Deep transfer learning model for disease identification in wheat crop

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 phyto-sanitary 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 ac-curacy, 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 litera-ture 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.