Image augmentation improves few-shot classification performance in plant disease recognition

• URL: http://arxiv.org/abs/2208.12613v1
• Date: Thu, 25 Aug 2022 00:53:19 GMT
• Title: Image augmentation improves few-shot classification performance in plant disease recognition
• Abstract summary: I tackle the problem of plant image data scarcity by exploring the efficacy of various data augmentation techniques. I propose an augmentation scheme utilizing a sequence of different augmentations which consistently improves accuracy through many trials.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the world population projected to near 10 billion by 2050, minimizing crop damage and guaranteeing food security has never been more important. Machine learning has been proposed as a solution to quickly and efficiently identify diseases in crops. Convolutional Neural Networks typically require large datasets of annotated data which are not available on demand. Collecting this data is a long and arduous process which involves manually picking, imaging, and annotating each individual leaf. I tackle the problem of plant image data scarcity by exploring the efficacy of various data augmentation techniques when used in conjunction with transfer learning. I evaluate the impact of various data augmentation techniques both individually and combined on the performance of a ResNet. I propose an augmentation scheme utilizing a sequence of different augmentations which consistently improves accuracy through many trials. Using only 10 total seed images, I demonstrate that my augmentation framework can increase model accuracy by upwards of 25\%.

Related papers

• Mapping Woody Vegetation from Multi-Source Imagery and Prediction Fusion for Enhanced Data Efficiency and Accuracy [0.04024586290381058]
  Tree cover mapping requires reliable and high-quality imagery, free of cloud and weather defects to ensure accurate model outputs.<n>Deep learning approaches can generate high quality maps with minimal human intervention but require large amounts of human annotated data to be successful.<n>We propose a framework consisting of methods that aim to improve the data efficiency and robustness of deep learning models.
  arXiv  Detail & Related papers  (2026-08-26T23:54:54Z)
• Do We Need All the Synthetic Data? Targeted Synthetic Image Augmentation via Diffusion Models [25.238823603331525]
  We show that synthetically augmenting part of the data that is not learned early in training with faithful images-containing same features but different noise-outperforms augmenting the entire dataset.<n>Our experiments show that by augmenting only 30%-40% of the data, our method boosts generalization by up to 2.8% in a variety of scenarios.
  arXiv  Detail & Related papers  (2025-05-27T07:27:03Z)
• Enhancing Image Classification with Augmentation: Data Augmentation Techniques for Improved Image Classification [0.0]
  Convolutional Neural Networks (CNNs) serve as the workhorse of deep learning, finding applications in various fields that rely on images. In this study, we explore the effectiveness of 11 different sets of data augmentation techniques, which include three novel sets proposed in this work. The ensemble of image augmentation techniques proposed emerges as the most effective on the Caltech-101 dataset.
  arXiv  Detail & Related papers  (2025-02-25T23:03:30Z)
• Enhancing Medical Image Analysis through Geometric and Photometric transformations [0.40964539027092917]
  We evaluate the effectiveness of data augmentation techniques on two different medical image datasets. In the first step, we applied some transformation techniques to the skin cancer dataset containing benign and malignant classes. In the second step, we used the Mixup technique by mixing two random images and their corresponding masks using the retina and blood vessels dataset.
  arXiv  Detail & Related papers  (2025-01-23T13:21:14Z)
• Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation [53.95204595640208]
  Data-Free Knowledge Distillation (DFKD) is an advanced technique that enables knowledge transfer from a teacher model to a student model without relying on original training data. Previous approaches have generated synthetic images at high resolutions without leveraging information from real images. MUSE generates images at lower resolutions while using Class Activation Maps (CAMs) to ensure that the generated images retain critical, class-specific features.
  arXiv  Detail & Related papers  (2024-11-26T02:23:31Z)
• Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image detectors [62.63467652611788]
  We introduce SEMI-TRUTHS, featuring 27,600 real images, 223,400 masks, and 1,472,700 AI-augmented images. Each augmented image is accompanied by metadata for standardized and targeted evaluation of detector robustness. Our findings suggest that state-of-the-art detectors exhibit varying sensitivities to the types and degrees of perturbations, data distributions, and augmentation methods used.
