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Abstract
Zeae-maydis, also known as maize gray leaf spot, and porcinia sorghi, known as maize common rust, are the two most prevalent and dangerous diseases that harm maize crops in Nigeria. Plant diseases are difficult for Nigerian farmers to recognize correctly, and it is impossible to assess their severity with the unaided eye. However, hiring a pathologist is more costly and time-consuming for large farms. Moreover, many support vector machine (SVM) classification models for maize leaf disease classification have been developed by different researchers. However, these existing models are impacted by imbalanced datasets, irrelevant feature selection, and difficulty in fine-tuning the hyperparameters of the SVM. Consequently, to resolve these problems, two optimized multiclass support vector machine classification models (BPSO-SVM and RSA-SVM) were trained to categorize maize leaves disease into Zeae-maydis and porcinia sorghi using 1,648 photos of maize leaves across all maize datasets, which included 574 photos of gray leaf spot disease, 574 photos of common rust disease, and 500 photos of healthy leaves obtained from the Kaggle village datasets. The images were scaled down, converted to grayscale, and enhanced using morphological filtering, bi-histogram equalisation techniques, and adaptive median filtering before the affected area was segmented through the Sobel edge detection method. The Gray Level Spatial Dependence and colour moment were then used to extract texture, shape, and colour features, which were then fused using the linear combination method. The 10-fold approach was used to train and test each classification model. The comparative experiments demonstrate that the BPSO-SVM model outperforms the RSA-SVM model at a threshold value of 0.80. The RSA-SVM model has a performance accuracy of 95.62% and 95.25% on the datasets for gray leaf spot and common rust disease, respectively, while the BPSO-SVM has a performance accuracy of 96.37% and 96.93% on the same datasets. The two models can be used to classify Zeae-maydis and porcinia sorghi in maize, according to a comparison with the current models. However, this study only identified two of the numerous diseases that affect maize, and it offered no suggestions for how to prevent any of these illnesses.
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References
J. Adarsh, “Detection and classification of leaf diseases in maize plant using machine learning. M.Sc Research Project Submitted to Natural College of Ireland. pp.1-22, 2019. https://norma.ncirl.ie/4278/1/adarshjayakumar.pdf
E. Alehegn, “ Maize leaf diseases recognition and classification based on imaging and machine learning techniques”, International Journal of Innovative Research in Computer and Communication Engineering, 5(12), pp. 1-11, 2017. https://www.rroij.com/open-access/maize-leaf-diseases-recognition-and-classification-based-on-imaging-and-machine-learning-techniques-.pdf
N. Ansori, A. Rachmad, E. S. Rochman, H. Fauzan and Y. P. Asmara, “ Corn stalk disease classification using random forest combination of extraction features”, Communications in Mathematical Biology and Neuroscience (CMBN), Vol. 19, pp. 1-20, 2024.
J. Basavaiah and A. A. Anthony, . “Tomato leaf disease classification using multiple feature extraction techniques”, Wireless Personal Communication, Vol. 115, No. 19, pp. 1-20, 2020.
D. Chauhan, R. Walia, C. Singh, M. Deivakani and M. Kumbhkar, “Detection of maize disease using random forest classification algorithm”, Turkish Journal of Computer and Mathematics Education, Vol. 12, No. 9, pp. 715-720, 2021.
P. Dong, K. Li, M. Wang, F. Li, W. Guo and H. Si, “Maize leaf compound disease recognition based on attention mechanism”, Agriculture, Vol. 14, No. 1, pp. 1-22, 2023.
M. Islam, A. Dinh, K. Wahid and P. Bhowmik, “Detection of potato diseases using image segmentation and multiclass support vector machine”, In: Proceeding of 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), 2017-06-15, pp. 1-5, 2017.
S. Jeyalakshmi and R. Radha, “Classification of tomato diseases using ensemble learning”, ICTACT Journal of Soft Computing, Vol. 11, No. 4, pp. 2408-2415, 2021. https://ictactjournals.in/paper/IJSC_Vol_11_Iss_4_Paper_3_2408_2415.pdf
N. R. Kakade and D. D. Ahire, “ Real time grape leaf disease detection”, International Journal of Advance Research and Innovative Ideas in Education, Vol. 4, No. 1, pp. 598-610, 2015.
C. U. Kumari, S. J. Prasad and G. Mounika, “Leaf disease detection. Feature extraction with k-means clustering and classification with ANN”, Proceedings of the Third International Conference on Computing Methodologies and Communication (ICCMC 2019). IEEE Xplore Part Number: CFP19K25-ART; ISDN:978-1-5386-7808-4, pp. 1095-1098, 2019.
B. S. Kusumo, A. Heryana, O. Mahendra and H. F. Pardede, “Machine learning-based for automatic detection of corn-plant disease using image processing”, In Proceeding of 2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA), pp. 93-97, 2018.
