MobileNet-GRU fusion for optimizing diagnosis of yellow vein mosaic virus

Abstract

Yellow vein mosaic virus (YVMV) is a destructive plant virus that commonly affects crops, particularly okra, in India. The virus is transmitted by whiteflies and poses significant challenges to agricultural productivity. Infection with YVMV leads to distinct yellow vein patterns on leaves, stunted growth, reduced yield, and ultimately economic losses for farmers. Timely and accurate detection of YVMV is crucial for effective disease management. In this article, we present a novel method that employs advanced deep-learning models to identify YVMV-infected okra plants. The study leverages a dataset of over 2000 okra plant leaves that implements transfer learning models, including MobileNet, EfficientNet, InceptionV3, VGG19, InceptionResNetV2, and ResNet50 and recurrent neural networks (RNN) variants, including Long short-term memory (LSTM), Bidirectional long short-term memory (BiLSTM) and Gated recurrent unit (GRU). Additionally, three hybrid models, combining MobileNet with LSTM, BiLSTM, and GRU, are incorporated to capitalize on the characteristics of both MobileNet and RNNs through superior feature extraction and detection of temporal dependencies. The results demonstrate that the MobileNet model combined with all three RNNs achieves exceptional accuracy, surpassing 99.27%. Notably, the MobileNet model integrated with GRU exhibits the most optimized performance with the least loss and greatest accuracy, facilitating improved disease management strategies and aiding in the yield of crops by reducing the impact of YVMV.