Optimizer Performance in Deep Learning for Leaf Disease Classification
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Abstract
Global food security is at risk due to the spread of plant diseases, particularly in regions where agriculture is a significant economic activity. Early and precise identification of plant diseases helps avoid crop loss and maintain agricultural output. Here, we investigate different deep learning optimizers for the classification of leaf diseases. We employ EfficientNet-B0, a cutting-edge model built on high accuracy and efficiency in image classification tasks intended for agricultural settings with limited resources. The PlantVillage and PlantDoc databases are used to determine the best optimizer. We evaluate the results of five popular optimizers on EfficientNet-B0: Adam, Nadam, Adagrad, RMSprop, and SGD. Empirical findings indicate that Adam produces training, validation, and testing outcomes that outperform other optimizers. Future real-time agricultural application implementations are anticipated to be fueled by this insight.
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References
Y. Gai and H. Wang, “Plant Disease: A Growing Threat to Global Food Security,” Agronomy, vol. 14, no. 8, pp. 1615, 2024.
S. Konduru, M. Amiruzzaman, V. D. Avina and M. R. Islam, “Plant Disease Detection and Classification Using Deep Learning Models,” J. Comput. Sci. Colleg., vol. 39, no. 3, pp. 66-75, 2023.
B. Yang, M. Li, F. Li and H. Wang, “A Novel Plant Type, Leaf Disease, and Severity Identification Framework Using CNN and Transformer with Multi-label Method,” Sci. Rep., vol. 14, no. 1, pp. 116644, 2024.
M. H. Saleem, J. Potgieter and K. M. Arif, “Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers,” Plants, vol. 9, no. 10, pp. 1319, 2020.
H. Farman, J. Ahmad, B. Jan, Y. Shahzad, M. Abdullah and S. Ali, “EfficientNet-based Robust Recognition of Peach Plant Diseases in Field Images,” Comput. Mater. Contin., vol. 71, no. 1, pp. 2073–2089, 2022.
P. P. Fathimathul Rajeena, S. U. Aswathy, 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, pp.1938, 2023.
M. M. Alqahtani, A. K. Dutta, S. Almotairi, M. Ilayaraja, A. A. Albraikan, F. N. Al-Wesabi and M. Al Duhayyim, “Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection,” Comput., Mater. & Contin., vol. 75, no. 1, pp. 217-232, 2023.
S. Ruder, “An Overview of Gradient Descent Optimization Algorithms,” arXiv, vol. abs/1609.04747, 2016.
J. Heaton, “Ian Goodfellow, Yoshua Bengio and Aaron Courville: Deep learning: The MIT Press, 2016, 800 pp, ISBN: 0262035618,” Genet. Program. Evolvable Mach., vol. 19, no. 1, pp. 305 – 307, 2018.
A. C. Wilson, R. Roelofs, M. Stern, N. Srebro and B. Recht, “The Marginal Value of Adaptive Gradient Methods in Machine Learning,” Adv. Neur. Informat. Process. Syst., vol. 30, 2017.
R. Y. Sun, “Optimization for Deep Learning: An Overview,” J. the Operat. Res. Soc. of China, vol. 8, no. 2, pp. 249-294, 2020.
E. M. Dogo, O. J. Afolabi and B. Twala, “On the Relative Impact of Optimizers on Convolutional Neural Networks with Varying Depth and Width for Image Classification,” Appl. Sci., vol. 12, no. 23, pp. 11976, 2022.
P. Singh, P. Singh, U. Farooq, S. S. Khurana, J. K. Verma and M. Kumar, “CottonLeafNet: Cotton Plant Leaf Disease Detection Using Deep Neural Networks,” Multimed. Tools Appl., vol. 82, no. 24, pp. 37151–37176, 2023.
I. Kunduracioglu and I. Pacal, "Deep Learning-based Disease Detection in Sugarcane Leaves: Evaluating EfficientNet Models," J. Oper. Intell., vol. 2, no. 1, pp. 321–335, 2024.
L. T. Duong, P. T. Nguyen, C. Di Sipio and D. Di Ruscio, “Automated Fruit Recognition using EfficientNet and MixNet,” Comput. Electron. Agric., vol. 171, pp. 105326, 2020.
W. Shafik, A. Tufail, C. De Silva Liyanage and R. A. A. H. M. Apong, “Using Transfer Learning-based Plant Disease Classification and Detection for Sustainable Agriculture,” BMC Plant Bio., vol. 24, no.1, pp. 136, 2024.
I. Khan, S. S. Sohail, D. Ø. Madsen and B. K. Khare, “Deep Transfer Learning for Fine-Grained Maize Leaf Disease Classification,” J. Agric. Food Res., vol. 16, pp. 101148, 2024.
S. Choubey and Divya, “Lightweight Federated Transfer Learning for Plant Leaf Disease Detection and Classification Across Multiclient Cross-Silo Datasets,” in BIO Web of Conf., vol. 82, pp. 05018, 2024.
S. P. Mohanty, D. P. Hughes and M. Salathé, “Using Deep Learning for Image-based Plant Disease Detection,” Front. Plant Sci., vol. 7, pp. 1419, 2016.