Low Cost Machine Vision for Insect Classification

• URL: http://arxiv.org/abs/2404.17488v1
• Date: Fri, 26 Apr 2024 15:43:24 GMT
• Title: Low Cost Machine Vision for Insect Classification
• Authors: Danja Brandt, Martin Tschaikner, Teodor Chiaburu, Henning Schmidt, Ilona Schrimpf, Alexandra Stadel, Ingeborg E. Beckers, Frank Haußer,
• Abstract summary: 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.
• Score: 33.7054351451505
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Preserving the number and diversity of insects is one of our society's most important goals in the area of environmental sustainability. A prerequisite for this is a systematic and up-scaled monitoring in order to detect correlations and identify countermeasures. Therefore, automatized monitoring using live traps is important, but so far there is no system that provides image data of sufficient detailed information for entomological classification. In this work, we present an imaging method as part of a multisensor system developed as a low-cost, scalable, open-source system that is adaptable to classical trap types. The image quality meets the requirements needed for classification in the taxonomic tree. Therefore, illumination and resolution have been optimized and motion artefacts have been suppressed. The system is evaluated exemplarily on a dataset consisting of 16 insect species of the same as well as different genus, family and order. We demonstrate that standard CNN-architectures like ResNet50 (pretrained on iNaturalist data) or MobileNet perform very well for the prediction task after re-training. Smaller custom made CNNs also lead to promising results. Classification accuracy of $>96\%$ has been achieved. Moreover, it was proved that image cropping of insects is necessary for classification of species with high inter-class similarity.

Related papers

• Fine-grained Recognition with Learnable Semantic Data Augmentation [68.48892326854494]
  Fine-grained image recognition is a longstanding computer vision challenge. We propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Our method significantly improves the generalization performance on several popular classification networks.
  arXiv  Detail & Related papers  (2023-09-01T11:15:50Z)
• Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classification [10.423464288613275]
  We present a deep learning pipeline for analyzing images captured by a moth scanner. We first localize individuals with a moth detector and afterward determine the species of detected insects. Our detector achieves up to 99.01% mean average precision and our classifier distinguishes 200 moth species with an accuracy of 93.13% on image cutouts.
  arXiv  Detail & Related papers  (2023-07-28T09:22:09Z)
• Facilitated machine learning for image-based fruit quality assessment in developing countries [68.8204255655161]
  Automated image classification is a common task for supervised machine learning in food science. We propose an alternative method based on pre-trained vision transformers (ViTs) It can be easily implemented with limited resources on a standard device.
  arXiv  Detail & Related papers  (2022-07-10T19:52:20Z)
• A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes [58.633364000258645]
  We call this dataset RIVAL10 consisting of roughly $26k$ instances over $10$ classes. We evaluate the sensitivity of a broad set of models to noise corruptions in foregrounds, backgrounds and attributes. In our analysis, we consider diverse state-of-the-art architectures (ResNets, Transformers) and training procedures (CLIP, SimCLR, DeiT, Adversarial Training)
  arXiv  Detail & Related papers  (2022-01-26T06:31:28Z)
• A deep convolutional neural network for classification of Aedes albopictus mosquitoes [1.6758573326215689]
  We introduce the application of two Deep Convolutional Neural Networks in a comparative study to automate the classification task. We use the transfer learning principle to train two state-of-the-art architectures on the data provided by the Mosquito Alert project. In addition, we applied explainable models based on the Grad-CAM algorithm to visualise the most discriminant regions of the classified images.
  arXiv  Detail & Related papers  (2021-10-29T17:58:32Z)
• 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)
• An Efficient Insect Pest Classification Using Multiple Convolutional Neural Network Based Models [0.3222802562733786]
  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.
  arXiv  Detail & Related papers  (2021-07-26T12:53:28Z)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)
• Automatic image-based identification and biomass estimation of invertebrates [70.08255822611812]
  Time-consuming sorting and identification of taxa pose strong limitations on how many insect samples can be processed. We propose to replace the standard manual approach of human expert-based sorting and identification with an automatic image-based technology. We use state-of-the-art Resnet-50 and InceptionV3 CNNs for the classification task.
  arXiv  Detail & Related papers  (2020-02-05T21:38:57Z)

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.