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Mushrooms are a rich source of antioxidants and nutritional values. Edible mushrooms, however, are susceptible to various diseases such as dry bubble, wet bubble, cobweb, bacterial blotches, and mites. Farmers face significant production losses due to these diseases affecting mushrooms. The manual detection of these diseases relies on expertise, knowledge of diseases, and human effort. Therefore, there is a need for computer-aided methods, which serve as optimal substitutes for detecting and segmenting diseases. In this paper, we propose a semantic segmentation approach based on the Random Forest machine learning technique for the detection and segmentation of mushroom diseases. Our focus lies in extracting a combination of different features, including Gabor, Bouda, Kayyali, Gaussian, Canny edge, Roberts, Sobel, Scharr, Prewitt, Median, and Variance. We employ constant mean-variance thresholding and the Pearson correlation coefficient to extract significant features, aiming to enhance computational speed and reduce complexity in training the Random Forest classifier. Our results indicate that semantic segmentation based on Random Forest outperforms other methods such as Support Vector Machine (SVM), Naïve Bayes, K-means, and Region of Interest in terms of accuracy. Additionally, it exhibits superior precision, recall, and F1 score compared to SVM. It is worth noting that deep learning-based semantic segmentation methods were not considered due to the limited availability of diseased mushroom images.
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S. Sharma, S. Kumar, and V. P. Sharma. Diseases and Competitor Moulds of Mushrooms and their Management. Technical Bulletin, National Research Centre for Mushroom (ICAR), India, 2007. https://dmrsolan.icar.gov.in/Disease___Competitor_Moulds__Dr._S.R._Sharma_.pdf.
J. T. Fletcher and R. H. Gaze. Mushroom pest and disease control. A colour handbook. CRC Press, London, United Kingdom, 2007. https://doi.org/10.1201/b15139 (Crossref)
E. Daniel, G. Julian, G. Helen, and B. Kerry. Viral agents causing brown cap mushroom disease of Agaricus bisporus. Applied and Environmental Microbiology Journal, 81(20):7125-7134, 2015. https://doi.org/10.1128/AEM.01093-15. (Crossref)
I. O. Elibuyuk and H. Bostan. Detection of a virus disease on white button mushroom (Agaricus bisporus) in Ankara, Turkey. International Journal of Agriculture and Biology, 12(4):597-600, 2010. http://www.fspublishers.org/published_papers/86156_..pdf.
J. Platt. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Microsoft Research Technical Report No. MSR-TR-98-14, Apr 1998. https://www.microsoft.com/en-us/research/publication/sequential-minimal-optimization-a-fast-algorithm-for-training-support-vector-machines/.
D. R. Chowdhury and S. Ojha. An empirical study on mushroom disease diagnosis: A data mining approach. International Research Journal of Engineering and Technology, 4(1):529-534, 2017. https://www.irjet.net/archives/V4/i1/IRJET-V4I190.pdf.
P. Goyal, E. Din, and D. Kapoor. A software for diagnosis and management of diseases and pests in white button mushrooms. International Journal of Advanced Research in Computer and Communication Engineering, 08(2):3136-3139, 2013.
A. Jensen, P. Boll, T. I., and B. Pathak. Pl@nteInfo® - a web based system for personalized decision support in crop management. Computers and Electronics in Agriculture, 25:271-293, 2000. https://doi.org/10.1016/S0168-1699(99)00074-5. (Crossref)
M. Y Minirah, M. Rozlini, and M. Y. Siti An expert system development: Its application on diagnosing oyster mushroom diseases. In: 13th International Conference on Control, Automation and systems, pp. 20-23. Gwangju, Korea (South), 2013. https://doi.org/10.1109/ICCAS.2013.6703917. (Crossref)
M. Y Minirah, M. Rozlini, and M. Y. Siti Design and rules development of expert system for diagnosing oyster mushroom diseases. In: Proc. of the Computer and Information Science (ICCIS), pp. 286-289, 2012. https://doi.org/10.1109/ICCISci.2012.6297255. (Crossref)
T. Zuva, O. O. Olugbara, S. O. Ojo, and S. M. Ngwira. Image segmentation, available techniques, developments and open issues. Canadian Journal on Image Processing and Computer Vision, 2(3):20-29, 2011. https://www.researchgate.net/publication/264854010_Image_Segmentation_Available_Techniques_Developments_and_Open_Issues.
N. Jothiaruna, K. J. A. Sundar, and B. Karthikeyan. segmentation method for disease spot images incorporating chrominance in comprehensive color feature and region growing. Computers and Electronics in Agriculture, 165:104934, 2019. https://doi.org/10.1016/j.compag.2019.104934. (Crossref)
S. Siddesha and S. K. Niranjan. Detection of affected regions of disease arecanut using k-means and Otsu method. International Journal of Scientific and Technology Research, 9(2):3404-3408, 2020. http://www.ijstr.org/paper-references.php?ref=IJSTR-0120-30236.
