The problem of identifying the plant type seems to be tough due to the altering leaf color, and the variations in leaf shape overage. The plant leaf classification is very challenging and important issue to solve. The main idea of this paper is to introduce a novel deep learning-based plant leaf classification model. Initially, the pre-processing is done by RGB to gray scale conversion, histogram equalization, and median filtering for improving the image quality necessary for additional processing. In CNN, the activation function is optimized by the hybrid Shark Smell-based Whale Optimization Algorithm (SS-WOA) in a manner that the classification accuracy is attained maximum. The classification of untrained images is very challenging task, so the optimized threshold-based CNN classification is introduced. From the analysis, the accuracy of the proposed SS-WOA-CNN is 0.86%, 0.78%, 1.28%, and 1.53% advanced than PSO-CNN, GWO-CNN, WOA-CNN, and SSO-CNN, respectively. The accuracy of the proposed SS-WOA-CNN is 4.02%, 3.23%, 1.95%, 2.12%, and 0.57% progressed than NB, SVM, DNN, NN, and CNN. The hybrid SS-WOA optimizes the threshold value that can attain maximum classification accuracy for untrained data. The performance of the developed method is validated by differentiating the diverse traditional machine learning.

由于叶片颜色的变化和叶片形状的变化，识别植物类型的问题似乎很困难。植物叶片分类是非常具有挑战性和需要解决的重要问题。本文的主要思想是介绍一种新的基于深度学习的植物叶片分类模型。最初，预处理是通过 RGB 到灰度转换、直方图均衡和中值滤波来完成的，以提高额外处理所需的图像质量。在 CNN 中，激活函数通过基于混合鲨鱼气味的鲸鱼优化算法 (SS-WOA) 进行优化，从而使分类精度达到最大。未训练图像的分类是一项非常具有挑战性的任务，因此引入了基于优化阈值的 CNN 分类。从分析来看，所提出的 SS-WOA-CNN 的准确率分别比 PSO-CNN、GWO-CNN、WOA-CNN 和 SSO-CNN 高 0.86%、0.78%、1.28% 和 1.53%。所提出的 SS-WOA-CNN 的准确率比 NB、SVM、DNN、NN 和 CNN 提高了 4.02%、3.23%、1.95%、2.12% 和 0.57%。混合 SS-WOA 优化了阈值，可以为未经训练的数据获得最大分类精度。通过区分不同的传统机器学习来验证所开发方法的性能。