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Deep transfer learning model for disease identification in wheat crop

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Title Deep transfer learning model for disease identification in wheat crop
Not Available
 
Creator Sapna Nigam
Rajni Jain
Sudeep Marwaha
Alka Arora
Md. Ashraful Haque
Akshay Dheeraj
Vaibhav Kumar Singh
 
Subject Wheat rusts, EfficientNet, Deep transfer learning, Convolutional neural networks
 
Description Not Available
Wheat rusts, caused by pathogenic fungi, are responsible for significant losses in Wheat production. Leaf rust can cause around 45–50% crop loss, whereas stem and stripe rust can cause up to 100% crop loss under suitable weather conditions. Early treatment is crucial in reducing yield loss and improving the effectiveness of phytosanitary measures. In this study, an EfficientNet architecture-based model for Wheat disease identification is proposed for automatically detecting major Wheat rusts. We prepared a dataset, referred to as WheatRust21, consisting of 6556 images of healthy and diseased leaves from natural field conditions. We attempted several classical CNN-based models such as VGG19, ResNet152, DenseNet169, InceptionNetV3, and MobileNetV2 for Wheat rust disease identification and obtained accuracy ranging from 91.2 to 97.8%. To further improve accuracy, we experimented with eight variants of EfficientNet architecture and discovered that our fine-tuned EfficientNet B4 model achieved a testing accuracy of 99.35%, a result that has not been reported in the literature so far to the best of our knowledge. This model can be easily integrated into mobile applications for use by stakeholders for image-based wheat disease identification in field conditions.
Not Available
 
Date 2023-04-26T09:43:16Z
2023-04-26T09:43:16Z
2023-07-01
 
Type Article
 
Identifier Nigam, S., Jain, R., Marwaha, S., Arora, A., Haque, M. A., Dheeraj, A., & Singh, V. K. (2023). Deep transfer learning model for disease identification in wheat crop. Ecological Informatics, 102068.
1574-9541
http://krishi.icar.gov.in/jspui/handle/123456789/76881
 
Language English
 
Relation Not Available;
 
Publisher Elsevier