Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors

• URL: http://arxiv.org/abs/2011.14306v4
• Date: Mon, 22 Feb 2021 16:44:55 GMT
• Title: Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors
• Authors: Ryoya Katafuchi, Terumasa Tokunaga
• Abstract summary: We propose an unsupervised anomaly detection technique for image-based plant disease diagnosis. Our proposed method includes a new image-based framework for plant disease detection that utilizes a conditional adversarial network called pix2pix. Experiments with PlantVillage dataset demonstrated the superiority of our proposed method compared to an existing anomaly detector.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes an unsupervised anomaly detection technique for image-based plant disease diagnosis. The construction of large and publicly available datasets containing labeled images of healthy and diseased crop plants led to growing interest in computer vision techniques for automatic plant disease diagnosis. Although supervised image classifiers based on deep learning can be a powerful tool for plant disease diagnosis, they require a huge amount of labeled data. The data mining technique of anomaly detection includes unsupervised approaches that do not require rare samples for training classifiers. We propose an unsupervised anomaly detection technique for image-based plant disease diagnosis that is based on the reconstructability of colors; a deep encoder-decoder network trained to reconstruct the colors of \textit{healthy} plant images should fail to reconstruct colors of symptomatic regions. Our proposed method includes a new image-based framework for plant disease detection that utilizes a conditional adversarial network called pix2pix and a new anomaly score based on CIEDE2000 color difference. Experiments with PlantVillage dataset demonstrated the superiority of our proposed method compared to an existing anomaly detector at identifying diseased crop images in terms of accuracy, interpretability and computational efficiency.

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)
• Hierarchical Salient Patch Identification for Interpretable Fundus Disease Localization [4.714335699701277]
  We propose a weakly supervised interpretable fundus disease localization method called hierarchical salient patch identification (HSPI) HSPI can achieve interpretable disease localization using only image-level labels and a neural network classifier (NNC) We conduct disease localization experiments on fundus image datasets and achieve the best performance on multiple evaluation metrics compared to previous interpretable attribution methods.
  arXiv  Detail & Related papers  (2024-05-23T09:07:21Z)
• Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image [63.59114880750643]
  We introduce a novel Spatial-aware Attention Generative Adrialversa Network (SAGAN) for one-class semi-supervised generation of health images. SAGAN generates high-quality health images corresponding to unlabeled data, guided by the reconstruction of normal images and restoration of pseudo-anomaly images. Extensive experiments on three medical datasets demonstrate that the proposed SAGAN outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-05-21T15:41:34Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Improving FHB Screening in Wheat Breeding Using an Efficient Transformer Model [0.0]
  Fusarium head blight is a devastating disease that causes significant economic losses annually on small grains. Image processing techniques have been developed using supervised machine learning algorithms for the early detection of FHB. A new Context Bridge is proposed to integrate the local representation capability of the U-Net network in the transformer model.
  arXiv  Detail & Related papers  (2023-08-07T15:44:58Z)
• Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images [0.0]
  Ophthalmic images may contain identical-looking pathologies that can cause failure in automated techniques to distinguish different retinal degenerative diseases. In this work, we propose a robust disease detection architecture with three learning heads. Our experimental results on two publicly available OCT datasets illustrate that the proposed model outperforms existing state-of-the-art models in terms of accuracy, interpretability, and robustness for out-of-distribution retinal disease detection.
  arXiv  Detail & Related papers  (2022-06-24T07:59:36Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  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.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• A Novel CropdocNet for Automated Potato Late Blight Disease Detection from the Unmanned Aerial Vehicle-based Hyperspectral Imagery [3.3283767441645478]
  Late blight disease is one of the most destructive diseases in potato crop, leading to serious yield losses globally. Current farm practices in crop disease diagnosis are based on manual visual inspection, which is costly, time consuming, subject to individual bias. Recent advances in imaging sensors (e.g. RGB, multiple spectral and hyperspectral cameras), remote sensing and machine learning offer the opportunity to address this challenge.
  arXiv  Detail & Related papers  (2021-07-28T11:18:48Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.