Crop insect pest detection based on dilated multi-scale attention U-Net.

BACKGROUND: Crop pests seriously affect the yield and quality of crops. Accurately and rapidly detecting and segmenting insect pests in crop leaves is a premise for effectively controlling insect pests. METHODS: Aiming at the detection problem of irregular multi-scale insect pests in the field, a dilated multi-scale attention U-Net (DMSAU-Net) model is constructed for crop insect pest detection. In its encoder, dilated Inception is designed to replace the convolution layer in U-Net to extract the multi-scale features of insect pest images. An attention module is added to its decoder to focus on the edge of the insect pest image. RESULTS: The experiments on the crop insect pest image IP102 dataset are implemented, and achieved the detection accuracy of 92.16% and IoU of 91.2%, which is 3.3% and 1.5% higher than that of MSR-RCNN, respectively. CONCLUSION: The results indicate that the proposed method is effective as a new insect pest detection method. The dilated Inception can improve the accuracy of the model, and the attention module can reduce the noise generated by upsampling and accelerate model convergence. It can be concluded that the proposed method can be applied to practical crop insect pest monitoring system.

Crop pest detection by three-scale convolutional neural network with attention.

Crop pests seriously affect the yield and quality of crop. To timely and accurately control crop pests is particularly crucial for crop security, quality of life and a stable agricultural economy. Crop pest detection in field is an essential step to control the pests. The existing convolutional neural network (CNN) based pest detection methods are not satisfactory for small pest recognition and detection in field because the pests are various with different colors, shapes and poses. A three-scale CNN with attention (TSCNNA) model is constructed for crop pest detection by adding the channel attention and spatial mechanisms are introduced into CNN. TSCNNA can improve the interest of CNN for pest detection with different sizes under complicated background, and enlarge the receptive field of CNN, so as to improve the accuracy of pest detection. Experiments are carried out on the image set of common crop pests, and the precision is 93.16%, which is 5.1% and 3.7% higher than ICNN and VGG16, respectively. The results show that the proposed method can achieve both high speed and high accuracy of crop pest detection. This proposed method has certain practical significance of real-time crop pest control in the field.

MVNMDA: A Multi-View Network Combing Semantic and Global Features for Predicting miRNA-Disease Association.

A growing body of experimental evidence suggests that microRNAs (miRNAs) are closely associated with specific human diseases and play critical roles in their development and progression. Therefore, identifying miRNA related to specific diseases is of great significance for disease screening and treatment. In the early stages, the identification of associations between miRNAs and diseases demanded laborious and time-consuming biological experiments that often carried a substantial risk of failure. With the exponential growth in the number of potential miRNA-disease association combinations, traditional biological experimental methods face difficulties in processing massive amounts of data. Hence, developing more efficient computational methods to predict possible miRNA-disease associations and prioritize them is particularly necessary. In recent years, numerous deep learning-based computational methods have been developed and have demonstrated excellent performance. However, most of these methods rely on external databases or tools to compute various auxiliary information. Unfortunately, these external databases or tools often cover only a limited portion of miRNAs and diseases, resulting in many miRNAs and diseases being unable to match with these computational methods. Therefore, there are certain limitations associated with the practical application of these methods. To overcome the above limitations, this study proposes a multi-view computational model called MVNMDA, which predicts potential miRNA-disease associations by integrating features of miRNA and diseases from local views, global views, and semantic views. Specifically, MVNMDA utilizes known association information to construct node initial features. Then, multiple networks are constructed based on known association to extract low-dimensional feature embedding of all nodes. Finally, a cascaded attention classifier is proposed to fuse features from coarse to fine, suppressing noise within the features and making precise predictions. To validate the effectiveness of the proposed method, extensive experiments were conducted on the HMDD v2.0 and HMDD v3.2 datasets. The experimental results demonstrate that MVNMDA achieves better performance compared to other computational methods. Additionally, the case study results further demonstrate the reliable predictive performance of MVNMDA.

Dilated convolution capsule network for apple leaf disease identification.

Accurate and rapid identification of apple leaf diseases is the basis for preventing and treating apple diseases. However, it is challenging to identify apple leaf diseases due to their various symptoms, different colors, irregular shapes, uneven sizes, and complex backgrounds. To reduce computational cost and improve training results, a dilated convolution capsule network (DCCapsNet) is constructed for apple leaf disease identification based on a capsule network (CapsNet) and two dilated Inception modules with different dilation rates. The network can obtain multi-scale deep-level features to improve the classification capability of the model. The dynamic routing algorithm is used between the front and back layers of CapsNet to make the model converge quickly. In DCCapsNet, dilated Inception instead of traditional convolution is used to increase the convolution receptive fields and extract multi-scale features from disease leaf images, and CapsNet is used to capture the classification features of changeable disease leaves and overcome the overfitting problem in the training network. Extensive experiment results on the apple disease leaf image dataset demonstrate that the proposed method can effectively identify apple diseases. The method can realize the rapid and accurate identification of apple leaf disease.