Javascript must be enabled to continue!
Classification of beetle type using the Convolutional Neural Network algorithm
View through CrossRef
Beetles (Order Coleoptera) are the largest order of animals. Beetles are a group of insects that make up the order Coleoptera. Estimates of the total number of living beetle species are millions of beetle species whose features make it difficult to visually identify beetle species. Currently, the beetle classification process is still carried out using direct observation and personal assumptions. CNN model ResNet50 is one of the ResNet variants that has 50 layers and VGG16 is a CNN model that utilizes a convolutional layer with a small convolutional filter specification (3×3) and always uses the same padding and maxpool layers of a 2x2 filter. In this Algorithm (CNN) with the ResNet50 model, it succeeded in exploring beetles with accuracy, precision, recall and F-1 Score with values of 93%, 94.24%, 89.28%, 91.69%, while the VGG16 model succeeded in conducting research on beetle species with accuracy, precision, recall and F-1 Score with values of 86.9%, 87.5%, 87%, 87.2%, so it can be said that the classification of beetle species using the CNN algorithm with the ResNet50 model is better than the VGG16 model.
Politeknik Ganesha
Title: Classification of beetle type using the Convolutional Neural Network algorithm
Description:
Beetles (Order Coleoptera) are the largest order of animals.
Beetles are a group of insects that make up the order Coleoptera.
Estimates of the total number of living beetle species are millions of beetle species whose features make it difficult to visually identify beetle species.
Currently, the beetle classification process is still carried out using direct observation and personal assumptions.
CNN model ResNet50 is one of the ResNet variants that has 50 layers and VGG16 is a CNN model that utilizes a convolutional layer with a small convolutional filter specification (3×3) and always uses the same padding and maxpool layers of a 2x2 filter.
In this Algorithm (CNN) with the ResNet50 model, it succeeded in exploring beetles with accuracy, precision, recall and F-1 Score with values of 93%, 94.
24%, 89.
28%, 91.
69%, while the VGG16 model succeeded in conducting research on beetle species with accuracy, precision, recall and F-1 Score with values of 86.
9%, 87.
5%, 87%, 87.
2%, so it can be said that the classification of beetle species using the CNN algorithm with the ResNet50 model is better than the VGG16 model.
Related Results
Graph convolutional neural networks for 3D data analysis
Graph convolutional neural networks for 3D data analysis
(English) Deep Learning allows the extraction of complex features directly from raw input data, eliminating the need for hand-crafted features from the classical Machine Learning p...
Identification of Acral Melanoma using Genetic Algorithms Compared with Convolutional Neural Network using Dermoscopic Images
Identification of Acral Melanoma using Genetic Algorithms Compared with Convolutional Neural Network using Dermoscopic Images
Aim: Identification of acral melanoma using genetic algorithm compared with convolutional neural network CNN using dermoscopic images. Materials and Methods: The study was conducte...
Neural Networks for Quality Sorting of Agricultural Produce
Neural Networks for Quality Sorting of Agricultural Produce
The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue Unive...
Modified neural networks for rapid recovery of tokamak plasma parameters for real time control
Modified neural networks for rapid recovery of tokamak plasma parameters for real time control
Two modified neural network techniques are used for the identification of the equilibrium plasma parameters of the Superconducting Steady State Tokamak I from external magnetic mea...
Analog Convolutional Operator Circuit for Low-Power Mixed-Signal CNN Processing Chip
Analog Convolutional Operator Circuit for Low-Power Mixed-Signal CNN Processing Chip
In this paper, we propose a compact and low-power mixed-signal approach to implementing convolutional operators that are often responsible for most of the chip area and power consu...
Endemic Jeffrey Pine Beetle Associates: Beetle/Mite Fungal Dissemination Strategies and Interactions That May Influence Beetle Population Levels
Endemic Jeffrey Pine Beetle Associates: Beetle/Mite Fungal Dissemination Strategies and Interactions That May Influence Beetle Population Levels
Fungal and mite associates may drive changes in bark beetle populations, and mechanisms constraining beetle irruptions may be hidden in endemic populations. We characterized common...
Optimising tool wear and workpiece condition monitoring via cyber-physical systems for smart manufacturing
Optimising tool wear and workpiece condition monitoring via cyber-physical systems for smart manufacturing
Smart manufacturing has been developed since the introduction of Industry 4.0. It consists of resource sharing and networking, predictive engineering, and material and data analyti...
A classification method of underwater target radiated noise signals based on enhanced images and convolutional neural networks
A classification method of underwater target radiated noise signals based on enhanced images and convolutional neural networks
As the economy and society continue to develop, the range of underwater vehicles is expanding and technology is constantly being upgraded. Consequently, it is becoming increasingly...