M. Masood, M. Nawaz, T. Nazir, A. Javed, R. Alkanhel, H. Elmannai, S. Dhahbi and S. Bourouis, “MaizeNet: A deep learning approach for effective recognition of maize plant leaf diseases”, IEEE Access, Vol. 11, pp. 1-15, 2023.
Z. H. Mohammed, I. O. Oyefolahan, M. D. Abdulmalik and S. A. Bashir, “Identification of bacterial leaf blight and powdery mildew diseases based on a combination of histogram of oriented gradient and local binary pattern features”, S. Misra and B. Muhammad-Bello (Eds.): ICTA 2020, CCIS 1350, pp. 301-314, 2021.
[14] S. N. Mohanty, H. Ghosh, I. S. Rahat and C. V. Rami Reddy, “Advanced deep learning models for corn leaf disease classification: A field study in Bangladesh”, Engineering Proceedings, Vol. 59, No. 1, pp. 1-9, 2023.
K. K. Nivethithaa and S. Vijayalakshmi, “Optimized svm model for maize and rice leaf disease detection”, Data Acquisition and Processing, Vol. 38, No. 2, pp. 3146-3159, 2023. https://sjcjycl.cn/article/view-2023/pdf/02_3146.pdf
D. A. Noola and D. R. Bassavaraju,” Corn leaf image classification based on machine learning technique for accurate leaf disease detection”, International Journal of Electrical and Computer Engineering (IJECE), Vol. 12, No. 3, pp. 2509-2516, 2022.
C. Nyasulu, A. Diattra, A. Traore, C. Ba, P. M. Diedhiou, Y. Sy, H. Raki and D. H. Peluflo-Ordonez, “A comparative study of machine learning-based classification of tomato fungal diseases: Application of GLCM texture features”, Heliyon, Vol. 9, pp.1-12, 2023.
V. M Ochango, G M. Wambugu and J. G. Ndia, “Comparative analysis of machine learning algorithms accuracy for maize leaf disease identification”, International Journal of Formal Sciences: Current and Future Research Trends (IJFSCFRT), Vol. 13, No 1, pp. 60-73, 2022.
A. Patel, R. Mishra and A. Sharma, “Maize plant leaf disease classification using a supervised machine learning algorithm”, Fusion: Practice and Applications (FPA), Vol. 13, No. 2, pp. 8-21, 2023.
S. Pavithra, A. Priyadharshini, V. Praveena and T. Monika, “Paddy leaf disease detection using SVM classifier”, International Journal of Communication and Computer Technologies, Vol. 3, No. 1, pp. 16-20, 2015.
M. Piske, D. Kurade,H. Khaladkar, V. Kolekar and S. Adagale, “Grape leaves disease detection using K-NN classification algorithm”, International Journal of Advance Research in Science and Engineering, Vol.11, No.4, pp. 48-56, 2022.
T. A. Prasetyo, V. L. Desrony, H. F. Panjaitan, R. Sianipar and Y. Pratama, “Corn plant disease classification based on leaf using residual networks-9 architecture”, International Journal of Electrical and Computer Engineering (IJECE), Vol. 13, No. 3, pp. 2908-2920.
J. D. Pujari, R. Yakkundimath and Byadji, “Classification of fungi disease symptoms affected on cereals using colour texture features”, International Journal of Signal Processing, Image Processors and Pattern Recognition, Vol. 6, No. 6, pp. 321-330, 2013.
A. Rachmad, N. Ansori, S. Rifka, E. M. Rochman, Hermawan, Husni and W. Setiawan, “Classification of diseases in corn stalks using a random forest based on a combination of the feature extraction (local binary pattern and colour histogram)”, Technium: Romanian Journal of Applied Sciences and Technology, Vol. 16, pp. 303-309, 2023.
R. K. Ray, M. Bhardway, R. Kumar and S. Chakravarty, “ Support vector machine-based classification for tomato leaves diseases”, International Journal of Modern Agriculture, Vol. 9, No. 4, 210-215, 2020.
Y. Restil, C. Irsan, J. F. Latif, I. Yani. and N. R. Dewi, “A bootstrap-aggregating in random forest model for classification of corn plant diseases and pest”, Science and Technology Indonesia, Vol. 8, No. 2, pp. 288-297, 2023.
Y. Restil, C. Irsan, M. T. Putril, I. Yani, Anshori and B.Suprihatin, “Identification of corn plant diseases and pests based on digital images using multinomial naïve bayes and k-nearest neighbour “, Science and Technology Indonesia, Vol. 7, No. 1, pp. 29-35, 2022.
M. Sibiya and M. Sumbwanyambe, “A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks”, AgriEngineering, Vol. 1, No. 1, pp. 119-131, 2019.
