An Efficient Insect Pest Classification Using Multiple Convolutional Neural Network Based Models

• URL: http://arxiv.org/abs/2107.12189v1
• Date: Mon, 26 Jul 2021 12:53:28 GMT
• Title: An Efficient Insect Pest Classification Using Multiple Convolutional Neural Network Based Models
• Authors: Hieu T. Ung, Huy Q. Ung, Binh T. Nguyen
• Abstract summary: Insect pest classification is a difficult task because of various kinds, scales, shapes, complex backgrounds in the field, and high appearance similarity among insect species. We present different convolutional neural network-based models in this work, including attention, feature pyramid, and fine-grained models. The experimental results show that combining these convolutional neural network-based models can better perform than the state-of-the-art methods on these two datasets.
• Score: 0.3222802562733786
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate insect pest recognition is significant to protect the crop or take the early treatment on the infected yield, and it helps reduce the loss for the agriculture economy. Design an automatic pest recognition system is necessary because manual recognition is slow, time-consuming, and expensive. The Image-based pest classifier using the traditional computer vision method is not efficient due to the complexity. Insect pest classification is a difficult task because of various kinds, scales, shapes, complex backgrounds in the field, and high appearance similarity among insect species. With the rapid development of deep learning technology, the CNN-based method is the best way to develop a fast and accurate insect pest classifier. We present different convolutional neural network-based models in this work, including attention, feature pyramid, and fine-grained models. We evaluate our methods on two public datasets: the large-scale insect pest dataset, the IP102 benchmark dataset, and a smaller dataset, namely D0 in terms of the macro-average precision (MPre), the macro-average recall (MRec), the macro-average F1- score (MF1), the accuracy (Acc), and the geometric mean (GM). The experimental results show that combining these convolutional neural network-based models can better perform than the state-of-the-art methods on these two datasets. For instance, the highest accuracy we obtained on IP102 and D0 is $74.13\%$ and $99.78\%$, respectively, bypassing the corresponding state-of-the-art accuracy: $67.1\%$ (IP102) and $98.8\%$ (D0). We also publish our codes for contributing to the current research related to the insect pest classification problem.

Related papers

• Unleashing the Power of Transfer Learning Model for Sophisticated Insect Detection: Revolutionizing Insect Classification [0.520707246175575]
  This study uses different models like MobileNetV2, ResNet152V2, Xecption, Custom CNN. A Convolutional Neural Network (CNN) based on the ResNet152V2 architecture is constructed and evaluated in this work. The results highlight its potential for real-world applications in insect classification and entomology studies.
  arXiv  Detail & Related papers  (2024-06-11T20:52:42Z)
• Low Cost Machine Vision for Insect Classification [33.7054351451505]
  We present an imaging method as part of a multisensor system developed as a low-cost, scalable, open-source system. The system is evaluated exemplarily on a dataset consisting of 16 insect species of the same as well as different genus, family and order. It was proved that image cropping of insects is necessary for classification of species with high inter-class similarity.
  arXiv  Detail & Related papers  (2024-04-26T15:43:24Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification [61.656149405657246]
  Domain adaptation is effective in image classification tasks where obtaining sufficient label data is challenging. We propose a novel method, named SELDA, for stacking ensemble learning via extending three domain adaptation methods. The experimental results using Age-Related Eye Disease Study (AREDS) benchmark ophthalmic dataset demonstrate the effectiveness of the proposed model.
  arXiv  Detail & Related papers  (2022-09-27T14:19:00Z)
• A fast accurate fine-grain object detection model based on YOLOv4 deep neural network [0.0]
  Early identification and prevention of various plant diseases in commercial farms and orchards is a key feature of precision agriculture technology. This paper presents a high-performance real-time fine-grain object detection framework that addresses several obstacles in plant disease detection. The proposed model is built on an improved version of the You Only Look Once (YOLOv4) algorithm.
  arXiv  Detail & Related papers  (2021-10-30T17:56:13Z)
• Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification [0.36700088931938835]
  This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with illumination correction for improved classification. The proposed architecture achieves 99.30% accuracy with a model size of 9.60MB and 4.87M floating-point operations.
  arXiv  Detail & Related papers  (2021-09-06T12:14:02Z)
• High performing ensemble of convolutional neural networks for insect pest image detection [124.23179560022761]
  Pest infestation is a major cause of crop damage and lost revenues worldwide. We generate ensembles of CNNs based on different topologies. Two new Adam algorithms for deep network optimization are proposed.
  arXiv  Detail & Related papers  (2021-08-28T00:49:11Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.