A Review about Computational Methods Dedicated to Apple Fruit Diseases Detection

Main Article Content

R. Manavalan


Agriculture is the most important sector that influences the country's economic growth. Fruits and vegetables can be an essential part of regular human diets. The fruits are rich in vitamins and minerals that help to stay healthy. Apple is the most significant fruit with an extensive list of antioxidants and vital health nutrients. Apple production is severely affected by pests and various diseases. So, the farmers severely suffer in economic losses. Hence, early detection of various diseases in apple fruit and pest control techniques is necessary to increase production. The identification of the apple leaves diseases by naked eyes leads to inaccurate control measurements of pesticides. Hence, automatic identification and early diagnosis of apple fruit diseases are necessary to increase production and quality. This article provides an overview of the various image processing techniques and machine learning approaches used to quickly obtain and examine apple fruit diseases.

Apple, plant diseases, computational methods, SVM, CNN, neural network

Article Details

How to Cite
Manavalan, R. (2021). A Review about Computational Methods Dedicated to Apple Fruit Diseases Detection. Asian Research Journal of Current Science, 3(1), 119-130. Retrieved from https://globalpresshub.com/index.php/ARJOCS/article/view/1226
Mini Review Papers


Boeing H, Bechthold A, Bub A, Ellinger S, Haller D, Kroke A, et al. Critical review: vegetables and fruit in the prevention of chronic diseases. Eur J Nutr [Internet]. 2012/06/09. 2012;51(6):637–63.


Pujari J, Yakkundimath R. Grading and classification of anthracnose fungal disease of fruits based on statistical texture features. In; 2013.

Agricoop.Nic.In. Horticulture Agricoop. [Internet].


Mhapne NV, HarishS V, Kini AS, NarendraV G. A comparative study to find an effective image segmentation technique using clustering to obtain the defective portion of an apple. 2019 Int Conf Autom Comput Technol Manag. 2019;304–9.

Kidskonnect. Apple facts, worksheets and health benefits information for kids. [Internet]; 2017.


You.Com NA. 7 surprising apple fruit nutrition facts and health benefits.


Department of Economic Development. Apple Scab [Internet].


ag.purdue.edu. Apple Diseases [Internet].

Available:https://ag.purdue.edu/ipia/Documents/afghanistan/SPS Documents/Apple-Diseases-Handout-English.pdf

Gardening. What is apple blotch fungus: Tips for treating apple tree fungus.


Ohioline.Osu.Edu. Bitter rot of apple [Internet].


Li Q, Wang M, Gu W. Computer vision based system for apple surface defect detection. Comput Electron Agric. 2002;36:215–23.

kavdır I, Guyer DE. Comparison of artificial neural networks and statistical classifiers in apple sorting using textural features. Biosyst Eng. 2004;89:331–44.

Bennedsen B, Peterson DL, Tabb A. Identifying defects in images of rotating apples. Comput Electron Agric. 2005;48.

Tiger B, Verma T. Identification and classification of normal and infected apples using neural network. In; 2013.

Bhatt AK, Pant D. Automatic apple grading model development based on back propagation neural network and machine vision, and its performance evaluation. AI Soc [Internet]. 2015;30(1):45–56.


Ballester P, Corrêa U, Birck M, Araujo R. Assessing the performance of convolutional neural networks on classifying disorders in apple tree leaves. 2017;31–38.

Chouhan S, Koul A, Singh DU, Jain S. Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards Plant Pathology. IEEE Access. 2018;PP:1.

Wang G, Sun Y, Wang J. Automatic image-based plant disease severity estimation using deep learning. Comput Intell Neurosci. 2017;2017:1–8.

Tete TN, Kamlu S. Detection of plant disease using threshold, k-mean cluster and ann algorithm. 2017 2nd Int Conf Converg Technol. 2017;523–6.

Bin L, Zhang Y, He D, Li Y. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry (Basel). 2017;10:11.

Al-Shawwa Mohammed. Apple fruits classification using deep learning. Int J Acad Eng Res; 2019.

Dandawate Y. An automated approach for classification of plant diseases towards development of futuristic decision support system in indian perspective; 2015.

Agarwal M, Kaliyar R, Singal G, Gupta S. FCNN-LDA: A faster convolution neural network model for leaf disease identification on apple’s leaf dataset. 2019;246–251.

Sathya V. Fruit and leaves disease prediction using deep learning algorithm. Int Res J Multidiscip Technovation. 2019;1(1):8–16.

Bi C, Wang J, Duan Y, Fu B, Kang JR, Shi Y. Mobile net based apple leaf diseases identification. Mob Networks Appl; 2020

Yan Q, Yang B, Wang W, Wang B, Chen P, Zhang J. Apple leaf diseases recognition based on an improved convolutional neural network. Sensors (Switzerland). 2020;20(12):1–14.

Zhong Y, Zhao M. Research on deep learning in apple leaf disease recognition. Comput electron agric [Internet]. 2020;168:105146.


Chao X, Sun G, Zhao H, Li M, He D. Identification of apple tree leaf diseases based on deep learning models. Symmetry (Basel). 2020;12(7):1–17.

