论文标题
使用基于双相卷积神经网络的系统鉴定水稻疾病，针对小数据集
Rice grain disease identification using dual phase convolutional neural network based system aimed at small dataset
论文作者
论文摘要
尽管卷积神经网络（CNN）被广泛用于植物疾病检测，但在处理各种异质背景时，它们需要大量训练样本。在这项工作中，已经提出了一种基于CNN的双相方法，该方法可以有效地在具有异质性的小水稻疾病数据集上起作用。在第一阶段，使用更快的RCNN方法用于从图像中裁切出明显的部分（米粒）。这一初始阶段导致了没有异质背景的水稻谷物的次要数据集。使用CNN结构对这种派生和简化样品进行疾病分类。将双相方法与CNN在小谷物数据集上的直接应用进行比较显示了所提出的方法的有效性，该方法提供了5倍的交叉验证精度为88.07％。
Although Convolutional neural networks (CNNs) are widely used for plant disease detection, they require a large number of training samples when dealing with wide variety of heterogeneous background. In this work, a CNN based dual phase method has been proposed which can work effectively on small rice grain disease dataset with heterogeneity. At the first phase, Faster RCNN method is applied for cropping out the significant portion (rice grain) from the image. This initial phase results in a secondary dataset of rice grains devoid of heterogeneous background. Disease classification is performed on such derived and simplified samples using CNN architecture. Comparison of the dual phase approach with straight forward application of CNN on the small grain dataset shows the effectiveness of the proposed method which provides a 5 fold cross validation accuracy of 88.07%.