A. S. Singh, B. Chourasia, N. Raghuwanshi and K. Raju, “BPSO based feature selection for rice plant leaf disease detection with random forest classifier”, International Journal of Engineering Trends and Technology, Vol. 69, No. 4, pp. 34-43., 2021
F. Solihin, M. Syarief, E. M. Rochman and A. Rachmad, “Comparison of support vector machine (SVM), k-nearest neighbour (KNN), and stochastic gradient descent (SGD) for classifying corn leaf disease based on histogram of oriented gradients (HOG) feature extraction”, Electronics, Informatics, and Vocational Education (ELINVO), Vol. 8, No. 1, pp. 121-129, 2023.
P. Srivastava, “Corn leaf disease identification with improved accuracy”, In: Proceeding of Advances in Computation Intelligence, Its Concepts & Applications at ISIC 2022, May 17-19, Savannah, United States. pp. 1-6. https://ceur-ws.org/Vol-3283/Paper44.pdf
M. Syarief, N. Prastiti and W. Setiawan, “Maize leaf disease image classification using bag of features”, Journal of Informatics Telecommunication Electronics, Vol. 11, No. 2, pp. 48-54, 2019.
M. Thanjaivadivel and R. Suguna, “Leaf disease prediction using fast enhanced learning method”, International Journal of Engineering Trends and Technology, Vol. 69, No. 9, pp. 34-44, 2021.
A. Ubaidillah, E. M. S. Rochman, D. A. Fatah and A. Rachmad, “Classification of corn disease using random forest, neural network, and naïve bayes method”, Journal of Physics: Conference Series, Vol. 2406, pp. 1-11, 2023.
E. Vamsidhar, P. J. Rani and K. R. Babu, “ Plant disease identification and classification using image processing”, International Journal of Engineering and Advanced Technology (IJEAT), Vol. 8, No. 3, pp. 442-446, 2019.
M. A. Mohd Yusof and A. Nazari, “The disease detection for maize-plant using k-means clustering”, Evolution in Electrical and Electronic Engineering, Vol. 2, No. 2, pp. 834-841, 2021.
S. Yang, Z. Xing, H. Wang, X. Dong, X. Gao, Z. Liu, X. Zhang, S. Li and Y. Zhao, “Maize-YOLO: A new high-precision and real-time method for maize pest detection”, Insects, Vol. 14, No. 3, pp. 1-13, 2023.
H. Yu, J. Liu, C. Chen, A. A. Heidari, Q. Zhang, H. Chen, M. Marfaja and H. Turabieh, “Corn leaf diseases diagnosis based on k-means clustering and deep learning”, IEEE Access, Vol. 9, pp. 1-12, 2021.
H. Zhang, Z. Guoxiong, A. Chen, J. Li, M. Li, W. Zhang, Y. Hu and W. Yu, “Maize disease recognition based on image enhancement and OSCRNet”, pp. 1-29, 2021. https://assets.researchsquare.com/files/rs-871678/v1_covered.pdf?c=1631878799
Z. Zhang, X. He, X. Sun, L. Guo, J. Wang and F. Wang, “Image recognition of maize leaf disease based on GA-SVM”, Chemical Engineering Transactions, Vol. 46. Pp. 199-204, 2015.
W. Zhuo and X. Yu, “A particle swarm optimization algorithm based on dynamic adaptive and chaotic search”, IOP Conference Series: Materials Science and Engineering, Vol. 612, pp. 1-8, 2019.
M. Li, L. Liu, G. Sun, K. Su, H. Zhang, B. Chen and Y.Wu, “Particle swarm optimization algorithm based on chaotic sequences and dynamic self-adaptive strategy”, Journal of Computer and Communication, Vol.5, No.12, pp. 13-23, 2017. https://www.scrip.org/pdf/JCC_2017092915395182.pdf
H. Jia, C. Lu, D. Wu, C., Wen, H. Rao and L. Abualigah, “An improved reptile search algorithm with ghost opposition-based learning for global optimization problems”, Journal of Computational Design and Engineering, Vol. 10, pp. 1390-1422, 2023.
L. Alzubaidi, J. Zhang, A.J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaria and M.A. Fadhel, “Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions”, Journal of Big Data, vol. 8, No. 53, 1-74, 2021.
V.K.Vishnoi, K. Kumar. and B. Kumar, “A comprehensive study of feature extraction techniques for plant leaf disease detection”, Multimedia Tools and Applications, vol. 80, No.2, pp.1-54, 2021.
https://www.researchgate.net/publication/354507540_A_comprehensive_study_ of_feature_extraction_techniques_for_plant_leaf_disease_detection
S. Albahli and M. Masood, “Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification”, Frontiers in Plant Science, vol. 13, pp. 1-18, 2022.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597248/
M.O.Yin, and C.H Nay, “Plant Leaf Disease Detection and Classification using Image Processing”, International Journal of Research and Engineering, vol. 5, No. 9, pp. 516-523, 2018.
E.L da Rocha, L.F. Rodrigues, and J.F.Mari, “Maize leaf disease classification using convolutional neural networks and hyperparameter optimization”. In: Proceeding of Conference XVI Workshop de Visao Computational (WVC 2020),