Background Plants have an important place in the life of all living things. Today, there is a risk of extinction for many plant species due to climate change and its environmental impact. Therefore, researchers have conducted various studies with the aim of protecting the diversity of the planet’s plant life. Generally, research in this area is aimed at determining plant species and diseases, with works predominantly based on plant images. Advances in deep learning techniques have provided very successful results in this field, and have become widely used in research studies to identify plant species. Methods In this paper, a Multi-Division Convolutional Neural Network (MD-CNN)-based plant recognition system was developed in order to address an agricultural problem related to the classification of plant species. In the proposed system, we divide plant images into equal nxn-sized pieces, and then deep features are extracted for each piece using a Convolutional Neural Network (CNN). For each part of the obtained deep features, effective features are selected using the Principal Component Analysis (PCA) algorithm. Finally, the obtained effective features are combined and classification conducted using the Support Vector Machine (SVM) method. Results In order to test the performance of the proposed deep-based system, eight different plant datasets were used: Flavia, Swedish, ICL, Foliage, Folio, Flower17, Flower102, and LeafSnap. According to the results of these experimental studies, 100% accuracy scores were achieved for the Flavia, Swedish, and Folio datasets, whilst the ICL, Foliage, Flower17, Flower102, and LeafSnap datasets achieved results of 99.77%, 99.93%, 97.87%, 98.03%, and 94.38%, respectively.

中文翻译：

---

基于多分卷积神经网络的植物识别系统

背景植物在所有生物的生命中都占有重要的地位。今天，由于气候变化及其对环境的影响，许多植物物种面临灭绝的风险。因此，研究人员进行了各种研究，旨在保护地球植物生命的多样性。通常，该领域的研究旨在确定植物物种和疾病，主要基于植物图像。深度学习技术的进步在该领域提供了非常成功的成果，并已广泛用于识别植物物种的研究。方法在本文中，为了解决与植物物种分类相关的农业问题，开发了一种基于多分卷积神经网络（MD-CNN）的植物识别系统。在建议的系统中，我们将植物图像分成相等的 nxn 大小的块，然后使用卷积神经网络 (CNN) 为每个块提取深度特征。对于获得的深度特征的每一部分，使用主成分分析（PCA）算法选择有效特征。最后，将获得的有效特征进行组合，并使用支持向量机（SVM）方法进行分类。结果为了测试所提出的基于深度的系统的性能，使用了八种不同的植物数据集：Flavia、Swedish、ICL、Foliage、Folio、Flower17、Flower102 和 LeafSnap。根据这些实验研究的结果，Flavia、Swedish 和 Folio 数据集的准确率达到 100%，而 ICL、Foliage、Flower17、Flower102 和 LeafSnap 数据集的准确率分别为 99.77%、99.93%、97.87%、 98.03%,