 49–54.  

83 Muhammed, H.H., 2005. Hyperspectral crop reflectance data for characterizing and estimating fungal disease severity in wheat. Biosystems Engineering 91 (1), 9–20.  

84 Naidu, R.A., Perry, E.M., Pierce, F.J., Mekuria, T., 2009. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 S. Sankaran et al. / Computers and Electronics in Agriculture 72 (2010) 1–13 13 in two red-berried wine grape cultivars. Computers and Electronics in Agriculture 66, 38–45.  

85 Narvankar, D.S., Singh, C.B., Jayas, D.S., White, N.D.G., 2009. Assessment of soft X-ray imaging for detection of fungal infection in wheat. Biosystems Engineering 103 (1), 49–56.  

86 Nicolaï, B.M., Lötze, E., Peirs, A., Scheerlinck, N., Theron, K.I., 2006. Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging. Postharvest Biology and Technology 40 (1), 1–6.  

87 Oerke, E.C., Lindenthal, M., Fröhling, P., Steiner, U., 2005. Digital infrared thermography for the assessment of leaf pathogens. In: Stafford, J.V. (Ed.), Precision Agriculture ’05. Wageningen University Press, Wageningen, The Netherlands, pp. 91–98.  

88 Oerke, E.C., Steiner, U., Dehne, H.W., Lindenthal, M., 2006. Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. Journal of Experimental Botany 57 (9), 2121–2132.    

89 Okamoto, H., Suzuki, Y., Kataoka, T., Sakai, K., 2009. Unified hyperspectral imaging methodology for agricultural sensing using software framework. Acta Horticulturae 824, 49–56.  

90 Pearson, T.C., Wicklow, D.T., 2006. Detection of kernels infected by fungi. Transactions of the ASABE 49 (4), 1235–1245. Pimentel, D., Zuniga, R., Morrison, D., 2005. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics 52 (3), 273–288.  

91 Polischuk, V.P., Shadchina, T.M., Kompanetz, T.I., Budzanivskaya, I.G., Sozinov, A., 1997. Changes in reflectance spectrum characteristic of Nicotiana debneyi plant under the influence of viral infection. Archives of Phytopathology and Plant Protection 31 (1), 115–119.  

92 Pontius, J., Hallett, R., Martin, M., 2005. Assessing hemlock decline using visible and near-infrared spectroscopy: indices comparison and algorithm development. Applied Spectroscopy 59 (6), 836–843.    

93 Prithiviraj, B., Vikram, A., Kushalappa, A.C., Yaylayam, V., 2004. Volatile metabolite profiling for the discrimination of onion bulbs infected by Erwinia carotovora ssp. carotovora, Fusarium oxysporum and Botrytis allii. European Journal of Plant Physiology 110, 371–377.  

94 Purcell, D.E., O’Shea, M.G., Johnson, R.A., Kokot, S., 2009. Near-infrared spectroscopy for the prediction of disease rating for Fiji leaf gall in sugarcane clones. Applied Spectroscopy 63 (4), 450–457.    

95 Qin, J., Burks, T.F., Kim, M.S., Chao, K., Ritenour, M.A., 2008. Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method. Sensing and Instrumentation for Food Quality and Safety 2, 168–177.  

96 Qin, J., Burks, T.F., Ritenour, M.A., Bonn, W.G., 2009. Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering 93 (2), 183–191.  

97 Rajamäki, T., Alakomi, H.L., Ritvanen, T., Skyttä, E., Smolander, M., Ahvenainen, R., 2006. Application of an electronic nose for quality assessment of modified atmosphere packaged poultry meat. Food Control 17 (1), 5–13.  

98 Ramon, H., Anthonis, J., Vrindts, E., Delen, R., Reumers, J., Moshou, D., Deprez, K., De Baerdemaeker, J., Feyaerts, F., Van Gool, L., DeWinne, R., Van den Bulcke, R., 2002. Development of a weed activated spraying machine for targeted application of herbicides. Aspects of Applied Biology 66, 147–164.  

99 Roberts, M.J., Schimmelpfennig, D., Ashley, E., Livingston, M., Ash, M., Vasavada, U., 2006. The value of plant disease early-warning systems. Economic Research Service No. 18, United States Department of Agriculture.  

