Superlative Approach for Plant Disease Identification with Enhanced CSA Algorithm
DOI:
https://doi.org/10.13052/jmm1550-4646.18414Keywords:
Disease detection, Cuckoo search algorithm, K nearest neighbour algorithm , classification.Abstract
Disease detection in plant leaf helps farmers to protect the plant from diseases at its early stage. The most important problems are determining and anticipating plant diseases, which may be addressed for increasing output. In this research, Rider Cuckoo Search algorithm is improved with K nearest neighbour algorithm is used to classify the diseased leaf. Initially the Gaussian filtering is used in pre-processing to remove the noises in image. Following getting pre-processed image, it is exposed to segmentation step, which uses piecewise fuzzy C-means (piFCM) clustering to acquire the segments. Segmentation involves the feature extraction process which has information gain, histogram of oriented gradients (HOG), and entropy. Finally plant Disease is classified using the KNN algorithm. This proposed algorithm is implemented with the images of the plant village dataset. The proposed research work is evaluated using certain parameters like accuracy of the disease detection, Error of the algorithm, Speed of the algorithm, and time for classifying the disease. The Proposed algorithm outperformed with the values of 99.32% accuracy, 0.68% error, 2400 obs/sec speed, and time taken is 0.57743 sec respectively when compared with the existing algorithms like Hybrid SIFT algorithm, Hybrid K-means Fuzzy logic SVM algorithm, and Cuckoo Search Algorithm.
Downloads
References
W. Bao, J. Zhao, G. Hu, D. Zhang, L. Huang, and D. Liang, “Identification of wheat leaf diseases and their severity based on elliptical-maximum margin criterion metric learning,” Sustain. Comput. Informatics Syst., vol. 30, p. 100526, Jun. 2021, doi: 10.1016/j.suscom.2021.100526.
A. Y. Dong, Z. Wang, J. J. Huang, B. A. Song, and G. F. Hao, “Bioinformatic tools support decision-making in plant disease management,” Trends in Plant Science. Elsevier Ltd, May 24, 2021, doi: 10.1016/j.tplants.2021.05.001.
A. Abade, P. A. Ferreira, and F. de Barros Vidal, “Plant diseases recognition on images using convolutional neural networks: A systematic review,” Computers and Electronics in Agriculture, vol. 185. Elsevier B.V., p. 106125, Jun. 01, 2021, doi: 10.1016/j.compag.2021.106125.
D. Shah, V. Trivedi, V. Sheth, A. Shah, and U. Chauhan, “ResTS: Residual Deep interpretable architecture for plant disease detection,” Inf. Process. Agric., Jun. 2021, doi: 10.1016/j.inpa.2021.06.001.
V. Bischoff, K. Farias, J. P. Menzen, and G. Pessin, “Technological support for detection and prediction of plant diseases: A systematic mapping study,” Computers and Electronics in Agriculture, vol. 181. Elsevier B.V., p. 105922, Feb. 01, 2021, doi: 10.1016/j.compag.2020.105922.
G. B.V. and U. D. G., “Identifying and classifying plant disease using resilient LF-CNN,” Ecol. Inform., vol. 63, p. 101283, Jul. 2021, doi: 10.1016/j.ecoinf.2021.101283.
K. Thaiyalnayaki and C. Joseph, “Classification of plant disease using SVM and deep learning,” Mater. Today Proc., May 2021, doi: 10.1016/j.matpr.2021.05.029.
R. Sujatha, J. M. Chatterjee, N. Z. Jhanjhi, and S. N. Brohi, “Performance of deep learning vs machine learning in plant leaf disease detection,” Microprocess. Microsyst., vol. 80, p. 103615, Feb. 2021, doi: 10.1016/j.micpro.2020.103615.
V. Tiwari, R. C. Joshi, and M. K. Dutta, “Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images,” Ecol. Inform., vol. 63, p. 101289, Jul. 2021, doi: 10.1016/j.ecoinf.2021.101289.
E. Liu, H. Zhao, S. Zhang, J. He, X. Yang, and X. Xiao, “Identification of plant species in an alpine steppe of Northern Tibet using close-range hyperspectral imagery,” Ecol. Inform., vol. 61, p. 101213, Mar. 2021, doi: 10.1016/j.ecoinf.2021.101213.
R. Cristin, B. Santhosh Kumar, C. Priya, K. Karthick, “Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection”, Artificial Intelligence Review 2020. https://doi.org/10.1007/s10462-020-09813-w.
Zhang, Xuhui, Liu, Yang, Lin, Haijun and Liu, Yukun. (2016). Research on SVM Plant Leaf Identification Method Based on CSA:. 624. 171–179. 10.1007/978-981-10-2098-8_20.
M. P. Vaishnnave, K. S. Devi, P. Srinivasan and G. A. P. Jothi, “Detection and Classification of Groundnut Leaf Diseases using KNN classifier,” 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), 2019, pp. 1–5, doi: 10.1109/ICSCAN.2019.8878733.
Munisami, T., Ramsurn, M., Kishnah, S., and Pudaruth, S. (2015). Plant Leaf Recognition Using Shape Features and Colour Histogram with K-nearest Neighbour Classifiers. Procedia Computer Science, 58. doi: 10.1016/j.procs.2015.08.095.
Verma, Hanuman, Tech, M. and Agrawal, R.. (2015). “Possibilistic Intuitionistic Fuzzy C-means Clustering Algorithm for MRI Brain Image Segmentation”, International Journal on Artificial Intelligence Tools. 24. 150205174241005. 10.1142/S0218213015500165.
Wang P, Fu H, Zhang K (2018a) A pixel-level entropy-weighted image fusion algorithm based on bidimensional ensemble empirical mode decomposition. Int J Distrib Sens Netw 14(12):1–16.
Roobaert D, Karakoulas G, Chawla NV (2006) Information gain, correlation and support vector machines. In: Guyon I, Nikravesh M, Gunn S, Zadeh LA (eds) Feature extraction. Springer, Berlin, pp. 463–470.
Damer N, Führer B (2012) Ear recognition using multi-scale histogram of oriented gradients. In: Proceedings of eighth international conference on intelligent information hiding and multimedia signal processing. IEEE, pp. 21–24.
Hu, L.Y., Huang, M.W., Ke, S.W. and Tsai, C.F., 2016. The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus, 5(1), pp. 1–9.
A. Agapitos, M. O’Neill, and A. Brabazon, “Adaptive distance metrics for nearest neighbour classification based on genetic programming,” in Proceedings of European Conference on Genetic Programming, 2013, pp. 1–12.
Wang, G., Yuan, Y. and Guo, W., 2019. An improved rider optimization algorithm for solving engineering optimization problems. IEEE Access, 7, pp. 80570–80576.
Binu, D. and Kariyappa, B.S., 2018. RideNN: A new rider optimization algorithm-based neural network for fault diagnosis in analog circuits. IEEE Transactions on Instrumentation and Measurement, 68(1), pp. 2–26.
Yang, X.S. and Deb, S., 2014. Cuckoo search: recent advances and applications. Neural Computing and Applications, 24(1), pp. 169–174.
Shehab, M., Khader, A.T. and Al-Betar, M.A., 2017. A survey on applications and variants of the cuckoo search algorithm. Applied Soft Computing, 61, pp. 1041–1059.
