A Hybrid Method for Automatic Plant Leaf Disease Identification Using Whale Optimization Algorithm and Convolutional Neural Networks
محورهای موضوعی : Meta-heurestics
زهرا آکشته
1
(گروه مهندسی کامپیوتر، واحد بروجرد، دانشگاه آزاد اسلامی، بروجرد، ایران)
Parvaneh Asghari
2
(گروه مهندسی کامپیوتر، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران)
سید حمید حاجی سید جوادی
3
(گروه مهندسی کامپیوتر، دانشگاه شاهد، تهران، ایران)
حمید رضا نویدی قاضیانی
4
(گروه ریاضی و علوم کامپیوتر، دانشگاه شاهد، تهران، ایران)
کلید واژه: leaf disease, classification, convolutional neural network, whale optimization algorithm.,
چکیده مقاله :
This study introduces a combined approach using deep learning and optimization to accurately and efficiently classify plant leaves based on disease and health. By optimizing hyper parameters with a whale optimization algorithm and utilizing a convolutional neural network for disease classification, the model achieves high accuracy. The Plant Village dataset is used, and data augmentation is applied to improve the model's performance. The optimized network achieves a classification accuracy of 95.22% for the test set and 99.57% for the training set, with precision and recall values of 95.24% and 95.22% respectively. The performance and efficiency of the proposed method are proven to be superior when compared to other models and pre-trained networks. This study has potential applications in various image classification tasks and can be valuable in agriculture, horticulture, and plant disease identification. Furthermore, the proposed network achieves higher accuracy with fewer trainable parameters and computations compared to similar works.
This study introduces a combined approach using deep learning and optimization to accurately and efficiently classify plant leaves based on disease and health. By optimizing hyper parameters with a whale optimization algorithm and utilizing a convolutional neural network for disease classification, the model achieves high accuracy. The Plant Village dataset is used, and data augmentation is applied to improve the model's performance. The optimized network achieves a classification accuracy of 95.22% for the test set and 99.57% for the training set, with precision and recall values of 95.24% and 95.22% respectively. The performance and efficiency of the proposed method are proven to be superior when compared to other models and pre-trained networks. This study has potential applications in various image classification tasks and can be valuable in agriculture, horticulture, and plant disease identification. Furthermore, the proposed network achieves higher accuracy with fewer trainable parameters and computations compared to similar works.