100 Roggo, Y., Duponchel, L., Huvenne, J.P., 2003. Comparison of supervised pattern recognition methods with McNemar’s statistical test: application to qualitative analysis of sugar beet by near-infrared spectroscopy. Analytica Chimica Acta 477 (2), 187–200.  

101 Ruiz-Ruiz, S., Ambrós, S., Carmen Vives, M., Navarro, L., Moreno, P., Guerri, J., 2009. Detection and quantification of Citrus leaf blotch virus by TaqMan real-time RTPCR. Journal of Virological Methods 160 (1–2), 57–62.    

102 Saponari, M., Manjunath, K., Yokomi, R.K., 2008. Quantitative detection of Citrus tristeza virus in citrus and aphids by real-time reverse transcription-PCR (TaqMan®). Journal of Virological Methods 147 (1), 43–53.    

103 Schaad, N.W., Frederick, R.D., 2002. Real-time PCR and its application for rapid plant disease diagnostics. Canadian Journal of Plant Pathology 24 (3), 250–258.  

104 Scharte, J., Schön, H., Weis, E., 2005. Photosynthesis and carbohydrate metabolism in tobacco leaves during an incompatible interaction with Phytophthora nicotianae. Plant, Cell and Environment 28, 1421–1435.  

105 Shafri, H.Z.M., Hamdan, N., 2009. Hyperspectral imagery for mapping disease infection in oil palm plantation using vegetation indices and red edge techniques. American Journal of Applied Sciences 6 (6), 1031–1035.  

106 Sighicelli, M., Colao, F., Lai, A., Patsaeva, S., 2009. Monitoring post-harvest orange fruit disease by fluorescence and reflectance hyperspectral imaging. ISHS Acta Horticulturae 817, 277–284.  

107 Sirisomboon, P., Hashimoto, Y., Tanaka, M., 2009. Study on non-destructive evaluation methods for defect pods for green soybean processing by near-infrared spectroscopy. Journal of Food Engineering 93, 502–512.  

108 Spinelli, F., Noferini, M., Costa, G., 2006. Near infrared spectroscopy (NIRs): Perspective of fire blight detection in asymptomatic plant material. Proceeding of 10th International Workshop on Fire Blight. Acta Horticulturae 704, 87–90.  

109 Staudt, M., Lhoutellier, L., 2007. Volatile organic compound emission from holm oak infested by gypsy moth larvae: evidence for distinct responses in damaged and undamaged leaves. Tree Physiology 27, 1433–1440.    

110 Sundaram, J., Kandala, C.V., Butts, C.L., 2009. Application of near infrared (NIR) spectroscopy to peanut grading and quality analysis: Overview. Sensing and Instrumentation for Food Quality and Safety 3 (3), 156–164.  

111 Tallada, J.G., Nagata, M., Kobayashi, T., 2006. Detection of bruises in strawberries by hyperspectral Imaging. ASABE Paper No. 063014, 2006 ASABE Annual International Meeting, Portland, Oregon, 9–12 July 2006.  

112 Teixeira, D.C., Danet, J.L., Eveillard, S., Martins, E.C., Junior, W.C.J., Yamamoto, P.T., Lopes, S.A., Bassanezi, R.B., Ayres, A.J., Saillard, C., Bové, J.M., 2005. Citrus huanglongbing in São Paulo State, Brazil: PCR detection of the ‘Candidatus’ Liberibacter species associated with the disease. Molecular and Cellular Probes 19, 173– 179.    

113 Tholl, D., Boland, W., Hansel, A., Loreto, F., Röse, U.S.R., Schnitzler, J.P., 2006. Practical approaches to plant volatile analysis. The Plant Journal 45, 540–560.    

114 Urasaki, N., Kawano, S., Mukai, H., Uemori, T., Takeda, O., Sano, T., 2008. Rapid and sensitive detection of “Candidatus Liberibacter asiaticus” by cycleave isothermal and chimeric primer-initiated amplification of nucleic acids. Journal of General Plant Pathology 74, 151–155.  

115 Vallat, A., Gu, H., Dorn, S., 2005. How rainfall, relative humidity and temperature influence volatile emissions from apple trees in situ. Phytochemistry 66, 1540–1550.    

116 Vikram, A., Lui, L.H., Hossain, A., Kushalappa, A.C., 2006. Metabolic fingerprinting to discriminate diseases of stored carrots. Annals of Applied Biology 148, 17–26.  

