Precision agriculture requires the estimation of plant growth stages in real-time. When the plant growth stage is known, the wastage of resources in cultivation, such as nutrients and water, is reduced as only the required resources need to be supplied. Plants at different growth stages, however, have similar morphological features, which can make autonomous growth stage estimation difficult. This paper presents two feature extraction methods for growth stage estimation: one that uses a bank of Gabor filters and morphological operations, and the other that uses pre-trained convolutional neural networks (CNNs) and transfer learning. We test these methods on a publicly available plant growth stage dataset (``bccr-segset``) for two species, canola and radish, grown and captured under indoor conditions. The two proposed feature extraction methods are compared, using support vector machines and boosted trees as classifiers. We find that both methods are suitable for real-time applications, and that CNN features outperform the hand-crafted features, both with regard to speed and accuracy. The best system (VGG-19 features, classified with a radial basis function support vector machine) obtained an accuracy of 98.4% for both species, processing an image in 0.08 seconds.

翻译：精准农业需要实时估计植物生长阶段。当植物生长阶段已知时，可以仅供应所需的资源，从而减少栽培中养分、水分等资源的浪费。然而，不同生长阶段的植物具有相似的形态特征，这使得自主生长阶段估计变得困难。本文提出了两种用于生长阶段估计的特征提取方法：一种采用Gabor滤波器组与形态学操作，另一种采用预训练卷积神经网络（CNN）与迁移学习。我们在公开的植物生长阶段数据集（“bccr-segset”）上，对室内条件下培育并采集的油菜与萝卜两个物种进行了方法测试。以支持向量机与提升树作为分类器，对两种特征提取方法进行了比较。结果表明，两种方法均适用于实时应用，且CNN特征在速度与准确率方面均优于手工特征。最佳系统（采用径向基函数支持向量机分类的VGG-19特征）对两个物种均取得了98.4%的准确率，单张图像处理时间为0.08秒。