Plants are extensively dispersed in nature, and they are essential for the survival and growth of the entire living creatures on the planet. Leaf classification and identification are critical in botany for recognizing new or endangered tree species. Leaf classification must be done quickly and accurately to sustain agricultural products. Traditional methods used manual classification of plant species. However, this method is costly, time‐consuming, and in some circumstances, impracticable. In the domains of image classification, target identification, and other fields, the new generation of Convolutional Neural Networks (CNNs) has shown outstanding outcomes. However, there exist some practical difficulties in executing these artificial neural networks. Owing to its high complexity and colossal running time, and also insufficient efficiency, the performance of classification is affected. An inappropriate count of neurons at each hidden layer may lead to an overfitting or underfitting problem. Thus, the core concept of this task is concerned with a novel technique for the growth of plant leaf classification for untrained data by hybridized classifiers. The core concept of this task is to find untrained data. The leaf images are collected from online sources. The images are further fed to the preprocessing phase. Pattern extraction is done on the preprocessed image using Weighted Weber Local Pattern. This preprocessed image is used for the classification of untrained data using hybridized deep‐structured architectures. Here, the combination of CNN with Recurrent Neural Network (RNN) and CNN with Support Vector Machine (SVM) is utilized for classification. Averaging the classification score of both hybrid techniques with parameter and threshold being optimized by the Best Searchable Modified Harris Hawks Optimization (BM‐HHO) produces the best classification outcome for untrained data. Here, the corresponding leaf type is classified if the input is given as the trained leaf images. Simultaneously, if the other types of data, like, palm tree leaf images, are given as input, and then explicitly shows the output as another type of leaves (or) untrained data. The experimental outcomes on the benchmark data set containing leaf images reveal that the introduced method achieved a higher accuracy than the conventional techniques. The accuracy of BM‐HHO‐CRNN + CSVM at the 65th learning rate is 3.43%, 0.84%, 1.80%, and 1.05% superior to HHO‐CRNN + CSVM, SS‐WOA‐CRNN + CSVM, WOA‐CRNN + CSVM, and SSO‐CRNN + CSVM.

中文翻译：

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改进的 Harris hawks 优化对未经训练的植物叶子分类的混合深度学习的影响

植物广泛分布在自然界中，它们对于地球上所有生物的生存和生长至关重要。叶子分类和识别对于植物学中识别新树种或濒危树种至关重要。必须快速准确地进行叶子分类才能维持农产品。传统方法采用手工对植物物种进行分类。然而，这种方法成本高昂、耗时，并且在某些情况下不切实际。在图像分类、目标识别等领域，新一代卷积神经网络（CNN）表现出了出色的成果。然而，执行这些人工神经网络存在一些实际困难。由于其复杂度高、运行时间长、效率不够高，影响了分类的性能。每个隐藏层的神经元数量不适当可能会导致过度拟合或欠拟合问题。因此，该任务的核心概念涉及一种通过混合分类器对未经训练的数据进行植物叶子分类的新技术。该任务的核心概念是找到未经训练的数据。叶子图像是从在线资源收集的。图像进一步馈送到预处理阶段。使用加权韦伯局部模式对预处理图像进行模式提取。该预处理图像用于使用混合深度结构化架构对未经训练的数据进行分类。这里，CNN与循环神经网络（RNN）和CNN与支持向量机（SVM）的组合用于分类。通过最佳可搜索改进哈里斯霍克斯优化 (BM-HHO) 优化参数和阈值，对两种混合技术的分类得分进行平均，可为未经训练的数据产生最佳分类结果。在这里，如果输入作为经过训练的叶子图像给出，则对相应的叶子类型进行分类。同时，如果将其他类型的数据（例如棕榈树叶图像）作为输入给出，然后将输出显式显示为另一种类型的叶子（或）未经训练的数据。在包含叶子图像的基准数据集上的实验结果表明，所引入的方法比传统技术具有更高的准确性。BM-HHO-CRNN + CSVM 在第 65 个学习率下的准确率分别优于 HHO-CRNN + CSVM、SS-WOA-CRNN + CSVM、WOA-CRNN + CSVM，分别为 3.43%、0.84%、1.80% 和 1.05%，和 SSO-CRNN + CSVM。