Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging

• URL: http://arxiv.org/abs/2112.11242v1
• Date: Mon, 20 Dec 2021 13:29:13 GMT
• Title: Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging
• Authors: Alessandro Benfenati, Paola Causin, Roberto Oberti, Giovanni Stefanello
• Abstract summary: This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
• Score: 63.62764375279861
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objectives. Sustainable management of plant diseases is an open challenge which has relevant economic and environmental impact. Optimal strategies rely on human expertise for field scouting under favourable conditions to assess the current presence and extent of disease symptoms. This labor-intensive task is complicated by the large field area to be scouted, combined with the millimeter-scale size of the early symptoms to be detected. In view of this, image-based detection of early disease symptoms is an attractive approach to automate this process, enabling a potential high throughput monitoring at sustainable costs. Methods. Deep learning has been successfully applied in various domains to obtain an automatic selection of the relevant image features by learning filters via a training procedure. Deep learning has recently entered also the domain of plant disease detection: following this idea, in this work we present a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data and we propose the use of autoencoder architectures to investigate two strategies for disease detection: i) clusterization of features in a compressed space; ii) anomaly detection. Results. The two proposed approaches have been assessed by quantitative indices. The clusterization approach is not fully capable by itself to provide accurate predictions but it does cater relevant information. Anomaly detection has instead a significant potential of resolution which could be further exploited as a prior for supervised architectures with a very limited number of labeled samples.

Related papers

• Small data deep learning methodology for in-field disease detection [6.2747249113031325]
  We present the first machine learning model capable of detecting mild symptoms of late blight in potato crops. Our proposal exploits the availability of high-resolution images via the concept of patching, and is based on deep convolutional neural networks with a focal loss function. Our model correctly detects all cases of late blight in the test dataset, demonstrating a high level of accuracy and effectiveness in identifying early symptoms.
  arXiv  Detail & Related papers  (2024-09-25T17:31:17Z)
• Exploring Deep Learning Techniques for Glaucoma Detection: A Comprehensive Review [0.0]
  Glaucoma is one of the primary causes of vision loss around the world. Recent developments in deep learning approaches demonstrate potential in automating glaucoma detection. The use of deep learning algorithms may significantly improve the efficacy, usefulness, and accuracy of glaucoma detection.
  arXiv  Detail & Related papers  (2023-11-02T17:39:40Z)
• Deep Learning and Computer Vision for Glaucoma Detection: A Review [0.8379286663107844]
  Glaucoma is the leading cause of irreversible blindness worldwide. Recent advances in computer vision and deep learning have demonstrated the potential for automated assessment. We survey recent studies on AI-based glaucoma diagnosis using fundus, optical coherence tomography, and visual field images.
  arXiv  Detail & Related papers  (2023-07-31T09:49:51Z)
• Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges [58.32937972322058]
  "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic.
  arXiv  Detail & Related papers  (2023-07-30T16:08:45Z)
• Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection [10.953610196636784]
  We propose a Contour Attention Preserving (CAP) method based on lung cavity edge extraction. Experimental results indicate that the proposed method achieves state-of-the-art performance in multiple adversarial defense and generalization tasks.
  arXiv  Detail & Related papers  (2022-11-30T08:01:23Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)
• Deep Semi-supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection [49.78612417406883]
  We propose a novel semi-supervised deep metric learning method for cervical cancer cell detection. Our model learns an embedding metric space and conducts dual alignment of semantic features on both the proposal and prototype levels. We construct a large-scale dataset for semi-supervised cervical cancer cell detection for the first time, consisting of 240,860 cervical cell images.
  arXiv  Detail & Related papers  (2021-04-07T17:11:27Z)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

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.