NemaNet: A convolutional neural network model for identification of nematodes soybean crop in brazil

• URL: http://arxiv.org/abs/2103.03717v1
• Date: Fri, 5 Mar 2021 14:47:00 GMT
• Title: NemaNet: A convolutional neural network model for identification of nematodes soybean crop in brazil
• Authors: Andre da Silva Abade, Lucas Faria Porto, Paulo Afonso Ferreira, Flavio de Barros Vidal
• Abstract summary: Phytoparasitic nematodes (or phytonematodes) are causing severe damage to crops and generating large-scale economic losses worldwide. This work presents a new public data set called NemaDataset containing 3,063 microscopic images from five nematode species with the most significant damage relevance for the soybean crop.
• Score: 0.43968605222413054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Phytoparasitic nematodes (or phytonematodes) are causing severe damage to crops and generating large-scale economic losses worldwide. In soybean crops, annual losses are estimated at 10.6% of world production. Besides, identifying these species through microscopic analysis by an expert with taxonomy knowledge is often laborious, time-consuming, and susceptible to failure. In this perspective, robust and automatic approaches are necessary for identifying phytonematodes capable of providing correct diagnoses for the classification of species and subsidizing the taking of all control and prevention measures. This work presents a new public data set called NemaDataset containing 3,063 microscopic images from five nematode species with the most significant damage relevance for the soybean crop. Additionally, we propose a new Convolutional Neural Network (CNN) model defined as NemaNet and a comparative assessment with thirteen popular models of CNNs, all of them representing the state of the art classification and recognition. The general average calculated for each model, on a from-scratch training, the NemaNet model reached 96.99% accuracy, while the best evaluation fold reached 98.03%. In training with transfer learning, the average accuracy reached 98.88\%. The best evaluation fold reached 99.34% and achieve an overall accuracy improvement over 6.83% and 4.1%, for from-scratch and transfer learning training, respectively, when compared to other popular models.

Related papers

• Classifier Enhanced Deep Learning Model for Erythroblast Differentiation with Limited Data [0.08388591755871733]
  Hematological disorders, which involve 1% of conditions and genetic diseases, present significant diagnostic challenges. Our approach evaluates various machine learning settings offering efficacy of various machine variety learning (ML) models. When data is available, the proposed solution is a solution for achieving higher accuracy for small and unique datasets.
  arXiv  Detail & Related papers  (2024-11-23T15:51:15Z)
• Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNN [0.24578723416255752]
  This paper proposes a CNN classification network based on Bagging and stacking ensemble learning methods for breast cancer classification. The model is capable of fast and accurate classification of input images. For binary classification (presence or absence of breast cancer), the accuracy reached 98.84%, and for five-class classification, the accuracy reached 98.34%.
  arXiv  Detail & Related papers  (2024-07-15T09:44:43Z)
• Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques [0.0]
  Malaria parasites pose a significant global health burden, causing widespread suffering and mortality. We formulate a deep learning technique for malaria-infected cell classification using traditional CNNs and transfer learning models. The system can be accessed through a web interface, where users can upload blood smear images for malaria detection.
  arXiv  Detail & Related papers  (2024-06-27T16:50:36Z)
• Quantifying Nematodes through Images: Datasets, Models, and Baselines of Deep Learning [3.219431589024008]
  Plant parasitic nematodes cause a significant loss of crops worldwide every year. Computer vision techniques provide a feasible solution for quantifying nematodes or nematode infections. This survey presents the potential state-of-the-art object detection models, training techniques, optimisation techniques, and evaluation metrics for deep learning beginners.
  arXiv  Detail & Related papers  (2024-04-30T17:52:31Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• A Saliency-based Clustering Framework for Identifying Aberrant Predictions [49.1574468325115]
  We introduce the concept of aberrant predictions, emphasizing that the nature of classification errors is as critical as their frequency. We propose a novel, efficient training methodology aimed at both reducing the misclassification rate and discerning aberrant predictions. We apply this methodology to the less-explored domain of veterinary radiology, where the stakes are high but have not been as extensively studied compared to human medicine.
  arXiv  Detail & Related papers  (2023-11-11T01:53:59Z)
• FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation [176.56131810249602]
  evaluating the factuality of long-form text generated by large language models (LMs) is non-trivial. We introduce FACTSCORE, a new evaluation that breaks a generation into a series of atomic facts and computes the percentage of atomic facts supported by a reliable knowledge source.
  arXiv  Detail & Related papers  (2023-05-23T17:06:00Z)
• Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification [71.06194656633447]
  We establish an uncertainty-inspired open-set (UIOS) model, which was trained with fundus images of 9 retinal conditions. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set. UIOS correctly predicted high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images.
  arXiv  Detail & Related papers  (2023-04-08T10:47:41Z)
• 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)
• Automatic Detection and Classification of Tick-borne Skin Lesions using Deep Learning [7.6146285961466]
  This study builds upon the approach used by Burlina et al. by using a variety of convolutional neural network models to detect tick-borne skin lesions. We expanded the data inputs by acquiring images from Google in seven different languages to test if this would diversify training data and improve the accuracy of skin lesion detection.
  arXiv  Detail & Related papers  (2020-11-23T15:16:14Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)

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.