Multi-output Deep-Supervised Classifier Chains for Plant Pathology

• URL: http://arxiv.org/abs/2507.20125v1
• Date: Sun, 27 Jul 2025 04:23:17 GMT
• Title: Multi-output Deep-Supervised Classifier Chains for Plant Pathology
• Authors: Jianping Yao, Son N. Tran,
• Abstract summary: We propose a new model named Multi-Supervised Chains (Mo-DsCC) which weaves the prediction of plant species and disease by chaining the output layers for the two labels.<n>To evaluate the advantages of our model, we perform intensive experiments on two benchmark datasets.
• Score: 12.004640584906666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Plant leaf disease classification is an important task in smart agriculture which plays a critical role in sustainable production. Modern machine learning approaches have shown unprecedented potential in this classification task which offers an array of benefits including time saving and cost reduction. However, most recent approaches directly employ convolutional neural networks where the effect of the relationship between plant species and disease types on prediction performance is not properly studied. In this study, we proposed a new model named Multi-output Deep Supervised Classifier Chains (Mo-DsCC) which weaves the prediction of plant species and disease by chaining the output layers for the two labels. Mo-DsCC consists of three components: A modified VGG-16 network as the backbone, deep supervision training, and a stack of classification chains. To evaluate the advantages of our model, we perform intensive experiments on two benchmark datasets Plant Village and PlantDoc. Comparison to recent approaches, including multi-model, multi-label (Power-set), multi-output and multi-task, demonstrates that Mo-DsCC achieves better accuracy and F1-score. The empirical study in this paper shows that the application of Mo-DsCC could be a useful puzzle for smart agriculture to benefit farms and bring new ideas to industry and academia.

Related papers

• Clustering-Guided Multi-Layer Contrastive Representation Learning for Citrus Disease Classification [17.627760587507737]
  Citrus is one of the most economically important fruit crops globally.<n> Accurate disease detection and classification serve as critical prerequisites for implementing targeted control measures.<n>Recent advancements in artificial intelligence, particularly deep learning-based computer vision algorithms, have substantially decreased time and labor requirements.
  arXiv  Detail & Related papers  (2025-07-15T10:22:52Z)
• Self-Classification Enhancement and Correction for Weakly Supervised Object Detection [113.51483527300496]
  weakly supervised object detection (WSOD) has attracted much attention due to its low labeling cost.<n>In this work, we introduce a novel WSOD framework to ameliorate these two issues.<n>For one thing, we propose a self-classification enhancement module that integrates intra-class binary classification (ICBC) to bridge the gap between the two distinct MCC tasks.<n>For another, we propose a self-classification correction algorithm during inference, which combines the results of both MCC tasks to effectively reduce the mis-classified predictions.
  arXiv  Detail & Related papers  (2025-05-22T06:45:58Z)
• Automatic Fused Multimodal Deep Learning for Plant Identification [1.2289361708127877]
  We introduce a pioneering multimodal DL-based approach for plant classification with automatic modality fusion.<n>Our method achieves 82.61% accuracy on 979 classes of Multimodal-PlantCLEF, surpassing state-of-the-art methods and outperforming late fusion by 10.33%.
  arXiv  Detail & Related papers  (2024-06-03T15:43:29Z)
• SugarViT -- Multi-objective Regression of UAV Images with Vision Transformers and Deep Label Distribution Learning Demonstrated on Disease Severity Prediction in Sugar Beet [3.2925222641796554]
  This work will introduce a machine learning framework for automatized large-scale plant-specific trait annotation. We develop an efficient Vision Transformer based model for disease severity scoring called SugarViT. Although the model is evaluated on this special use case, it is held as generic as possible to also be applicable to various image-based classification and regression tasks.
  arXiv  Detail & Related papers  (2023-11-06T13:01:17Z)
• Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches [14.73818032506552]
  We survey current deep learning approaches for plant identification and disease classification. We propose a new model named Generalised Stacking Multi-output CNN (GSMo-CNN) We show that the proposed GSMo-CNN achieves state-of-the-art performance on three benchmark datasets.
  arXiv  Detail & Related papers  (2023-10-25T01:06:18Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification [59.698811329287174]
  We leverage GPT-2 for generating artificial training instances in order to improve classification performance. Our results show that fine-tuning GPT-2 in a handful of label instances leads to consistent classification improvements.
  arXiv  Detail & Related papers  (2021-11-17T12:10:03Z)
• Neural networks for Anatomical Therapeutic Chemical (ATC) [83.73971067918333]
  We propose combining multiple multi-label classifiers trained on distinct sets of features, including sets extracted from a Bidirectional Long Short-Term Memory Network (BiLSTM) Experiments demonstrate the power of this approach, which is shown to outperform the best methods reported in the literature.
  arXiv  Detail & Related papers  (2021-01-22T19:49:47Z)
• 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)
• Adversarial Feature Hallucination Networks for Few-Shot Learning [84.31660118264514]
  Adversarial Feature Hallucination Networks (AFHN) is based on conditional Wasserstein Generative Adversarial networks (cWGAN) Two novel regularizers are incorporated into AFHN to encourage discriminability and diversity of the synthesized features.
  arXiv  Detail & Related papers  (2020-03-30T02:43:16Z)

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.