DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification

Abstract

The identification of corn leaf diseases in a real field environment faces several difficulties, such as complex background disturbances, variations and irregularities in the lesion areas, and large intra-class and small inter-class disparities. Traditional Convolutional Neural Network (CNN) models have a low recognition accuracy and a large number of parameters. In this study, a lightweight corn disease identification model called DFCANet (Double Fusion block with Coordinate Attention Network) is proposed. The DFCANet consists mainly of two components: The dual feature fusion with coordinate attention and the Down-Sampling (DS) modules. The DFCA block contains dual feature fusion and Coordinate Attention (CA) modules. In order to completely fuse the shallow and deep features, these features were fused twice. The CA module suppresses the background noise and focuses on the diseased area. In addition, the DS module is used for down-sampling. It reduces the loss of information by expanding the feature channel dimension and the Depthwise convolution. The results show that DFCANet has an average recognition accuracy of 98.47%. It is more efficient at identifying corn leaf diseases in real scene images, compared with VGG16 (96.63%), ResNet50 (93.27%), EffcientNet-B0 (97.24%), ConvNeXt-B (94.18%), DenseNet121 (95.71%), MobileNet-V2 (95.41%), MobileNetv3-Large (96.33%), and ShuffleNetV2-1.0× (94.80%) methods. Moreover, the model’s Params and Flops are 1.91M and 309.1M, respectively, which are lower than heavyweight network models and most lightweight network models. In general, this study provides a novel, lightweight, and efficient convolutional neural network model for corn disease identification.

Improved EfficientNet for corn disease identification

“…Meanwhile, CNNs have shown better recognition performance in datasets with simple backgrounds ( Ahila Priyadharshini et al., 2019 ; Lv et al., 2020 ; Yin et al., 2022 ), but the robustness of CNNs decreases when they are applied to corn disease identification in real environments ( Zeng et al., 2022a ). Moreover, our image dataset is similar to the studies of Chen et al. (2022) and Zeng et al.…”

“…Lin et al (2022a) proposed a lightweight CNN model called GrapeNet for recognizing specific grape diseases at different symptom stages, and the experimental results showed that the recognition accuracy for seven classes of grape diseases was 97.85%. Chen et al (2022) proposed a lightweight CNN model, DFCANet, for recognizing corn diseases in real environments, and the experimental results showed that the classification accuracy for six classes of corn diseases reached 98.47%.…”

LFMNet: a lightweight model for identifying leaf diseases of maize with high similarity

“…The recognition accuracy of the model is 98.5% on the open corn dataset with simple background and 95.86% on the local corn disease dataset with complex background. Chen Y. et al. (2022) proposed a lightweight corn disease recognition model called DFCANet, which relies on dual-feature fusion and downsampling module fusion of deep and shallow features, suppressing background noise and focusing on the lesion area.…”

CSIU-Net+: Pepper and corn leaves classification and severity identification using hybrid optimization

“…Recently, artificial intelligence (AI) based machine learning (ML) and deep learning (DL) techniques have played a vital part in the agricultural field, especially in the disease detection process [13,14]. Recently, ML-based techniques like k-nearest neighbor (KNN), multilayer perceptron (MLP), decision tree (DT), artificial neural network (ANN), and random forest (RF), and have been introduced in several studies to recognize crop leaf diseases effectually [15]. Despite the current practices performing well, there are limitations like low precision, high computational complexity, and deprived generalization ability.…”