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Penang Invention, Innovation And Research Design 2017 (PIID 2017) Project Title: FUNGAL LEAF DISEASE DETECTION FOR AGRICULTURAL STUDENTS A Professional Category B IPT Students √ Specialization Area ICT Mechanical Eng. Electrical Eng. Civil Eng. Chemical Eng. Design & Creativity Health Sciences Social Sciences √ Noraini Hasan1, Norbahiyah Awang1, Nurbaity Sabri1 Diana Juniza Juanis2, Nabilah Zakaria1 Project Member(s) Affiliation Email Correspondence 1 Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (Melaka), Kampus Jasin, Melaka, Malaysia 2 Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (Negeri Sembilan), Kampus Seremban 3, Negeri Sembilan, Malaysia 1 [email protected], [email protected], 1 [email protected], [email protected], 1 [email protected] Noraini Hasan Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (Melaka), Kampus Jasin, 77300 Merlimau, Melaka , Malaysia. Tel: +606-2645607, Fax:+ 606-2645016 Penang Invention, Innovation And Research Design 2017 (PIID 2017) Abstract Keywords Early detection of plant disease is very important to prevent serious outbreak. There are various causes of diseases on plants. One of it is a fungal infection. This project proposes and experimentally evaluates a framework of detection and classification of leaf diseases using image processing techniques and Support Vector Machine (SVM). A few samples of infected leaves were collected by capturing their images using a digital camera with the specific calibration procedure under controlled environment. The classification of the leaf diseases is based on texture feature extraction using Red Green Blue (RGB) color model. This RGB pixel color extracted from the identified Region Of Interest (ROI). The proposed automated classification model employs a classification of SVM in Matlab for disease classification. The proposed techniques based on performance results are promising with accuracy. Disease Detection, Texture Features, K-Means, SVM, GLCM. Product description This product is developed to identify the disease of the leaves cause by fungal infection using image processing techniques. The image of infected leaf is converted to grayscale image. The grayscale image is then segmented into several clusters, using k-mean clustering. It finds partitions such that objects within each cluster are as close to each other as possible, and as far from objects in other clusters as possible. The clustering will extract the disease area of the leaf. The K-means clustering is set to use squared Euclidean distance. Gray level concurrences matrices (GLCM) is used to extract the texture feature of the leaves. This feature is the best for classification on types of leaf disease. Classification of image is done by using Support Vector Machine (SVM) and the area of fungal affected leaf will be calculated based on region of interest cluster image affected leaf. Lastly, the accuracy is defined as the ratio of correctly recognized image samples to the total number of samples image tested. Penang Invention, Innovation And Research Design 2017 (PIID 2017) Pictures/ Schematic diagrams/ Flow Charts/Screenshots /Graphs and etc. *Guidelines We suggest that you use the insert picture function to insert the graphics (which is ideally a 300 dpi resolution TIFF or EPS file with all fonts embedded) because this method is somewhat more stable than directly inserting (copy-paste) a picture. Penang Invention, Innovation And Research Design 2017 (PIID 2017) Novelty and uniqueness This computer application has uniqueness where it is an automated detection system. Compared to current practice that used naked eyes in identifying the diseases; this application provides unbiased and more consistent result. Moreover, this application will not contaminate the plant as it is only required the image of the infected leaves as input. Benefit to mankind This application is developed to facilitate agriculture students to identify the disease cause by fungal infection. With this application, the diseases can be detected much faster and much more accurate, compared to current practice. A consistent and unbiased outcome may result in saving the plant as the appropriate treatment can be provided to the plant. Potential commercialization This application can play an important role as teaching and learning equipment especially for agriculture study. With suitable improvement and appropriate upgrading made on this application, such as making this application mobile and expanding the types of disease detected, this application can be a useful tool for the agriculture industry. Acknowledgment Penang Invention, Innovation And Research Design 2017 (PIID 2017) Noraini Hasan is an academician in the Faculty of Computer and Mathematical Sciences with a specialisation in mathematics at UiTM (Melaka) Kampus Alor Gajah. She's holding a Masters of Mathematical Science from Universiti Teknologi Malaysia. Researchers Biographical Data *Guidelines: We suggest that you use the insert picture function to insert the photo for each your project member(s) (which is ideally a 300 dpi resolution TIFF or EPS file with all fonts embedded) because this method is somewhat more stable than directly inserting (copy-paste) a picture. Norbahiyah Awang is an academician in the Faculty of Computer and Mathematical Sciences with a specialisation in Mathematics at UiTM (Melaka) Kampus Jasin. She's holding a Masters of Mathematical Science from Universiti Kebangsaan Malaysia. Nurbaity Sabri is an academician in the Faculty of Computer and Mathematical Sciences with a specialization in Image Processing at UiTM (Melaka) Kampus Jasin. She's holding a Masters of Computer Science from Universiti Teknologi Malaysia. Diana Juniza Juanis is an academician in the Faculty of Computer and Mathematical Sciences with a specialisation in actuarial sciences at UiTM (Negeri Sembilan) Kampus Seremban 3. She's holding a Masters of Actuary Science from City University of London. Photo Nabilah Zakaria is a graduate of the Faculty of Computer and Mathematical Sciences at UiTM (Melaka) Jasin. She's holding a Bachelor of computer science from UiTM (Melaka) Jasin.