117 Vuorinen, T., Nerg, A.M., Syrjälä, L., Peltonen, P., Holopainen, J.K., 2007. Epirrita autumnata induced VOC emission of silver birch differ from emission induced by leaf fungal pathogen. Arthropod–Plant Interactions 1, 159–165.  

118 Wang, D., Ram, M.S., Dowell, F.E., 2002. Classification of damaged soybean seeds using near-infrared spectroscopy. Transactions of the ASAE 45 (6), 1943–1948.  

119 Wang, W., Thai, C., Li, C., Gitaitis, R., Tollner, E.W., Yoon, S.C., 2009. Detecting of sour skin diseases in Vidalia sweet onions using near-infrared hyperspectral imaging. In: 2009 ASABE Annual International Meeting, Reno, NV, Paper No. 096364.  

120 West, J.S., Bravo, C., Oberti, R., Lemaire, D., Moshou, D., McCartney, H.A., 2003. The potential of optical canopy measurement for targeted control of field crop disease. Annual Review of Phytopathology 41, 593–614.  

121 Williamson, B., Goodman, B.A., Chudek, J.A., 1992. Nuclear magnetic resonance (NMR) micro-imaging of ripening red raspberry fruits. New Phytologist 120, 21–28.  

122 Wu, D., Feng, L., Zhang, C., He, Y., 2008. Early detection of Botrytis cinerea on eggplant leaves based on visible and near-infrared spectroscopy. Transactions of the ASABE 51 (3), 1133–1139.  

123 Xing, J., Baerdemaeker, J.D., 2005. Bruise detection on ‘Jonagold’ apples using hyperspectral imaging. Postharvest Biology and Technology 37 (2), 152–162.  

124 Xing, J., Bravo, C., Jancsók, P.T., Ramon, H., Baerdemaeker, J.D., 2005. Detecting bruises on ‘golden delicious’ apples using hyperspectral imaging with multiple wavebands. Biosystems Engineering 90 (1), 27–36.  

125 Xu, H.R., Ying, Y.B., Fu, X.P., Zhu, S.P., 2007. Near-infrared spectroscopy in detecting leaf miner damage on tomato leaf. Biosystems Engineering 96 (4), 447–454.  

126 Yang, C.M., Cheng, C.H., 2001. Spectral characteristics of rice plants infested by brown planthoppers. Proceedings of the National Science Council, Republic of China. Part B, Life Sciences 25 (3), 180–186.  

127 Yang, C.M., Cheng, C.H., Chen, R.K., 2007. Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder. Crop Science 47, 329–335.  

128 Yao, H., Hruska, Z., DiCrispino, K., Brabham, K., Lewis, D., Beach, J., Brown, R.L., Cleveland, T.E., 2005. Differentiation of fungi using hyperspectral imagery for food inspection. ASAE Paper No. 053127, 2005 ASAE Annual International Meeting, Tampa, FL, 17–20 July 2005.  

129 Yvon, M., Thébaud, G., Alary, R., Labonne, G., 2009. Specific detection and quantification of the phytopathogenic agent ‘Candidatus Phytoplasma prunorum’. Molecular and Cellular Probes 23 (5), 227–234.    

130 Zhang, C., Shen, Y., Chen, J., Xiao, P., Bao, J., 2008a. Nondestructive prediction of total phenolics, flavonoid contents, and antioxidant capacity of rice grain using near-infrared spectroscopy. Journal of Agricultural and Food Chemistry 56 (18), 8268–8272.    

131 Zhang, H., Chang, M., Wang, J., Ye, S., 2008b. Evaluation of peach quality indices using an electronic nose by MLR, QPST and BP network. Sensors and Actuators B: Chemical 134 (1), 332–338.  

132 Zhang, H., Wang, J., 2007. Detection of age and insect damage incurred by wheat, with an electronic nose. Journal of Stored Products Research 43, 489– 495.  

133 Zhang, M., Qin, Z., Liu, X., Ustin, S.L., 2003. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation 4, 295– 310.  

134 Zhang, M., Qin, Z., Liu, X., 2005. Remote sensed spectral imagery to detect late blight in field tomatoes. Precision Agriculture 6 (6), 489–508.