Explainability of Deep Learning-Based Plant Disease Classifiers Through Automated Concept Identification

• URL: http://arxiv.org/abs/2412.07408v1
• Date: Tue, 10 Dec 2024 10:59:43 GMT
• Title: Explainability of Deep Learning-Based Plant Disease Classifiers Through Automated Concept Identification
• Authors: Jihen Amara, Birgitta König-Ries, Sheeba Samuel,
• Abstract summary: We apply the Automated Concept-based Explanation (ACE) method to plant disease classification. ACE automatically identifies the visual concepts found in the image data and provides insights about the critical features influencing the model predictions. Our findings demonstrate the potential of ACE to improve the explainability of plant disease classification based on deep learning.
• Score: 0.0
• License:
• Abstract: While deep learning has significantly advanced automatic plant disease detection through image-based classification, improving model explainability remains crucial for reliable disease detection. In this study, we apply the Automated Concept-based Explanation (ACE) method to plant disease classification using the widely adopted InceptionV3 model and the PlantVillage dataset. ACE automatically identifies the visual concepts found in the image data and provides insights about the critical features influencing the model predictions. This approach reveals both effective disease-related patterns and incidental biases, such as those from background or lighting that can compromise model robustness. Through systematic experiments, ACE helped us to identify relevant features and pinpoint areas for targeted model improvement. Our findings demonstrate the potential of ACE to improve the explainability of plant disease classification based on deep learning, which is essential for producing transparent tools for plant disease management in agriculture.

Related papers

• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Gastrointestinal Disease Classification through Explainable and Cost-Sensitive Deep Neural Networks with Supervised Contrastive Learning [0.0]
  This paper introduces a novel approach on classifying gastrointestinal diseases by leveraging cost-sensitive pre-trained deep convolutional neural network (CNN) architectures with supervised contrastive learning. Our approach enables the network to learn representations that capture vital disease-related features, while also considering the relationships of similarity between samples.
  arXiv  Detail & Related papers  (2023-07-14T19:56:30Z)
• Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping [59.0626764544669]
  In this study, we use Deep Learning methods to semantically segment grapevine leaves images in order to develop an automated object detection system for leaf phenotyping. Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified.
  arXiv  Detail & Related papers  (2022-10-24T14:37:09Z)
• Explainable vision transformer enabled convolutional neural network for plant disease identification: PlantXViT [11.623005206620498]
  Plant diseases are the primary cause of crop losses globally, with an impact on the world economy. In this study, a Vision Transformer enabled Convolutional Neural Network model called "PlantXViT" is proposed for plant disease identification. The proposed model has a lightweight structure with only 0.8 million trainable parameters, which makes it suitable for IoT-based smart agriculture services.
  arXiv  Detail & Related papers  (2022-07-16T12:05:06Z)
• 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)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• 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)
• 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)
• Achieving Explainability for Plant Disease Classification with Disentangled Variational Autoencoders [0.0]
  Knowing the logic or features used in decision making, such as in a classification task, is very important for verification, algorithm improvement, training data improvement, knowledge extraction, etc. We developed a classification method based on a variational autoencoder architecture that can show not only the location of the most important features but also what variations of that particular feature are used. Although the proposed method was tested for disease diagnosis in some crops, the method can be extended to other crops as well as other image classification tasks.
  arXiv  Detail & Related papers  (2021-02-05T10:04:00Z)
• Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review [0.2793095554369281]
  Plant diseases are considered one of the main factors influencing food production and minimize losses in production. Deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. This work presents a systematic review of the literature that aims to identify the state of the art of the use of convolutional neural networks in the process of identification and classification of plant diseases.
  arXiv  Detail & Related papers  (2020-09-09T15:36:04Z)
• 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.