论文标题
低数据状态下用于植物疾病鉴定的SSM-NET
SSM-Net for Plants Disease Identification in Low Data Regime
论文作者
论文摘要
植物疾病检测是增加农业生产的重要因素。由于疾病检测的困难，农民在其农作物上喷洒各种农药以保护它们，从而对农作物的生长和食物标准造成了巨大伤害。深度学习可以为检测此类疾病提供关键帮助。但是，收集大量数据的所有形式的疾病都困扰着特定植物物种是非常不便的。在本文中，我们提出了一种新的基于指标的少量学习SSM Net体系结构，该结构由堆叠的暹罗和匹配网络组件组成，以解决低数据制度中疾病检测的问题。我们在两个数据集上演示了我们的实验：微型叶片疾病和甘蔗疾病数据集。我们已经表明，SSM-NET方法可以在微型叶片数据集中获得更好的决策界限，而甘蔗数据集的精度为92.7％。与广泛使用的VGG16转移学习方法相比，精度分别提高了约10％和约5％。此外，我们使用甘蔗数据集上的SSM NET和MINI-LEAVES数据集上的SSM NET达到了0.90的F1得分。我们的代码实现可在GitHub上获得：https：//github.com/shruti-jadon/plantsdiseardetection。
Plant disease detection is an essential factor in increasing agricultural production. Due to the difficulty of disease detection, farmers spray various pesticides on their crops to protect them, causing great harm to crop growth and food standards. Deep learning can offer critical aid in detecting such diseases. However, it is highly inconvenient to collect a large volume of data on all forms of the diseases afflicting a specific plant species. In this paper, we propose a new metrics-based few-shot learning SSM net architecture, which consists of stacked siamese and matching network components to address the problem of disease detection in low data regimes. We demonstrated our experiments on two datasets: mini-leaves diseases and sugarcane diseases dataset. We have showcased that the SSM-Net approach can achieve better decision boundaries with an accuracy of 92.7% on the mini-leaves dataset and 94.3% on the sugarcane dataset. The accuracy increased by ~10% and ~5% respectively, compared to the widely used VGG16 transfer learning approach. Furthermore, we attained F1 score of 0.90 using SSM Net on the sugarcane dataset and 0.91 on the mini-leaves dataset. Our code implementation is available on Github: https://github.com/shruti-jadon/PlantsDiseaseDetection.