Li X, Rai L. Apple leaf disease identification and classification using resnet models. In: 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT). 2020;738–42.

Albayati JSH, Üstündağ BB. Evolutionary feature optimization for plant leaf disease detection by deep neural networks. Int J Comput Intell Syst. 2020;13(1):12–23.

Sardoğan M, Özen Y, Tuncer A. Faster R-CNN kullanarak elma yapraği hastaliklarinin tespiti. Düzce Üniversitesi Bilim ve Teknol Derg. 2020;8:1110–7.

Tahir M Bin, Khan MA, Javed K, Kadry S, Zhang Y-D, Akram T, et al. Recognition of apple leaf diseases using deep learning and variances-controlled features reduction. Microprocess Microsyst [Internet]. 2021;104027.


Srinidhi VV, Sahay A, Deeba K. Plant pathology disease detection in apple leaves using deep convolutional neural networks: Apple leaves disease detection using efficientnet and densenet. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). 2021;1119–27.

Khan AI, Quadri SMK, Banday S. Deep learning for apple diseases: Classification and identification. Int J Comput Intell Stud [Internet]. 2021;10(1):1–12.


Acosta A, Ochoa A, Rodriguez-Eparza E, Oliva D, Juan AA, Pajares G. Classification system to detect diseases in apples by using a convolutional neural network BT - technological and industrial applications associated with industry 4.0. In: Ochoa-Zezzatti A, Oliva D, Hassanien AE, editors. Cham: Springer International Publishing. 2022;331–40.


Chakraborty S, Paul S, Rahat-uz-Zaman M. Prediction of apple leaf diseases using multiclass support vector machine. In: 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). 2021;147–51.

Singh S, Gupta S, Tanta A, Gupta R. Extraction of multiple diseases in apple leaf using machine learning. Int J Image Graph [Internet]. 2021;2140009.


Huang W, Zhang C, Zhang B. Identifying apple surface defects based on gabor features and SVM using machine vision. IFIP Advances in Information and Communication Technology. 2011;370:343–350.

Dubey SR, Jalal A. Detection and classification of apple fruit diseases using complete local binary patterns. Proceedings of the 2012 3rd International Conference on Computer and Communication Technology, ICCCT 2012; 2012.

Zhang B, Huang W, Gong L, Li J, Zhao C, Liu C, et al. Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier. J Food Eng. 2014;146.

Dubey SR, Jalal AS. Apple disease classification using color, texture and shape features from images. Signal, Image Video Process [Internet]. 2016;10(5):819–26.


Jolly P, Raman S. Analyzing surface defects in apples using gabor features. 2016;178–185.

Sujatha P, Sandhya J, Chaitanya J, Subashini R. Enhancement of segmentation and feature fusion for apple disease classification. 2018;175–181.

Ganesan S. Classification of leaf diseases in apple using support vector machine. Int J Adv Res Comput Sci. 2018;9:261–5.

Agarwal A, Sarkar A, Dubey A. Computer vision-based fruit disease detection and classification: Proceedings of ICSICCS-2018. In. 2019;105–15.

Ayyub S, Manjramkar A. Fruit disease classification and identification using image processing. 2019;754–758.

Singh S, Singh N. Machine learning-based classification of good and rotten apple: Select proceedings of IC3E 2018. In. 2019;377–86.

Wijekoon C, Goodwin PH, Hsiang T. Quantifying fungal infection of plant leaves by digital image analysis using Scion Image software. J Microbiol Methods. 2008;74:94–101.

Puchalski C. Image analysis for apple defect detection. Teka Kom Mot Energ Roln – OL PAN. 2008;8:197–205.

Moradi G, Shamsi M, Sedaaghi M, Moradi S. Apple defect detection using statistical histogram based fuzzy C-means algorithm. 2011 7th Iran Conf Mach Vis Image Process MVIP 2011 – Proc; 2011.

Farahani L, Etebarian H, Mohseni H, Sahebani N, Aminian H. Computer-based recognition of severity of apple blue mould using RGB components. Int Res J Appl Basic Sci. 2012;3:39–45.

Samajpati B, Degadwala S. Hybrid approach for apple fruit diseases detection and classification using random forest classifier. 2016;1015–1019.

Jamdar AV, Patil AP. Apple fruit disease detection using image segmentation algorithm. In; 2017.

Zhang S, Wu X, You Z, Zhang L. Leaf image based cucumber disease recognition using sparse representation classification. Comput Electron Agric. 2017;134:135–41.

Pl C. Defect identification in the fruit apple using k-means color image segmentation algorithm. Int J Adv Res Comput Sci. 2017;8:381–8.

Singh S, Gupta S. A novel algorithm for segmentation of diseased apple leaf images. J Adv Res Dyn Control Syst. 2018;6.

Goel L, Mustaq F, Tak C. Hybrid swarm intelligence algorithm for detection of health of an apple: ICCI-2017. In: Advances in Intelligent Systems and Computing. 2019;333–42.

Tiwari R, Chahande M. Apple fruit disease detection and classification using K-means clustering method BT - advances in intelligent computing and communication. In: Das S, Mohanty MN, editors. Singapore: Springer Singapore. 2021;71–84.