  arXiv  Detail & Related papers  (2024-11-12T01:17:27Z)
• PotatoGANs: Utilizing Generative Adversarial Networks, Instance Segmentation, and Explainable AI for Enhanced Potato Disease Identification and Classification [0.0]
  Our research employs a novel approach termed as PotatoGANs. Two types of Generative Adversarial Networks (GANs) are utilized to generate synthetic potato disease images. Using the Inception score as a measure, our experiments show the better quality and realisticness of the images created by PotatoGANs.
  arXiv  Detail & Related papers  (2024-05-12T17:00:52Z)
• Evaluating Data Augmentation Techniques for Coffee Leaf Disease Classification [2.0892083471064407]
  This paper uses the RoCoLe dataset and approaches based on deep learning for classifying coffee leaf diseases from images. Our study demonstrates the effectiveness of Transformer-based models, online augmentations, and CycleGAN augmentation in improving leaf disease classification.
  arXiv  Detail & Related papers  (2024-01-11T09:22:36Z)
• Effective Data Augmentation With Diffusion Models [65.09758931804478]
  We address the lack of diversity in data augmentation with image-to-image transformations parameterized by pre-trained text-to-image diffusion models. Our method edits images to change their semantics using an off-the-shelf diffusion model, and generalizes to novel visual concepts from a few labelled examples. We evaluate our approach on few-shot image classification tasks, and on a real-world weed recognition task, and observe an improvement in accuracy in tested domains.
  arXiv  Detail & Related papers  (2023-02-07T20:42:28Z)
• Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images [2.5075774184834803]
  Deep neural networks still rely on large amounts of training data to avoid overfitting. Labeled training data for real-world applications such as healthcare is limited and difficult to access. We build upon the emerging success of text-to-image diffusion probabilistic models in augmenting the training samples of our macroscopic skin disease dataset.
  arXiv  Detail & Related papers  (2023-01-12T04:22:23Z)
• RangeAugment: Efficient Online Augmentation with Range Learning [54.61514286212455]
  RangeAugment efficiently learns the range of magnitudes for individual as well as composite augmentation operations. We show that RangeAugment achieves competitive performance to state-of-the-art automatic augmentation methods with 4-5 times fewer augmentation operations.
  arXiv  Detail & Related papers  (2022-12-20T18:55:54Z)
• Local Magnification for Data and Feature Augmentation [53.04028225837681]
  We propose an easy-to-implement and model-free data augmentation method called Local Magnification (LOMA) LOMA generates additional training data by randomly magnifying a local area of the image. Experiments show that our proposed LOMA, though straightforward, can be combined with standard data augmentation to significantly improve the performance on image classification and object detection.
  arXiv  Detail & Related papers  (2022-11-15T02:51:59Z)
• Development of an algorithm for medical image segmentation of bone tissue in interaction with metallic implants [58.720142291102135]
  This study develops an algorithm for calculating bone growth in contact with metallic implants. Bone and implant tissue were manually segmented in the training data set. In terms of network accuracy, the model reached around 98%.
  arXiv  Detail & Related papers  (2022-04-22T08:17:20Z)
• Feature transforms for image data augmentation [74.12025519234153]
  In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we build ensembles on the data level by adding images generated by combining fourteen augmentation approaches. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method.
  arXiv  Detail & Related papers  (2022-01-24T14:12:29Z)
• Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification [0.36700088931938835]
  This work proposes a lightweight transfer learning-based approach for detecting diseases from tomato leaves. It utilizes an effective preprocessing method to enhance the leaf images with illumination correction for improved classification. The proposed architecture achieves 99.30% accuracy with a model size of 9.60MB and 4.87M floating-point operations.
  arXiv  Detail & Related papers  (2021-09-06T12:14:02Z)
• Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection [0.0]
  We propose a method that generates agricultural images from a synthetic 3D crop model domain into real world crop domains. The method uses a semantically constrained GAN (generative adversarial network) to preserve the fruit position and geometry. Incremental training experiments in vineyard grape detection tasks show that the images generated from our method can significantly speed the domain process.
  arXiv  Detail & Related papers  (2021-08-30T16:11:59Z)

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.