Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning Approach

[1] N. B. A. Mustafa et al., “Image processing of an agriculture produce: Determination of size and ripeness of a banana,” in 2008 International Symposium on Information Technology, Kuala Lumpur, Malaysia, Aug. 2008, pp. 1–7. https://doi.org/10.1109/ITSIM.2008.4631636 Search in Google Scholar

[2] M.J. Vipinadas and A. Thamizharasi, “Banana leaf disease identification technique,” International Journal of Advanced Engineering Research and Science (IJAERS), vol. 3, no. 6, pp. 120–124, Nov. 2016. https://doi.org/10.22161/ijaers/3.11.21 Search in Google Scholar

[3] H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik, and Z. ALRahamneh, “Fast and accurate detection and classification of plant diseases,” International Journal of Computer Applications, vol. 17, no. 1, pp. 31–38, Mar. 2011. https://doi.org/10.5120/2183-2754 Search in Google Scholar

[4] K. Suganya Devi, P. Srinivasan, and Sivaji Bandhopadhyay, “ H2K – A robust and optimum approach for detection and classification of groundnut leaf diseases,” Computers and Electronics in Agriculture, vol. 178, Nov. 2020, Art. no. 105749. https://doi.org/10.1016/j.compag.2020.105749 Search in Google Scholar

[5] D. Yirgou and J. F. Bradbury, “Bacterial wilt of enset (Ensete ventricosum) incited by Xanthomonas musacearum sp. n,” Phytopathology, vol. 58, pp. 111–112, 1968. https://www.musalit.org/seeMore.php?id=10016 Search in Google Scholar

[6] L. Tripathi, M. Mwangi, V. Aritua, W.K. Tushemereirwe, S. Abele, and R. Bandyopadhyay, “Xanthomonas wilt: a threat to banana production in East and Central Africa,” Plant Disease, vol. 93, no. 5, May 2009. https://doi.org/10.1094/PDIS-93-5-0440 Search in Google Scholar

[7] D. Surya Prabha and J. Satheesh Kumar, “Study on banana leaf disease identification using image processing methods,” International Journal of Research in Computer Science and Information Technology (IJRCSIT), vol. 2, no. 2(A), pp. 89–94, Mar. 2014. https://www.researchgate.net/publication/299486544_Study_on_Banana_Leaf_Disease_Identification_Using_Image_Processing_Methods Search in Google Scholar

[8] V. Devappa and T. C. Archith, “Wilt diseases of ornamental crops and their management,” in Wilt Diseases of Crops. Today and Tomorrow Printers and Publisher, New Delhi, India, 2019, pp. 141–164. https://www.researchgate.net/publication/331917069_Wilt_diseases_of_ornamental_crops_and_their_management Search in Google Scholar

[9] R. Dheepa and S. Paranjothi, “Transmission of cucumber mosaic virus (CMV) infecting banana by aphid and mechanical methods,” Emirates Journal of Food and Agriculture, vol. 22, no. 2, pp. 117–129, Oct. 2010. https://doi.org/10.9755/ejfa.v22i2.4899 Search in Google Scholar

[10] H. R. Almadhoun and S. S. Abu-Naser, “Banana knowledge based system diagnosis and treatment,” International Journal of Academic Pedagogical Research (IJAPR), vol. 2, no. 7, pp. 1–11, Jul. 2018. https://hal.science/hal-01847731v1/file/IJAPR180701.pdf Search in Google Scholar

[11] S. C. Nelson, “Banana bunchy top: Detailed signs and symptoms,” 2004. [Online]. Available: https://www.ctahr.hawaii.edu/bbtd/downloads/bbtv-details.pdf Search in Google Scholar

[12] S. Ganesan, H. Shankar Singh, S. Petikam, and D. Biswal, “Pathological status of Pyricularia angulata causing blast and pitting disease of banana in Eastern India,” Plant Pathol. J., vol. 33, no. 1, pp. 9–20, Feb. 2017. https://doi.org/10.5423/PPJ.OA.08.2016.0162 Search in Google Scholar

