DFF-ResNet: An insect pest recognition model based on residual networks

Abstract

Insect pest control is considered as a significant factor in the yield of commercial crops. Thus, to avoid economic losses, we need a valid method for insect pest recognition. In this paper, we proposed a feature fusion residual block to perform the insect pest recognition task. Based on the original residual block, we fused the feature from a previous layer between two 1 1 convolution layers in a residual signal branch to improve the capacity of the block. Furthermore, we explored the contribution of each residual group to the model performance. We found that adding the residual blocks of earlier residual groups promotes the model performance significantly, which improves the capacity of generalization of the model. By stacking the feature fusion residual block, we constructed the Deep Feature Fusion Residual Network (DFF-ResNet). To prove the validity and adaptivity of our approach, we constructed it with two common residual networks (Pre-ResNet and Wide Residual Network (WRN)) and validated these models on the Canadian Institute For Advanced Research (CIFAR) and Street View House Number (SVHN) benchmark datasets. The experimental results indicate that our models have a lower test error than those of baseline models. Then, we applied our models to recognize insect pests and obtained validity on the IP102 benchmark dataset. The experimental results show that our models outperform the original ResNet and other state-of-the-art methods.

A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model

“…Therefore, the development of open, standardized, and intelligent [ 3 ] green tea classifications and identification methods is an inevitable trend. New classification and assessment methods for green tea have been emerging, such as physicochemical review methods [ 4 , 5 ], fingerprinting assessment methods [ 6 , 7 ], intelligent sensory review methods [ 8 , 9 ], and infrared spectral imaging technology detection methods [ 10 , 11 ], but these methods have their limitations to a certain extent, such as relevant instruments and cumbersome and complicated operations, and most of them are based on the overall tea leaves. It is necessary to propose an objective, simple, fast, and low-cost method for green tea classification, since most of them are based on the whole tea leaves for review, which requires specific and time-consuming requirements.…”

“…Convolutional neural networks, as an important member of image classification algorithms, have the advantages of high recognition accuracy, fast detection speed, and great development potential [ 12 ], have achieved considerable success in image classification [ 13 ], object detection [ 14 ], pose estimation [ 15 ], image segmentation [ 16 ], and face recognition [ 17 , 18 ], have great scaling advantages [ 19 ], and have been widely used in agriculture [ 20 ], healthcare [ 21 ], education [ 22 ], energy [ 23 ], industrial inspection [ 24 ], and other fields [ 25 ]. Currently, convolutional neural networks have been used for tea tree pest and disease identification [ 26 ], tea grade sieving [ 7 ], and the sorting of tea tree fresh leaves [ 8 ], but for the recognition and classification of different species of green tea based on ResNet, a typical convolutional neural network is proposed by researchers in recent years to perform computer vision tasks, which minimizes the gradient disappearance problem caused by increasing the depth of the network due to the introduction of the residual module and reduces the redundancy of information in the data while maintaining a high accuracy rate, which is simple and practical.…”

Graph neural architecture search: A survey

“…Deep learning (DL) models have achieved superior performance in fields such as computer vision [1,2] , natural language processing [3][4][5] , and speech recognition [6,7] because of their ability to capture hidden patterns and leverage the statistical properties of data. DL models, e.g., recurrent neural networks (RNNs) and convolutional neural networks (CNNs), have been widely used by researchers for machine learning (ML) tasks and have solved a wide variety of problems in many cross disciplines, including but not limited to medicine [8,9] , agriculture [10] , commerce [11,12] , and finance [13] . However, these models, which are predominantly applied to data represented as a regular grid in the Euclidean space, fail to extract latent representations from graph data.…”

Data-Driven Channel Pruning towards Local Binary Convolution Inverse Bottleneck Network Based on Squeeze-and-Excitation Optimization Weights

“…In recent studies, the attention mechanism has been widely used in Convolutional Neural Network (CNN). From the spatial attention mechanism [31][32][33][34][35][36], channel attention mechanism [37], and part of the research used a mixed attention mechanism [38][39][40]. The attention mechanism itself calculates the features extracted by the model and adds more weight to the more important features of the result.…”