Record Details

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

NOPR - NISCAIR Online Periodicals Repository

View Archive Info
 
 
Field Value
 
Title Hyperparameter Optimization for Transfer Learning-based Disease Detection in Cassava Plants
 
Creator G, Kalyani
K, Sai Sudheer
B, Janakiramaiah
B, Narendra Kumar Rao
 
Subject Cassava leaf diseases
Deep learning
EfficientNet
Plant disease detection
Precision agriculture
 
Description 536-545
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.
 
Date 2023-05-10T05:17:55Z
2023-05-10T05:17:55Z
2023-05
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61886
https://doi.org/10.56042/jsir.v82i05.1089
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(05) [May 2023]