Classification Of Mustard Leaf Diseases Using Convolutional Neural Network Architecture
AbstractDiseases in mustard leaves can reduce productivity if not detected early. This study aims to develop and evaluate a disease classification system for mustard leaves using Convolutional Neural Network (CNN) architectures, specifically Xception and VGG19, while comparing their performance in terms of accuracy and computational efficiency. The mustard leaf image dataset undergoes preprocessing before being used for model training and testing. Experimental results show that Xception achieves the highest validation accuracy of 99% with better loss stability compared to VGG19, which attains 94.50% accuracy but exhibits greater fluctuation. In terms of time efficiency, VGG19 reaches optimal accuracy faster and completes the training process in 42 seconds, whereas Xception requires more epochs and a training time of 50 seconds. Therefore, Xception is recommended for classification tasks that demand high accuracy and stability, while VGG19 is more suitable for rapid detection with a slight trade-off in accuracy stability. |
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References
Y. T. Samiha, “Strategi Pemanfaatan Media Air (Hidroponik) Pada Budidaya Tanaman Kangkung, Pakcoy dan Sawi Sebagai Alternatif Urban Farming,” J. Educ., vol. 06, no. 01, pp. 5835–5848, 2023.
A. Sood, P. Kumar Sarangi, A. Kumar Sahoo, L. Rani, K. Bajaj, and A. Kumar Agrawal, “AI-Driven Mustard Disease Identification: A Multiclass and Binary Classification Approach for Advanced Crop Health Monitoring,” Proc. - Int. Conf. Technol. Adv. Comput. Sci. ICTACS 2023, pp. 256–261, 2023, doi: 10.1109/ICTACS59847.2023.10390082.
M. Utami and Erwin Dwika Putra, “Deteksi Objek Kualitas Daun Sawi Menggunakan Metode HSV Color dan Color Blob,” JUSIBI (Jurnal Sist. Inf. dan Bisnis), vol. 5, no. 2, pp. 85–93, 2023, doi: 10.54650/jusibi.v5i2.518.
Sakshi, C. Sharma, S. Sharma, T. Sharma, and S. Gochhait, “Green Intelligence: A Sequential CNN Odyssey in Mustard Leaf Disease Detection,” 2024 ASU Int. Conf. Emerg. Technol. Sustain. Intell. Syst. ICETSIS 2024, no. March, pp. 877–882, 2024, doi: 10.1109/ICETSIS61505.2024.10459509.
Y. M. Abd Algani, O. J. Marquez Caro, L. M. Robladillo Bravo, C. Kaur, M. S. Al Ansari, and B. Kiran Bala, “Leaf disease identification and classification using optimized deep learning,” Meas. Sensors, vol. 25, no. September 2022, p. 100643, 2023, doi: 10.1016/j.measen.2022.100643.
M. T. A. Syech Ahmad and B. Sugiarto, “Implementasi Convolutional Neural Network (CNN) untuk Klasifikasi Ikan Cupang Berbasis Mobile,” Digit. Transform. Technol., vol. 3, no. 2, pp. 712–723, 2023, doi: 10.47709/digitech.v3i2.3245.
C. L. Nazalia, P. Palupiningsih, B. Prayitno, and Y. S. Purwanto, “Implementation of Convolutional Neural Network Algorithm to Pest Detection in Caisim,” ICCoSITE 2023 - Int. Conf. Comput. Sci. Inf. Technol. Eng. Digit. Transform. Strateg. Facing VUCA TUNA Era, pp. 609–614, 2023, doi: 10.1109/ICCoSITE57641.2023.10127792.
C. L. Srinidhi, O. Ciga, and A. L. Martel, “Deep neural network models for computational histopathology: A survey,” Med. Image Anal., vol. 67, p. 101813, 2021, doi: 10.1016/j.media.2020.101813.
L. Trihardianingsih, A. Sunyoto, and T. Hidayat, “Classification of Tea Leaf Diseases Based on ResNet-50 and Inception V3,” Sinkron, vol. 8, no. 3, pp. 1564–1573, 2023, doi: 10.33395/sinkron.v8i3.12604.
A. Mehrish, N. Majumder, R. Bharadwaj, R. Mihalcea, and S. Poria, A review of deep learning techniques for speech processing, vol. 99. 2023. doi: 10.1016/j.inffus.2023.101869.
W. Bian, “Real-Fake Face Detection Based on Joint Multi-Layer CNN Structure and Data Augmentation,” no. Daml 2023, pp. 25–29, 2024, doi: 10.5220/0012800300003885.
J. A. AYENI, “Convolutional Neural Network (CNN): The architecture and applications,” Appl. J. Phys. Sci., vol. 4, no. 4, pp. 42–50, 2022, doi: 10.31248/ajps2022.085.
A. Mumuni and F. Mumuni, “Data augmentation: A comprehensive survey of modern approaches,” Array, vol. 16, no. November, p. 100258, 2022, doi: 10.1016/j.array.2022.100258.
S. M. Hassan and A. K. Maji, “Plant Disease Identification Using a Novel Convolutional Neural Network,” IEEE Access, vol. 10, pp. 5390–5401, 2022, doi: 10.1109/ACCESS.2022.3141371.
J. Kotwal, D. R. Kashyap, and D. S. Pathan, “Agricultural plant diseases identification: From traditional approach to deep learning,” Mater. Today Proc., vol. 80, no. March, pp. 344–356, 2023, doi: 10.1016/j.matpr.2023.02.370.
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