Fruit disease detection and classification plays vital role in agriculture area and separation of disease and non-diseased fruits take more time. In this paper, we propose quad tree method to detect the diseased region from the fruit to facilitate effective classification. To detect diseased region we explored to check homogeneity of the sub-tree image pixel of quad tree. Subsequently, the diseased area is used for classification using deep learning approach. In deep learning six hidden layers are used. Further, we have collected 1000 samples of diseased and 1000 samples of non-diseased images from the 20 fruits class to conduct extensive experiment on both detection and classification. In experimentation, we compared the proposed method results with SVM and KNN classifier, the proposed method results shows that compared to SVM and KNN classifiers deep learning gives better results.

Deep Learning, Detection, Quad tree, Sementaion, Classification

[1] S R Dubey and A S Jalal, "Automatic Fruit Disease Classifiction using Images", Computer Vision and Image Processing in Intelligent Systems and Multimedia Technologies, 2014.
[2] J D Pujari, R Yakkundimath, A S Byadgi, "Grading and Classification of Anthranose Fungal Disease Fruits based on Statistical Texture Features", International Journal of AdvancedScience and Technology, Vol. 52, 2013.
[3] M S Hossian, M Al-Hammadi, and G Muhammad, "Automatic Fruit Classification using Deep Learning for Industrial Applications", IEEE Transactions on Industrial Informaics, Vol. 15, Issue. 2, pp.1027-1034, 2019.
[4] S K Behera, L Jena, A K Rath and P K Sthy, "Disease Classification and Grading of Orange using Machine Learning and Fuzzy Logic", 2018 Internaional Conference on Communication and Signal Processing (ICCSP), pp.0678-0682, 2018.
[5] A S M Shafi, Md. B Rahman and M M Rahman, "Fruit Disease Recogniton and Automatic Classification using MSVM with Multiple Features ", Internaional Journal of Computer Apllications, 181(10), pp.12-15, 2018.
[6] Dakshayini Patil, "Fruit Disease Detection using Image Processing Techniques", International Journal for Research in Engineering Application and Mangement(IJREAM), 2018.
[7] S A Gaikwad, K S Deore and M K Waykar, "Fruit Disease Detection and Classification", Internaional Journal of Engineering and Technology (IRJET), Vol.4, Issue.2, 2017.
[8] S Panda and P K Sethy, "Post-Harvest Grading for Carica Papaya Fruit using Image Segmentation and Soft Computing", International Journal for Advance Research in Computer Science, Vol.8, 2017.
[9] A S Nadarajan and A Thamizhar, "Detection of Bacterial Disease in Alphonso Mango using Image Processing", International Jornal for Advanced Research Trends in Engineering and Technology (IJARTET), Vol.4 Isuue.6, 2017.
[10] S Varughese, N Shinde, S Yadav and J Sisodia,"Learning-Based Fruit Disease using Image Processing", International Journal of Innovative and Emerging Research in Engineering, Vol.3, Issue.2, 2016.
[11] S T Khot, P Supriya, M Gitanjali and L Vidya, "Pomegranate Disease Detection using Image Processing Techniques ", International Journal of Advanced Research in Electrical and Electronics and Instrumentation Engineering, Vol.5, Issue.4, 2016.
[12] B J Samajpati and S D Degadwala, "Hybrid Approach for Apple Fruit Diseases Detection and Classification using Random Forest Classifier", 2016 International Con ference on communication and signal Processing (ICCSP), pp. 1015-1019, 2016.
[13] AAwate, D Deshmankar, G Amrutkar, U Bagul and S Sonavane, " Fruit Disease Detection using Color, Texture Analysis and ANN", 2015 International Conference on Green Computing and Internet Things (ICGCIoT) pp. 970-975, 2015.
[14] M Dhakate and A B Ingole, "Dignosis of Pomegranante plant Diseases using Neural Networks", 2015 Fifth National Conference on Computer Vision and Pattern Recogniton, Image Processing and Graphics (NCVPRIPG), pp. 1- 4, 2015.
[15] S H Mohana and C J Prabhakar,"Automatic Detection of Surface Defects onCitrus Fruit Based on Computer Vision Techniques", Iternational Journal of image, Graphics and Signal Processing,Vol.7, Issue. 9, pp. 11-19, 2015.
[16] A Mizushima and R Lu, "An Image Segmentation method for Apple Sorting and Grading using Support Vector Machine and Otsu`s Method", Computers and Electronics in Agriculture, Elsevier, Vol. 94, pp. 29-37, 2013.
[17] M Jhuria, A Kumar, R Borse,"Image Processing for Smart Farming: Detection of Disease and Fruit Grading", 2013 IEEE second International Conference on Image Information Processing(ICIIP), pp. 521-526, 2013.
[18] S R dubey and A S Jalal, "Detectuion and Classification of Apple Fruit Disease using Complete Local Binary Patterns", 2012 Third International Conference on Computer and Communication Technology, pp. 346-3351, 2012.
[19] D G Kim, T F Burks, J Qin and D M Bulanon, "Classification of Grape Fruit Peel Diseases using Color Texture Features", International Journal of Agriculture and Biological Engineering, pp. 41-50, 2009.
[20] J Blasco, N Aleixos and E Molt,"Machine Vision System for Automatic Quality Grading of Fruit", Biosystems Engineering, Elsevier, Vol. 85, Isuue. 4, pp. 415-423, 2003.
[21] V Leemans, H Magein and F Destain, "Defect Segemenation on Jonagold apples using colour vision and Bayesian calssification method", Computers and Ectronics in Agriculture, Elsevier, Vol. 23, Issue. 1, pp. 43-53.
[22] Z F Muhsin, A Rehman, A Altameem, A Saba and M Uddin, "Improved Quadtree Image Segmentation approach to region informmation", The Imaging Science Journal, Vol. 62, Issue.1, 2014.
[23] K S Raghunandan, B M Chethan Kumar, G Hemantha Kumar and C Sunil,"Convolution Neural Network Based Deep Features for Text Recognition in Multi-type Images", 2018 International Conference on Advance Computing, Communications and Informatics(ICACCI), Banglore, pp. 502-507, 2018.
[24] K N Ranjit, H K Chethan and C Naveen, "Identification and Classification of Fruit Diseases", International Journal of Engineering Research and Appliction, Vol. 6, Isuue. 7, 2016.