论文标题
图像增强可改善植物疾病识别中几乎没有射击分类的性能
Image augmentation improves few-shot classification performance in plant disease recognition
论文作者
论文摘要
到2050年，随着世界人口预计将近100亿，最大程度地减少农作物的损害并确保粮食安全从未如此重要。已经提出了机器学习作为一种解决方案，以快速有效地识别农作物中的疾病。卷积神经网络通常需要大量的带注释数据的数据集，而这些数据无法根据需要提供。收集这些数据是一个漫长而艰巨的过程，涉及手动采摘，成像和注释每个叶片。我通过探索与转移学习结合使用时探索各种数据增强技术的功效来解决植物图像数据稀缺问题。我分别评估了各种数据增强技术的影响，并且对重新系统的性能进行了组合。我提出了一种利用一系列不同增强序列的增强方案，通过许多试验，可以始终提高准确性。仅使用10个总种子图像，我证明我的增强框架可以将模型准确性提高25 \％。
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\%.
