LiteCShuffle: a lightweight and efficient deep learning approach for plant disease classification

Recent advancement in artificial intelligence and deep learning technologies has made automatic diagnosis of plant disease a reality, which offers a better alternative to traditional manual methods. However, the large size of many existing deep learning models limits their deployment on resource-constrained platforms such as IoT or mobile devices. This study proposed LiteCShuffle (LCS), a lightweight model based on the standard ShuffleNetv2 model, to solve this issue. The LCS model utilized channel attention mechanisms and channel shuffle in its inverted residual block for improving the model’s ability to extract relevant features at various stages. The proposed model performance is assessed on the PlantVillage dataset, which includes 39 classes across 13 crop types. The LCS model shows a notable enhancement in plant disease classification, attaining an accuracy of 99.86% while having only 0.15 million trainable parameters and a total size of 0.58 MB. In comparison to ShuffleNetV2, which attains an accuracy of 99.68% with 1.29 million parameters and a model size of 15.4 MB. The proposed model uses fewer parameters and is less complex. Additionally, it surpasses other frequently employed models, such as VGG16, EfficientNetV2s, DenseNet201, SqueezeNet, and MobileNetV2. The model’s effectiveness for real-time applications is demonstrated by its 28 millisecond latency on the Nvidia Jetson Nano, which is far lower than ShuffleNetV2 models. Code at: https://github.com/Dsangeeta97/Litechsuffle/.