Corn Leaf Disease Classification Optimization Using Resnet50 Architecture Utilizing Bayesian Optimization

DOI: https://doi.org/10.33650/jeecom.v7i1.9809

Authors (s)


(1) * Yahya Auliya Abdillah   (Universitas Amikom yogyakarta)  
        Indonesia
(2)  Kusrini Kusrini   (Universitas Amikom yogyakarta)  
        Indonesia
(*) Corresponding Author

Abstract


This research aims to optimize the classification of diseases on corn leaves using Convolutional Neural Network (CNN) architecture, ResNet50, combined with hyperparameter optimization techniques using Bayesian Optimization. The dataset used comes from Kaggle, consisting of four classes of corn leaf diseases, namely corn leaf spot, leaf rust, corn leaf blight, and healthy corn leaves. Data pre-processing was done to balance the amount of data between classes and reduce the risk of overfitting. This study tested various scenarios, including the use of the original dataset and a pre-processed dataset. The experimental results show that the use of Bayesian Optimization in hyperparameter search gives better results than manual parameter setting. The scenario with hyperparameter optimization using Bayesian Optimization technique on the pre-processed dataset shows an increase in accuracy by 5% (87.79%) compared to the scenario without optimization (82.82%). This research concludes that hyperparameter optimization techniques and proper data pre-processing can improve the performance of CNN models in corn plant disease classification, providing the potential to assist farmers in detecting diseases earlier and reducing the economic losses incurred.

Keywords

Convolutional Neural Network; Bayesian Optimization; Classification; Corn Leaf; Deep Learning;



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References


H. D. Mafukidze, G. Owomugisha, D. Otim, A. Nechibvute, C. Nyamhere, and F. Mazunga, “Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation,” Appl. Sci., vol. 12, no. 17, 2022, doi: 10.3390/app12178412.

F. Rajeena P. P, A. S. U, M. A. Moustafa, and M. A. S. Ali, “Detecting Plant Disease in Corn Leaf Using EfficientNet Architecture—An Analytical Approach,” Electron., vol. 12, no. 8, 2023, doi: 10.3390/electronics12081938.

M. Masood et al., “MaizeNet: A Deep Learning Approach for Effective Recognition of Maize Plant Leaf Diseases,” IEEE Access, vol. 11, no. June, pp. 52862–52876, 2023, doi: 10.1109/ACCESS.2023.3280260.

D. A. Noola and D. R. Basavaraju, “Corn leaf image classification based on machine learning techniques for accurate leaf disease detection,” Int. J. Electr. Comput. Eng., vol. 12, no. 3, pp. 2509–2516, 2022, doi: 10.11591/ijece.v12i3.pp2509-2516.

R. L. Gaho, I. T. Ali, and E. Prakasa, “Klasifikasi Kualitas Permukaan Jalan Raya Menggunakan Metode Cnn Berbasis Arsitektur Xception,” INOVTEK Polbeng - Seri Inform., vol. 9, no. 1, pp. 354–365, 2024, doi: 10.35314/isi.v9i1.4213.

W. G. Pamungkas, M. I. P. Wardhana, Z. Sari, and Y. Azhar, “Leaf Image Identification: CNN with EfficientNet-B0 and ResNet-50 Used to Classified Corn Disease,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 2, pp. 326–333, 2023, doi: 10.29207/resti.v7i2.4736.

M. Fraiwan, E. Faouri, and N. Khasawneh, “Classification of Corn Diseases from Leaf Images Using Deep Transfer Learning,” Plants, vol. 11, no. 20, pp. 1–14, 2022, doi: 10.3390/plants11202668.

S. Malliga, P. S. Nandhini, S. V. Kogilavani, R. Jaya Harini, S. Jaya Shree, and G. Jeeva, “Maize leaf disease classification using convolutional neural network,” AIP Conf. Proc., vol. 2387, 2021, doi: 10.1063/5.0068599.

P. K. Nalli, M. V. Subbarao, D. P. Garapati, K. P. Swaroop, R. Priyakanth, and G. P. Kumar, “Performance Analysis of Pre-Trained Deep Learning Architectures for Classification of Corn Leaf Diseases,” 2023 Int. Conf. Network, Multimed. Inf. Technol. NMITCON 2023, pp. 1–8, 2023, doi: 10.1109/NMITCON58196.2023.10275915.

V. V. Srinidhi, A. Sahay, and K. Deeba, “Plant Pathology Disease Detection in Apple Leaves Using Deep Convolutional Neural Networks : Apple Leaves Disease Detection using EfficientNet and DenseNet,” Proc. - 5th Int. Conf. Comput. Methodol. Commun. ICCMC 2021, no. Iccmc, pp. 1119–1127, 2021, doi: 10.1109/ICCMC51019.2021.9418268.

N. Lysbetti Marpaung, R. Juan Hendri Butar-Butar, and S. Hutabarat, “Implementasi Deep learning untuk Identifikasi Daun Tanaman Obat Menggunakan Metode Transfer learning,” JEPIN (Jurnal Edukasi dan Penelit. Inform. , vol. 9, no. 3, pp. 348–354, 2023.

M. Dahiya, M. S. Guru Prasad, T. Anand, K. Kumar, S. Bansal, and H. N. Naveen Kumar, “An Effective Detection of Litchi Disease using Deep Learning,” 2023 14th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2023, pp. 1–6, 2023, doi: 10.1109/ICCCNT56998.2023.10307717.

I. Analuisa-Aroca, A. Vergara-Romero, and I. B. Pérez Almeida, “Convolutional neural networks ResNet-50 for weevil detection in corn kernels,” Sci. Agropecu., vol. 14, no. 3, pp. 385–394, 2023, doi: 10.17268/sci.agropecu.2023.034.

E. al. Pallepati Vasavi, “Image based Chili Crop Disease Prediction Using Deep Transfer Learning,” Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 10, pp. 1145–1149, 2023, doi: 10.17762/ijritcc.v11i10.8635.

M. N. Zhafar, K. Usman, and F. Akhyar, “Penerapan Metode Clustering Dengan Algoritma K-Means Untuk Analisa Persebaran Varian Covid-19 ( Studi Kasus Kelurahan Antapani Kidul ),” e-Proceeding Eng., vol. 10, no. 5, pp. 4257–4264, 2023.

Ramanjot et al., “Plant Disease Detection and Classification: A Systematic Literature Review,” Sensors, vol. 23, no. 10, 2023, doi: 10.3390/s23104769.


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