Class-specific data augmentation for plant stress classification

This is a companion dataset for the paper titled "Class-specific data augmentation for plant stress classification" by Nasla Saleem, Aditya Balu, Talukder Zaki Jubery, Arti Singh, Asheesh K. Singh, Soumik Sarkar, and Baskar Ganapathysubramanian published in The Plant Phenome Journal, https://doi.org/10.1002/ppj2.20112Abstract: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.

本数据集为发表于《植物表型组学杂志（The Plant Phenome Journal）》的论文《类别专属数据增强（data augmentation）用于植物胁迫分类》的配套数据集，论文作者为Nasla Saleem、Aditya Balu、Talukder Zaki Jubery、Arti Singh、Asheesh K. Singh、Soumik Sarkar及Baskar Ganapathysubramanian，DOI链接为https://doi.org/10.1002/ppj2.20112。摘要：数据增强（data augmentation）是提升基于深度学习（deep learning）的植物胁迫识别与分类图像分类器性能的有效手段。但从大量候选增强方案中筛选出有效策略仍是核心挑战，在不平衡且存在类别混淆的数据集中这一问题尤为突出。我们提出了一种基于遗传算法（genetic algorithm）的自动化类别专属数据增强方法。我们在叶片表现出胁迫症状的大豆[Glycine max (L.) Merr]胁迫分类任务中验证了该方法的实用性；由于该数据集存在类别混淆问题，这是一项极具挑战性的任务。我们的方法取得了优异性能，在大豆叶片胁迫数据集上实现了97.61%的每类平均准确率与98%的总体准确率。该方法显著提升了最难分类类别的准确率，具体提升分别为从83.01%至88.89%，以及从85.71%至94.05%。本研究的一项关键发现是，高性能的增强策略可通过计算高效的方式识别得到。我们仅针对不同增强方案微调基线模型的线性层，无需为每种增强策略从头训练分类器，从而降低了计算负担，同时实现了卓越的性能。本研究推动了面向植物胁迫分类的自动化数据增强策略的发展，尤其针对存在类别混淆的数据集场景。我们的研究成果为定制化增强技术的相关研究积累了更多证据，并凸显了其在病害管理策略、作物产量及全球粮食安全领域的潜在价值。所提出的方法有望提升农业领域植物胁迫管理相关深度学习工具的准确率与效率。