[13] S. M. Muturi, F. N. Wachira, L. S. Karanja, and L. K. Njeru, “The mode of transmission of banana streak virus by Paracoccus burnerae (Homiptera; Planococcidae) vector is non-circulative,” British Microbiology Research Journal, vol. 12, no. 6, pp. 1–10, 2016. https://doi.org/10.9734/BMRJ/2016/21574 Search in Google Scholar

[14] S. K. Das, Md. R. Mia, S. Roy, and Md. A. Rahman, “Mango leaf disease recognition using neural network and support vector machine,” Iran Journal of Computer Science, vol. 3, pp. 185–193, Apr. 2020. https://doi.org/10.1007/s42044-020-00057-z Search in Google Scholar

[15] A. E. Hassanien, T. Gaber, U. Mokhtar, and H. Hefny, „An improved moth flame optimization algorithm based on rough sets for tomato diseases detection,” Computers and Electronics in Agriculture, vol. 136, pp. 86–96, Apr. 2017. http://doi.org/10.1016/j.compag.2017.02.026 Search in Google Scholar

[16] Anjna, M. Sood, and P. K. Singh, “Hybrid system for detection and classification of plant disease using qualitative texture features analysis,” Procedia Computer Science, vol. 167, pp. 1056–1065, 2020. https://doi.org/10.1016/j.procs.2020.03.404 Search in Google Scholar

[17] P. Bedi and P. Gole, “Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network,” Artificial Intelli gence in Agriculture, vol. 5, pp. 90–101, 2021. https://doi.org/10.1016/j.aiia.2021.05.002 Search in Google Scholar

[18] M. Morgan, C. Blank, and R. Seetan, “Plant disease prediction using classification algorithms,” IAES International Journal of Artificial Intelligence (IJ -AI), vol. 10, no. 1, pp. 257-264, Mar. 2021. https://doi.org/10.11591/ijai.v10.i1.pp257-264 Search in Google Scholar

[19] B. M. Patil and V. Burkpalli, “A perspective view of cotton leaf image classification using machine learning algorithms using WEKA,” Advances in Human-Computer Interaction, 2021, Art. no. 9367778. https://doi.org/10.1155/2021/9367778 Search in Google Scholar

[20] “Godliver Owomugisha Implementation-BBW and BBS Diseases”. [Online]. Available: https://github.com/godliver/source-code-BBW-BBS (accessed on July 2019). Search in Google Scholar

[21] P. K. Sethy, N. K. Barpanda, A. K. Rath, and S. K. Behera, “Image processing techniques for diagnosing rice plant disease: A survey,” Procedia Computer Science, vol. 167, pp. 516–530, 2020. https://doi.org/10.1016/j.procs.2020.03.308 Search in Google Scholar

[22] V. N. T. Le, B. Apopei, and K. Alameh, “Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods,” Information Processing in Agriculture, vol. 6, no. 1, pp. 116–131, Mar. 2019. https://doi.org/10.1016/j.inpa.2018.08.002 Search in Google Scholar

[23] S. Iniyan, R. Jebakumar, P. Mangalraj, M. Mohit, and A. Nanda, “Plant disease identification and detection using Support Vector Machines and Artificial Neural Networks,” in Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, S. Dash, C. Lakshmi, S. Das, and B. Panigrahi, Eds., vol. 1056, Springer, Singapore, Feb. 2020, pp. 15–27. https://doi.org/10.1007/978-981-15-0199-9_2 Search in Google Scholar

[24] L. J. Muhammad, E. A. Algehyne, S. S. Usman, A. Ahmad, C. Chakraborty, and I. A. Mohammed, “Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset,” SN Computer Science, vol. 2, 2021, Art. no. 11. https://doi.org/10.1007/s42979-020-00394-7 Search in Google Scholar

[25] G. M. James and S. C. Punitha, “Tomato disease classification using ensemble learning approach,” IJRET: International Journal of Research in Engineering and Technology, vol. 5, no. 10, pp. 104–108, Oct. 2016. https://ijret.org/volumes/2016v05/i10/IJRET20160510019.pdf Search in Google Scholar