Computer vision based analysis and detection of defects in fruits causes due to nutrients deficiency

Presently, the fruit industry requires a fast and efficient method for classification and recognition of the quality of fruits in bulk processing. Fruit recognition based on computer vision is quite challenging as it is based on the intensity, size, contour, and texture features extraction from fruits along with their suitable classifier selection. In this paper, the pixels containing the defected regions are segmented and their features are extracted. Further, a support vector machine (SVM) classifier is used to identify the defects and recognizes the cause with its stage. During the process of classification, fruits are categorized into two groups, defected and no-defect. The sample image observed defected further classified into three categories as the first, second and final stage of fruit defect. The sample testing at an early stage helps one to further proceed with the production or halt based on the outcome of a computer vision-based recognition system.

[1]  L. Agilandeeswari,et al.  AUTOMATIC GRADING SYSTEM FOR MANGOES USING MULTICLASS SVM CLASSIFIER , 2017 .

[2]  Jayme Garcia Arnal Barbedo,et al.  Digital image processing techniques for detecting, quantifying and classifying plant diseases. , 2013 .

[3]  Jayme Garcia Arnal Barbedo,et al.  Plant disease identification from individual lesions and spots using deep learning , 2019, Biosystems Engineering.

[4]  Poshit Raj Gokul,et al.  Estimation of volume and maturity of sweet lime fruit using image processing algorithm , 2015, 2015 International Conference on Communications and Signal Processing (ICCSP).

[5]  Terry M. Peters,et al.  Interactive Hierarchical-Flow Segmentation of Scar Tissue From Late-Enhancement Cardiac MR Images , 2014, IEEE Transactions on Medical Imaging.

[6]  Mahdi Kadivar,et al.  Effect of dietary flaxseed oil level on the growth performance and fatty acid composition of fingerlings of rainbow trout, Oncorhynchus mykiss , 2013, SpringerPlus.

[7]  Jayme Garcia Arnal Barbedo,et al.  Factors influencing the use of deep learning for plant disease recognition , 2018, Biosystems Engineering.

[8]  Henrik Skov Midtiby,et al.  Plant species classification using deep convolutional neural network , 2016 .

[9]  Hans-Peter Kriegel,et al.  The (black) art of runtime evaluation: Are we comparing algorithms or implementations? , 2017, Knowledge and Information Systems.

[10]  Ni Made Satvika Iswari,et al.  Fruitylicious: Mobile application for fruit ripeness determination based on fruit image , 2017, 2017 10th International Conference on Human System Interactions (HSI).

[11]  Suchitra A. Khoje,et al.  Automated Skin Defect Identification System for Fruit Grading Based on Discrete Curvelet Transform , 2013 .

[12]  In Seop Na,et al.  Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images , 2019, Biosystems Engineering.

[13]  Yudong Zhang,et al.  Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine , 2012, Sensors.

[14]  Giovanni Attolico,et al.  Non-destructive and contactless quality evaluation of table grapes by a computer vision system , 2019, Comput. Electron. Agric..

[15]  Selman UluiŞik,et al.  Image processing based machine vision system for tomato volume estimation , 2018, 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT).

[16]  Anitha Raghavendra,et al.  A Survey on Internal Defect Detection in Fruits by Non-Intrusive Methods , 2016 .

[17]  Jidong Lv,et al.  Acquisition of fruit region in green apple image based on the combination of segmented regions , 2017, 2017 2nd International Conference on Image, Vision and Computing (ICIVC).

[18]  Izadora Binti Mustaffa,et al.  Identification of fruit size and maturity through fruit images using OpenCV-Python and Rasberry Pi , 2017, 2017 International Conference on Robotics, Automation and Sciences (ICORAS).

[19]  Megha P. Arakeri,et al.  Computer Vision Based Fruit Grading System for Quality Evaluation of Tomato in Agriculture industry , 2016 .

[20]  Kendra Schwartz,et al.  Characterizing inflammatory breast cancer among Arab Americans in the California, Detroit and New Jersey Surveillance, Epidemiology and End Results (SEER) registries (1988–2008) , 2012, SpringerPlus.

[21]  Mrityunjaya V. Latte,et al.  Rule based approach to determine nutrient deficiency in paddy leaf images. , 2017 .