AUTOMATIC FRUIT DISEASE DETECTION: A REVIEW

Authors

  • Neelofar Sohi Assistant Professor, Department of Computer Science & Engineering, Punjabi University, Patiala, Punjab, India. Author

DOI:

https://doi.org/10.34218/IJCET_16_04_001

Keywords:

Fruit Disease Detection, Image Processing, Fruit Disease

Abstract

Automatic and early detection of diseases in fruit is essential for identifying the signs of disease and prevent loss in production and quality of fruit. Various studies have been conducted to propose efficient techniques for fruit disease detection. In this study, problem of fruit disease detection is formulated giving its background and motivation and also general detection procedure is discussed.

References

Behera, S.K. & Rath, A.K. & Sethy, P.K. Automatic Fruits Identification and Disease Analysis using Machine Learning Techniques. International Journal of Innovative Technology and Exploring Engineering (IJITEE). 2019, Vol. 8: 6S2.

Behera, S.K. & Jena, L. & Rath, A.K. & Sethy, P.K. Disease Classification and Grading of Orange Using Machine Learning and Fuzzy Logic. International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2018, P. 0678-0682.

Fiona, R. & Thomas, S. & J, I.M. & Hannah, B. Identification Of Ripe And Unripe Citrus Fruits Using ARTIFICIAL NEURAL NETWORK. 2019, International Conference on Physics and Photonics Processes in Nano Sciences.

Sunny, S. & Indra Gandhi, M.P. An Efficient Citrus Canker Detection Method based on Contrast Limited Adaptive Histogram Equalization Enhancement. International Journal of Applied Engineering Research. 2018, Vol.13:1.

Komal, K. & Sonia. Quality Assessment Of Orange Fruit Using Svm Classifier And Gray Level Co-Occurrence Matrix Algorithm. International Journal of Scientific & Technology Research. 2019, Vol.8:11.

Poorani, S. Intelligent Fruit Detection System with Image Processing Techniques. International Journal of Scientific Research and Review. 2019, Vol.8:5.

Komal, K. & Sonia. GLCM Algorithm and SVM Classification Method for Orange Fruit Quality Assessment. International Journal of Engineering Research & Technology (IJERT). 2019, Vol. 8:9.

L, R.S. & N, A. & V, D. & T, K. Fruit Classification System Using Computer Vision: A Review. International Journal of Trend in Research and Development. 2018, Vol. 5: 1.

Banni, R. & Baligar, P.S. & Gorabal, J. CITRUS LEAF DISEASE DETECTION USING IMAGE PROCESSING APPROACHES. International Journal of Pure and Applied Mathematics. 2018, Vol.120:6, P. 727-735.

Kumar, C.S. & Kamarasan, M. An Effective Classification of Citrus Fruits Diseases using Adaptive Gamma Correction with Deep Learning Model. International Journal of Engineering and Advanced Technology (IJEAT). 2019, Vol. 9: 2.

Vyas, A.M. & Talati, B. & Naik, S. Colour Feature Extraction Techniques of Fruits: A Survey. International Journal of Computer Applications, 2013, Vol.83: 15.

Gawande, A.P. & Dhande, S.S. Implementation of fruit Grading System by Image Processing and Data Classifier- A Review. International Journal of Engineering Research and General Science, 2014, Vol.2: 6.

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Published

2025-07-02

How to Cite

Neelofar Sohi. (2025). AUTOMATIC FRUIT DISEASE DETECTION: A REVIEW. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY, 16(4), 1-11. https://doi.org/10.34218/IJCET_16_04_001