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Color Image Segmentation Using Perceptual Spaces Through Applets for Determining and Preventing Diseases in Chili Peppers

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Color Image Segmentation Using Perceptual Spaces Through Applets for Determining and Preventing Diseases in Chili Peppers African Journal of Biotechnology Vol. 12(7), pp. 679-688, 13 February, 2013 Available online at http://www.academicjournals.org/AJB DOI: 10.5897/AJB12.1198 ISSN 1684–5315 ©2013 Academic Journals Full Length Research Paper Color image segmentation using perceptual spaces through applets for determining and preventing diseases in chili peppers J. L. González-Pérez1 , M. C. Espino-Gudiño2, J. Gudiño-Bazaldúa3, J. L. Rojas-Rentería4, V. Rodríguez-Hernández5 and V.M. Castaño6 1Computer and Biotechnology Applications, Faculty of Engineering, Autonomous University of Queretaro, Cerro de las Campanas s/n, C.P. 76010, Querétaro, Qro., México. 2Faculty of Psychology, Autonomous University of Queretaro, Cerro de las Campanas s/n, C.P. 76010, Querétaro, Qro., México. 3Faculty of Languages Letters, Autonomous University of Queretaro, Cerro de las Campanas s/n, C.P. 76010, Querétaro, Qro., México. 4New Information and Communication Technologies, Faculty of Engineering, Department of Intelligent Buildings, Autonomous University of Queretaro, Cerro de las Campanas s/n, C.P. 76010, Querétaro, Qro., México. 5New Information and Communication Technologies, Faculty of Computer Science, Autonomous University of Queretaro, Cerro de las Campanas s/n, C.P. 76010, Querétaro, Qro., México. 6Center for Applied Physics and Advanced Technology, National Autonomous University of Mexico. Accepted 5 December, 2012 Plant pathogens cause disease in plants. Chili peppers are one of the most important crops in the world. There are currently disease detection techniques classified as: biochemical, microscopy, immunology, nucleic acid hybridization, identification by visual inspection in vitro or in situ but these have the following disadvantages: they require several days, their implementation is costly and highly trained. This paper proposes a method for knowing and preventing the disease in chili peppers plant through a color image processing, using online system developed in Java applets. This system gets results in real time and remotely (Internet). The images are converted to perceptual spaces [hue, saturation and lightness (HSL), hue, saturation, and intensity (HSI) and hue saturation and value (HSV)]. Sequence was applied to the proposed method. HSI color space was the best detected disease. The percentage of disease in the leaf is of 12.42%. HSL and HSV do not expose the exact area of the disease compared to the HSI color space. Finally, images were analyzed and the disease is known by the expert in plant pathology to take preventive or corrective actions. Key words: Applets, knowing disease, color image segmentation, perceptual spaces. INTRODUCTION Since the beginning of agriculture, there are problems in Mondino, 2008; Valadez-Bustos et al., 2009). Chili crop production due to the presence of plant diseases. peppers belong to the genus Capsicum of the Chili's production, as with other crops, is affected by Solanaceae family of plants (Ochoa-Alejo and Ramírez- pathogens that reduce their quality (Berrocal, 2009; Malagón, 2001). Capsicum annuum L. is the species most widely grown throughout the world (Mahasuk et al, 2009; Moscone et al, 2007). Some diseases can be diagnosed easily by visual inspection, but others require *Corresponding author. E-mail: [email protected]. Tel: laboratory tests for diagnosis (González-Pérez et al., 014421921200. 2011). These procedures may require several days or 680 Afr. J. Biotechnol. weeks, and in some cases have limited sensitivity. by its components red, green and blue (RGB). RGB Fortunately with the advancement of biotechnology, there model is recommended for viewing the color, but it is not are now new techniques and products that are available good for analysis because it has high degree of to supplement or replace the laboratory procedures. correlation between the components R, G and B (Angulo, Results are obtained faster and diseases are diagnosed 2007; Angulo and Serra, 2007; Denis et al., 2007). In earlier. addition, the distance in RGB perceptual space does not In general, conventional methods to detect pathogens represent color differences as the human visual system in plants are classified as: biochemical, microscopy, perceives them. For that reason in image analysis and immunology, nucleic acid hybridization and other processing, these components are often transformed into traditional methods such as identification by visual another perceptual space (Ortiz et al., 2002; Yang et al., inspection in situ or in vitro pure cultures by microscopic 2010). Perceptual spaces are best suited to represent the examination (Fox, 1997). Some disadvantages of