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Hyperparameter Optimization for Transfer Learning-based Disease Detection in Cassava Plants


Affiliations
1 Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada 520 007, Andhra Pradesh, India., India
2 Tejas Networks, Bengaluru 560 100, India., India
3 Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada 520 007, Andhra Pradesh, India., India
4 School of computing, Mohan Babu University, Tirupati 517102, Andhra Pradesh, India., India
 

Cassava is quite possibly the mostwidely recognized staple food crop. It isa nutty-flavored, starchy root vegetable that is a primary energy source and carbs for individuals. During cropcultivation, cassava plant infections can influence the leaf and root, bringing about a tremendous loss to the harvest and financial market esteem. Hence, it isvital to detect diseases in cassava plants. But it requires enormous labor, longer time planning, and thoroughplant-specific knowledge. If disease detection is possible at the initial stages, then actions can betaken on time. Hence, there is a need to develop automatic detection methods for monitoring different parts of cassava plants. This study evaluates the efficiency of applying transfer learning to the pre-trained models for identifying diseases in cassava plants. The pre-trained EfficientNet model detects the disorders using data augmentation, fine-tuning the hyperparameters, cross-validation, and transfer learning. The experimentation is done with the cassava dataset provided by Kaggle, which contains cassava plant leaf images belonging to five classes. An experimental investigation shows that EfficientNet with transfer learning attains up to 89% accuracy. The effect of transfer learning is significant; consider getting the results of high accuracy and less dispersion; in very few cases, the model forecasts the wrong class labels. The outcomes give a promising strength tothe objective of this work, i.e., a model trained explicitly for agriculture with transfer learningcan assist the farmers with highly accurate results during farming to get a high yield.

Keywords

Cassava Leaf Diseases, Deep Learning, EfficientNet, Plant Disease Detection, Precision Agriculture.
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  • Hyperparameter Optimization for Transfer Learning-based Disease Detection in Cassava Plants

Abstract Views: 62  |  PDF Views: 64

Authors

Kalyani G
Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada 520 007, Andhra Pradesh, India., India
Sai Sudheer K
Tejas Networks, Bengaluru 560 100, India., India
Janakiramaiah B
Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada 520 007, Andhra Pradesh, India., India
Narendra Kumar Rao B
School of computing, Mohan Babu University, Tirupati 517102, Andhra Pradesh, India., India

Abstract


Cassava is quite possibly the mostwidely recognized staple food crop. It isa nutty-flavored, starchy root vegetable that is a primary energy source and carbs for individuals. During cropcultivation, cassava plant infections can influence the leaf and root, bringing about a tremendous loss to the harvest and financial market esteem. Hence, it isvital to detect diseases in cassava plants. But it requires enormous labor, longer time planning, and thoroughplant-specific knowledge. If disease detection is possible at the initial stages, then actions can betaken on time. Hence, there is a need to develop automatic detection methods for monitoring different parts of cassava plants. This study evaluates the efficiency of applying transfer learning to the pre-trained models for identifying diseases in cassava plants. The pre-trained EfficientNet model detects the disorders using data augmentation, fine-tuning the hyperparameters, cross-validation, and transfer learning. The experimentation is done with the cassava dataset provided by Kaggle, which contains cassava plant leaf images belonging to five classes. An experimental investigation shows that EfficientNet with transfer learning attains up to 89% accuracy. The effect of transfer learning is significant; consider getting the results of high accuracy and less dispersion; in very few cases, the model forecasts the wrong class labels. The outcomes give a promising strength tothe objective of this work, i.e., a model trained explicitly for agriculture with transfer learningcan assist the farmers with highly accurate results during farming to get a high yield.

Keywords


Cassava Leaf Diseases, Deep Learning, EfficientNet, Plant Disease Detection, Precision Agriculture.

References