用于大豆品种识别的叶片深度特征学习方法

大豆有许多品种(cultivar),它们的叶片图像模式的差异非常细微,因此很难通过叶片特征将大豆品种区分开.虽然在使用叶片图像模式进行植物种类(species)识别方面的研究已经取得了巨大的进步,然而,作为一项非常细粒度的模式识别问题,大豆品种的识别与分类研究尚未引起足够的重视.传统的手工叶片图像分析方法一般无法刻画不同大豆品种的叶片特征的细微差异,因此识别率很低.本文尝试使用深度学习来提取具有强的辨识能力的叶片特征,以解决大豆的品种识别问题.我们提出了一种新颖的深度学习模型,称为目标转换注意力网络(Transformation Attention Network,TAN).该方法首先通过注意力机制提取细粒度的叶片图像特征,然后使用仿射变换纠正叶片姿势.我们构建了一个由240个大豆品种组成的大豆叶片品种图像数据库,每个品种有10个样本,以此数据集验证叶片图像模式中品种信息的可用性,并验证了所提出的深度学习模型对大豆品种识别的有效性.令人鼓舞的是实验结果证实了叶片图像模式在区分栽培大豆品种方面的有效性,并证明了所提出的方法优于流行的叶片手工特征提取方法和深度学习方法.

Learning Method of Leaf Deep Features for Soybean Cultivar Recognition

Soybeans include many varieties (cultivars) and their cultivars have subtle differences in leaf patterns, which makes it tough to distinguish them from leaf features. Great progress has been made in using leaf image patterns for plant species recognition. However, as a general fine-grained pattern recognition problem, soybean cultivar recognition has not yet received considerable attention. Traditional hand-operated leaf image analysis is limited to capture the subtle differences of leaf features among different cultivars. In this study, we attempt to use deep learning to harvest discriminatory leaf features for soybean cultivar recognition. A novel deep learning model, Transformation Attention Network (TAN), is proposed in this work. It first extracts fine-grained leaf features via the attention mechanism and then rectifies the leaf posture by affine transformations. We construct a soybean leaf cultivar dataset which consists of 240 soybean cultivars, with 10 samples per cultivar, to examine the availability of cultivar information in leaf patterns and validate the effectiveness of the proposed deep learning model for soybean cultivar recognition. The experimental results confirm the effectiveness of the leaf image patterns in distinguishing cultivars and demonstrate the better performance of the proposed method than that of the state-of-the-art hand-operated methods and deep learning methods in soybean cultivar recognition.