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

Abstract

Cassava is quite possibly the most widely recognized staple food crop. It is a nutty-flavored, starchy root vegetable that is a primary energy source and carbs for individuals. During crop cultivation, cassava plant infections can influence the leaf and root, bringing about a tremendous loss to the harvest and financial market esteem. Hence, it is vital to detect diseases in cassava plants. But it requires enormous labor, longer time planning, and thorough plant-specific knowledge. If disease detection is possible at the initial stages, then actions can be taken 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 to the objective of this work, i.e., a model trained explicitly for agriculture with transfer learning can assist the farmers with highly accurate results during farming to get a high yield.