these color because they are more similar to how humans methods are described as: high cost of its items, the perceive and interpret color. The components of these requirement of specialized equipment and the need of a color models are the attributes of the human perception high level of appropriate training for its execution, of color: hue, saturation and luminance (intensity) (Ortiz because of the possibility of contamination and the et al., 2002). consequent production of false positives. Other Importance of using color image processing is because disadvantages are the strict biosecurity requirements if it is a powerful descriptor that in most cases simplifies the radiolabeling is used. Biological assays on its part have identification and removal of objects in scene (Sharma the disadvantage of being laborious and expensive, due and Trussell, 1997; Youngbae et al., 2008). Besides the to the requirement of temperature-controlled facilities, fact that humans are able to distinguish approximately 10 extended period for the manifestation of symptoms and million colors under optimal conditions (Sharma and the need for specific indicator plants for each disease. It Trussell, 1997). However, one of the difficulties in color was observed that some pathogens can interfere with the image processing is that the color of an object depends expression of symptoms in materials with mixed not only on the reflective properties of its surfaces but infections. also on the light illuminations and on the properties of the Procedures used in optical and electron microscopy, imaging devices (Zhang and Georganas, 2004). It is allow efficient diagnosis of plant pathogens; which are not important to select an appropriate perceptual space to used in mass programs due to their limited capacity of represent the color of an image because the application analysis, high cost and requirement of specialized of different perceptual spaces can significantly change personnel (Leuven, 2006). Due to the disadvantages of the results of processing (Yang et al., 2010; Youngbae et the methods mentioned above, it is required to have al., 2008). For this reason, some authors have compared automatic methods to detect plant diseases. An area that the performance of several perceptual spaces. Stokman offers this possibility is the digital processing of color and Gevers (2005), proposed a selection framework for a images. Currently, the digital image processing is used in color model using the principles of diversification for a variety of applications to solve specific problems. image segmentation and edge detection. By formulating Detection of plant disease is not an exception. Some statistical and learning systems, researchers found the advantages of this field are as follows: optimal color channels and their weights. A comparison of edge detectors in color image in multiple perceptual A. Does not require expensive equipment, spaces has also been presented (Wesolkowski at al., B. Is not required to have complex and highly equipped 2000). Edge detectors such as Sobel operator were laboratories, evaluated using multiple perceptual spaces (Youngbae et C. Does not need specialized training, al., 2008). D. Results of processing can be obtained quickly and Machine vision technology has been employed in many directly in situ, agricultural applications involving color grading. Machine E. Information may be available in real time via vision systems for real-time color classification (Lee and networked systems and Anbalagan, 1995; Lee, 2000; Zhang et al., 1998) have F. Detection techniques are not invasive because they been commercialized to grade food products based on use digital images and, the plan is not affected. color. Other agricultural applications include the color grading of fresh market peaches (Miller and Delwiche, Color image processing, is one of the most interesting 1989a, b; Nimesh et al., 1993; Singh et al., 1992), apples topics in recent years in the area of image processing. (Hung, 1995; Varghese et al., 1991), potatoes (Tao et al., Color is very important in the detection of plant disease. 1995), peppers (Shearer and Payne, 1990), cucumbers For example, Pepper huasteco Yellow Vein Virus (Lin et al., 1993), tomatoes (Choi et al., 1995), and dates (PHYVV) is one of the pathogens that affect the (Janobi, 1998). Many of these systems have shown very cultivation of chili pepper and one of its symptoms is a promising results (Lee et al., 2008). Although, in México yellow mosaic in diseased leaves. Color image is specified there is no machine vision with applets applied to Chili's González-Pérez et al. 681 production; this is a big concern because Mexico ranks the quantitative treatment of the images. A representation second as producer of chili in the world after China should be based on distances or norms for vectors of the (Valadez-Bustos et al., 2009). points in the space of representation
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