Class-specific Data Augmentation for Plant Stress Classification
- URL: http://arxiv.org/abs/2406.13081v1
- Date: Tue, 18 Jun 2024 22:01:25 GMT
- Title: Class-specific Data Augmentation for Plant Stress Classification
- Authors: Nasla Saleem, Aditya Balu, Talukder Zaki Jubery, Arti Singh, Asheesh K. Singh, Soumik Sarkar, Baskar Ganapathysubramanian,
- Abstract summary: We propose an approach for automated class-specific data augmentation using a genetic algorithm.
We demonstrate the utility of our approach on soybean [Glycine max (L.) Merr] stress classification where symptoms are observed on leaves.
Our approach yields substantial performance, achieving a mean-per-class accuracy of 97.61% and an overall accuracy of 98% on the soybean leaf stress dataset.
- Score: 8.433217399526521
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Data augmentation is a powerful tool for improving deep learning-based image classifiers for plant stress identification and classification. However, selecting an effective set of augmentations from a large pool of candidates remains a key challenge, particularly in imbalanced and confounding datasets. We propose an approach for automated class-specific data augmentation using a genetic algorithm. We demonstrate the utility of our approach on soybean [Glycine max (L.) Merr] stress classification where symptoms are observed on leaves; a particularly challenging problem due to confounding classes in the dataset. Our approach yields substantial performance, achieving a mean-per-class accuracy of 97.61% and an overall accuracy of 98% on the soybean leaf stress dataset. Our method significantly improves the accuracy of the most challenging classes, with notable enhancements from 83.01% to 88.89% and from 85.71% to 94.05%, respectively. A key observation we make in this study is that high-performing augmentation strategies can be identified in a computationally efficient manner. We fine-tune only the linear layer of the baseline model with different augmentations, thereby reducing the computational burden associated with training classifiers from scratch for each augmentation policy while achieving exceptional performance. This research represents an advancement in automated data augmentation strategies for plant stress classification, particularly in the context of confounding datasets. Our findings contribute to the growing body of research in tailored augmentation techniques and their potential impact on disease management strategies, crop yields, and global food security. The proposed approach holds the potential to enhance the accuracy and efficiency of deep learning-based tools for managing plant stresses in agriculture.
Related papers
- Leveraging Pre-trained CNNs for Efficient Feature Extraction in Rice Leaf Disease Classification [1.4874449172133892]
We rigorously evaluate the impact of integrating feature extraction methodologies within pre-trained convolutional neural networks (CNNs)
Histogram of Oriented Gradients (HOG) yielded substantial improvements across architectures, notably propelling the accuracy of EfficientNet-B7 from 92% to an impressive 97%.
Grad-CAM unveiled that HOG integration resulted in heightened attention to disease-specific features, corroborating the performance enhancements observed.
arXiv Detail & Related papers (2024-02-26T07:19:48Z) - Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning [5.438725298163702]
Contrastive Self-Supervised Learning (SSL) offers a potential solution to labeled data scarcity.
We propose uncovering the optimal augmentations for applying contrastive learning in 1D phonocardiogram (PCG) classification.
We demonstrate that depending on its training distribution, the effectiveness of a fully-supervised model can degrade up to 32%, while SSL models only lose up to 10% or even improve in some cases.
arXiv Detail & Related papers (2023-12-01T11:06:00Z) - Data-Centric Long-Tailed Image Recognition [49.90107582624604]
Long-tail models exhibit a strong demand for high-quality data.
Data-centric approaches aim to enhance both the quantity and quality of data to improve model performance.
There is currently a lack of research into the underlying mechanisms explaining the effectiveness of information augmentation.
arXiv Detail & Related papers (2023-11-03T06:34:37Z) - Agave crop segmentation and maturity classification with deep learning
data-centric strategies using very high-resolution satellite imagery [101.18253437732933]
We present an Agave tequilana Weber azul crop segmentation and maturity classification using very high resolution satellite imagery.
We solve real-world deep learning problems in the very specific context of agave crop segmentation.
With the resulting accurate models, agave production forecasting can be made available for large regions.
arXiv Detail & Related papers (2023-03-21T03:15:29Z) - Enhancement attacks in biomedical machine learning [0.0]
"enhancement attacks" may be a greater threat to biomedical machine learning.
We developed two techniques to drastically enhance prediction performance of classifiers with minimal changes to features.
Our results demonstrate the feasibility of minor data manipulations to achieve any desired prediction performance.
arXiv Detail & Related papers (2023-01-05T03:03:28Z) - On the Importance of Hyperparameters and Data Augmentation for
Self-Supervised Learning [32.53142486214591]
Self-Supervised Learning (SSL) has become a very active area of Deep Learning research where it is heavily used as a pre-training method for classification and other tasks.
Here, we show that, indeed, the choice of hyper parameters and data augmentation strategies can have a dramatic impact on performance.
We introduce a new automated data augmentation algorithm, GroupAugment, which considers groups of augmentations and optimize the sampling across groups.
arXiv Detail & Related papers (2022-07-16T08:31:11Z) - Data augmentation for learning predictive models on EEG: a systematic
comparison [79.84079335042456]
deep learning for electroencephalography (EEG) classification tasks has been rapidly growing in the last years.
Deep learning for EEG classification tasks has been limited by the relatively small size of EEG datasets.
Data augmentation has been a key ingredient to obtain state-of-the-art performances across applications such as computer vision or speech.
arXiv Detail & Related papers (2022-06-29T09:18:15Z) - Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for
Semantic Segmentation [68.8204255655161]
We provide the first study on semantic image segmentation and introduce two new approaches: textitSmartAugment and textitSmartSamplingAugment.
SmartAugment uses Bayesian Optimization to search over a rich space of augmentation strategies and achieves a new state-of-the-art performance in all semantic segmentation tasks we consider.
SmartSamplingAugment, a simple parameter-free approach with a fixed augmentation strategy competes in performance with the existing resource-intensive approaches and outperforms cheap state-of-the-art data augmentation methods.
arXiv Detail & Related papers (2021-10-31T13:04:45Z) - Towards Reducing Labeling Cost in Deep Object Detection [61.010693873330446]
We propose a unified framework for active learning, that considers both the uncertainty and the robustness of the detector.
Our method is able to pseudo-label the very confident predictions, suppressing a potential distribution drift.
arXiv Detail & Related papers (2021-06-22T16:53:09Z) - Bootstrapping Your Own Positive Sample: Contrastive Learning With
Electronic Health Record Data [62.29031007761901]
This paper proposes a novel contrastive regularized clinical classification model.
We introduce two unique positive sampling strategies specifically tailored for EHR data.
Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
arXiv Detail & Related papers (2021-04-07T06:02:04Z) - Bayesian Active Learning for Wearable Stress and Affect Detection [0.7106986689736827]
Stress detection using on-device deep learning algorithms has been on the rise owing to advancements in pervasive computing.
In this paper, we propose a framework with capabilities to represent model uncertainties through approximations in Bayesian Neural Networks.
Our proposed framework achieves a considerable efficiency boost during inference, with a substantially low number of acquired pool points.
arXiv Detail & Related papers (2020-12-04T16:19:37Z)
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.