T. N. Tete and S. Kamlu. Plant disease detection using different algorithms. In: Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering-ACSIS, vol. 10, pp. 103–106, 2017. https://annals-csis.org/Volume_10/drp/24.html. (Crossref)
A. S. M. Shafi, B. Rahman, and M. M. Rahman. Fruit disease recognition and automatic classification using msvm with multiple features. International Journal of Computer Applications, 181(10):104934, 2018. https://doi.org/10.5120/ijca2018916773. (Crossref)
Y. R. Kumar and V. Chandrasekhar and A. K. Rao, An automatic multi-threshold image processing technique mushroom disease segmentation, International Journal of Current engineering research, 7(6):110-115, 2020 http://troindia.in/journal/ijcesr/vol7iss6/110-115.pdf.
T. K. Ho. Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, p. 278–282, Aug 1995. https://doi.org/10.1109/ICDAR.1995.598994. (Crossref)
L. Breiman. Random forests. Machine Learning, 45(1):5–32, 2001. https://doi.org/10.1023/A:1010933404324. (Crossref)
J. Shotton, M. Johnson, and R. Cipolla. Semantic texton forests for image categorization and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 2008, pp. 1-8. https://doi.org/10.1109/CVPR.2008.4587503. (Crossref)
F. Schroff, A. Criminisi, and A. Zisserman. Object class segmentation using random forests. In: Proceedings of the British Machine Vision Conference, p. 54.1-54.10, 2008. https://bmva-archive.org.uk/bmvc/2008/papers/207.html. (Crossref)
A. Criminisi and J. Shotton. Decision Forests for Computer Vision and Medical Image Analysis. Springer, London, 2013. https://doi.org/10.1007/978-1-4471-4929-3. (Crossref)
B. Kang, Y. Lee, and T. Q. Nguyen. Depth-adaptive deep neural network for semantic segmentation. IEEE Transactions on Multimedia, 20(9):2478-2490, 2018. https://doi.org/10.1109/TMM.2018.2798282. (Crossref)
L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4):834–848, 2018. https://doi.org/10.1109/TPAMI.2017.2699184. (Crossref)
H. Zhao, X. Qi, X. Shen, J. Shi, and J. Jia. ICNet for real-time semantic segmentation on high-resolution images. In: Computer Vision - Proc. ECCV 2018, Lecture Notes in Computer Science, vol. 11207, p. 418–434. Springer International Publishing, 2018. https://doi.org/10.1007/978-3-030-01219-9_25. (Crossref)
Z. Huang, X. Wang, Y. Wei, L. Huang, et al. CCNet: Criss-Cross attention for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(06):6896-6908, 2023. https://doi.org/10.1109/TPAMI.2020.3007032. (Crossref)
E. Shelhamer, J. Long, and T. Darrell. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4):640–651, 2017. https://doi.org/10.1109/TPAMI.2016.2572683. (Crossref)
V. Badrinarayanan, A. Kendall, and R. Cipolla. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12):2481-2495, 2017. https://doi.org/10.1109/TPAMI.2016.2644615. (Crossref)
G. Lin, A. Milan, C. Shen, and I. Reid. RefineNet: Multi-path refinement networks for high-resolution semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, Jul 2017, pp. 5168-5177. https://doi.org/10.1109/CVPR.2017.549. (Crossref)
E. Kawalec-Latała. Edge detection on images of pseudo impedance section supported by context and adaptive transformation model images. Studia Geotechnica et Mechanica 36(1):29-36, Mar 2014. https://doi.org/10.2478/sgem-2014-0004. (Crossref)
B. Bouda, L. Masmoudi, and D. Aboutajdine. Cvvefm: Cubical voxels and virtual electric field model for edge detection in color images. Signal Processing, 88(4):905-915, 2008. https://doi.org/10.1016/j.sigpro.2007.10.006. (Crossref)
P. Kuppusamy, M. M. Basha, and C.-L. Hung. Retinal blood vessel segmentation using random forest with gabor and canny edge features. In: International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN), Villupuram, India, Mar 2022, pp. 1-4. https://doi.org/10.1109/ICSTSN53084.2022.9761339. (Crossref)
A. Paul, D. P. Mukherjee, P. Das, A. Gangopadhyay, A. R. Chintha and S. Kundu. Improved random forest for classification. IEEE Transactions on Image Processing, 27(8):4012-4024, 2018. https://doi.org/10.1109/TIP.2018.2834830. (Crossref)
Forest Mushroom Research Center (FMRC), Seoul, Korea (South). https://www.fmrc.or.kr.
Imagenet. https://www.image-net.org.
Mushroom World. http://www.mushroom.world/.