Deep transfer learning model for disease identification in wheat crop

Abstract

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.