It is important for the sensitivity of ecological balance that image processing methods and techniques give better results day by day. Today, researchers use deep learning in image-based object recognition. Recently, the use of deep learning methods on plant species has increased. In this study, a hybrid method that is used together with feature selection methods and Convolutional Neural Network (CNN) models is presented. In the proposed model, CNN models are used for feature extraction. The features obtained from these models are combined and efficient features are selected with feature selection methods. The aim here is to subtract and classify intersecting features between the features obtained by feature selection methods. When the results of the experiments are compared, the intersection of the features obtained by feature selection methods are contributed to the classification performance. The classification success achieved by the Support Vector Machine (SVM) method was 98.91%.

对于生态平衡的敏感性而言，重要的是图像处理方法和技术每天都必须提供更好的结果。今天，研究人员在基于图像的对象识别中使用深度学习。最近，对植物物种的深度学习方法的使用有所增加。在这项研究中，提出了一种与特征选择方法和卷积神经网络（CNN）模型一起使用的混合方法。在提出的模型中，CNN模型用于特征提取。将从这些模型获得的特征进行组合，并使用特征选择方法选择有效的特征。此处的目的是减去和分类通过特征选择方法获得的特征之间的相交特征。比较实验结果后 通过特征选择方法获得的特征的交集有助于分类性能。通过支持向量机（SVM）方法实现的分类成功率为98.91％。