References
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- S. C. Nelson, “Banana bunchy top: Detailed signs and symptoms,” 2004. [Online]. Available: https://www.ctahr.hawaii.edu/bbtd/downloads/bbtv-details.pdf
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- “Godliver Owomugisha Implementation-BBW and BBS Diseases”. [Online]. Available: https://github.com/godliver/source-code-BBW-BBS (accessed on July 2019).
- 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
- 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
- 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
- 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
- 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