Cotton disease identification method based on pruning

Abstract

Deep convolutional neural networks (DCNN) have shown promising performance in plant disease recognition. However, these networks cannot be deployed on resource-limited smart devices due to their vast parameters and computations. To address the issue of deployability when developing cotton disease identification applications for mobile/smart devices, we compress the disease recognition models employing the pruning algorithm. The algorithm uses the γ coefficient in the Batch Normalization layer to prune the channels to realize the compression of DCNN. To further improve the accuracy of the model, we suggest two strategies in combination with transfer learning: compression after transfer learning or transfer learning after compression. In our experiments, the source dataset is famous PlantVillage while the target dataset is the cotton disease image set which contains images collected from the Internet and taken from the fields. We select VGG16, ResNet164 and DenseNet40 as compressed models for comparison. The experimental results show that transfer learning after compression overall surpass its counterpart. When compression rate is set to 80% the accuracies of compressed version of VGG16, ResNet164 and DenseNet40 are 90.77%, 96.31% and 97.23%, respectively, and the parameters are only 0.30M, 0.43M and 0.26M, respectively. Among the compressed models, DenseNet40 has the highest accuracy and the smallest parameters. The best model (DenseNet40-80%-T) is pruned 75.70% of the parameters and cut off 65.52% of the computations, with the model size being only 2.2 MB. Compared with the version of compression after transfer learning, the accuracy of the model is improved by 0.74%. We further develop a cotton disease recognition APP on the Android platform based on the model and on the test phone, the average time to identify a single image is just 87ms.

An Intelligent Approach for Cotton Plant Disease Detection using Convolutional Neural Networks: A Deep Learning Perspective

“…The prosperity of cotton farming is essential to the welfare of farming communities because it guarantees food security, economic stability, and the textile industry's continuous expansion [9]. Cotton crops are a mainstay of the agricultural landscape, and their production and health have a direct impact on wider socioeconomic variables [10] [11]. As such, maintaining this agricultural cornerstone depends on the efficient diagnosis and management of illnesses.…”

MS-Net: a novel lightweight and precise model for plant disease identification

“…Although the aforementioned studies tend to favor relatively lightweight network models, it is still difficult to achieve an ideal balance between accuracy and size. These studies generally use complex networks or fused networks to achieve higher accuracy and employ network compression techniques to reduce the model’s parameter count( Wang et al., 2021 ; Zhao et al., 2022 ; Zhu et al., 2022 ). However, network compression is a highly challenging task, making it difficult to effectively balance accuracy and latency( Hinton et al., 2015 ; Garg et al., 2023 ).…”