Detection of Fruit Diseases using Image Processing Techniques: A Review

International Journal of Electronics and Communication Engineering
© 2022 by SSRG - IJECE Journal
Volume 9 Issue 4
Year of Publication : 2022
Authors : Fouqiya Badar, Ayesha Naaz
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Citation:
MLA Style:

Fouqiya Badar, and Ayesha Naaz. "Detection of Fruit Diseases using Image Processing  Techniques: A Review" SSRG International Journal of Electronics and Communication Engineering, vol. 9, no. 4, Apr. 2022, pp. 10-14.  Crossref, https://doi.org/10.14445/23488549/IJECE-V9I4P102

APA Style:

Fouqiya Badar, & Ayesha Naaz. (2022). Detection of Fruit Diseases using Image Processing  Techniques: A Review. SSRG International Journal of Electronics and Communication Engineering, 9(4), 10-14. https://doi.org/10.14445/23488549/IJECE-V9I4P102

Abstract:

Production of fruit crops is a very necessary part of India. It is important in terms of economy and nutritional value, and it feeds not only humans but other living beings also. the trees provide shelter to living beings. Also, it absorbs many harmful gases and gives us pure and free oxygen. Fruits are a lot better than junk food, which has become the cause of many diseases today. Keeping these aspects in mind, the fruits must be prevented from getting infected with diseases at an early stage itself. This paper reviews the various image processing methods that can be used to detect diseases in fruits based on symptoms. Then the research gaps for every paper have been highlighted. This paper aims to help other researchers get to know the various methods that can be used in fruit disease detection.

References:

[1] Abhijeet V. Jamdar and A. P. Patil, Detection and Classification of Apple Fruit Diseases Using K-Means Clustering and Learning Vector Quantization Neural Network, International Journal of Scientific Development and Research (IJSDR). 2(6) (2017) 423-429.
[2] Abhijeet V. Jamdar and A. P. Patil, Apple Fruit Disease Detection Using Image Segmentation Algorithm, International Journal for Research Trends and Innovation. 2(6) (2017) 221-225.
[3] H. Ali A, MIMI Lali, MZMZ. Nawaz, M. Sharif, and BABA. Saleem, Symptom-Based Automated Detection of Citrus Diseases Using Color Histogram and Textural Descriptors, Computers, and Electronics in Agriculture. 138 (2017) 92-104.
[4] Esmael Hamuda, Brian Mc Ginley, Martin Glavin, and Edward Jones, Improved Image Processing-Based Crop Detection Using Kalman Filtering and the Hungarian Algorithm, Computers, and Electronics in Agriculture. 148 (2018) 37-44.
[5] Mohamed Kerkech, Adel Hafiane, Raphael Canals, Deep Leaning Approach With Colorimetric Spaces and Vegetation Indices for Vine Diseases Detection in UAV Images, Computers, and Electronics in Agriculture. 155 (2018) 237-243.
[6] PLPL. Chithra, M. Henila, Fruits Classification Using Image Processing Techniques, International Journal of Computer Sciences and Engineering. 7(7) (2019).
[7] Suma V, R Among Shetty, Rishab F Tated, Sunku Rohan, and Triveni S Pujar, CNN-Based Leaf Disease Identification and Remedy Recommendation System, International Conference on Electronics Communication and Aerospace Technology, (2019).
[8] Hardik Patel, Rashmin Prajapati, and Milin Patel, Detection of Quality in Orange Fruit Image Using SVM Classifier, Proceedings of the Third International Conference on Trends in Electronics and Informatics (ICOEI). (2019) 74-78.
[9] S. Hernandez and J. L. Lopez, Uncertainty Quantification for Plant Disease Detection Using Bayesian Deep Learning, Applied Soft Computing. 96 (2020).
[10] Murk Chohan, Adil Khan, Rozina Chohan, Saif Hassan Katpar, and Muhammad Saleem Mahar, Plant Disease Detection Using Deep Learning, International Journal of Recent Technology and Engineering (IJRTE).9(1) (2020) 909-914.
[11] Vinay Kukreja and Poonam Dhiman, A Deep Neural Network-Based Disease Detection Scheme for Citrus Fruits, Proceedings of the International Conference on Smart Electronics and Communication (ICOSEC). (2020) 97 – 101.
[12] Rahamathunnisa U, Nallakaruppan M. K, Anith. A and K. S. Sendhil Kumar, Vegetable Disease Detection Using K-Means Clustering and SVM, 6th International Conference on Advanced Computing and Communication Systems(ICACCS). (2020) 1308-1311.
[13] Malathy. S, Karthiga. R. R, Swetha. K, Preethi. G, Disease Detection in Fruits Using Image Processing, Proceedings of the Sixth International Conference on Inventive Computation Technologies (ICICT). (2021) 747 - 752.
[14] Arunabha M. Roy, and Jayabrata Bhaduri, A Deep Learning Enabled Multi-Class Plant Disease Detection Model Based on Computer Vision, AI. 2(3) (2021) 413-428.
[15] Pallavi U. Patil, Sudhir B. Lande, Vinay J. Nagalkar, Sonal B. Nikam and G. C. Wakchaure, Grading and Sorting Technique of Dragon Fruits Using Machine Learning Algorithms, Journal of Agriculture and Food Research. 4 (2021) 100-118.
[16] Raju Hosakoti, Soma Pavan Kumar, Padmaja Jain, Disease Detection in Fruits Using Deep Learning, Journal of the University of Shanghai for Science and Technology. 23(7) (2021).
[17] Muammer Turkoglu, Berrin Yanikoglu and Davut Hanbay, Plant Disease Net: Convolutional Neural Network Ensemble for Plant Disease and Pest Detection, Signal, Image and Video Processing. 16 (2022) 1-9

Key Words:

Image Processing, k-means clustering, Object tracking, Deep learning, Disease