TALOS: Optimization of the CNN for the detection of the Tomato Leaf Diseases

Authors: (IJEECS ID 38896)
TALOS: Optimization of the CNN for the detection of the Tomato Leaf Diseases
Detection of plant diseases in its early stage using a Convolutional Neural Network (CNN) constitutes a pivotal application at the intersection of agriculture and computer vision. However, Optimizing deep learning models to achieve peak performance is a significant challenge. Existing optimization methods suffer from issues like manual hyperparameter tuning, limited architecture search, and resource intensiveness. The proposed work comprises two main steps: first, customizing the CNN to identify Tomato plant diseases using a limited image set, which results in a reduced number of parameters, and second, adopting an automated hyperparameter tuning technique to obtain the optimal set of hyperparameters within a minimum time limit. Tunable Automated hyperparameter Learning Optimization System (TALOS) emerges as a prime choice since it automates critical hyperparameter tuning, thereby enhancing the likelihood of identifying optimal hyperparameters essential for achieving peak CNN performance. The performance of the proposed approach is recognizable through its exploration of five different hyperparameters across a search space of 32 combinations, yielding optimal parameters. Using 3030 tomato leaf images from a benchmark data set, the model achieves a remarkable 95.92% validation accuracy with 33647 trainable parameters, surpassing existing methods. This demonstrates its efficacy in plant disease identification, marking significant progress in the field.
Indonesian Journal of Electrical Engineering and Computer Science
ijeecs.iaescor...
Supported by Master Program of Electrical and Computer Engineering, Universitas Ahmad Dahlan, mee.uad.ac.id #yogyakarta
Admission: mee.uad.ac.id/...
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