In the last decade, plant leaf disease identification has been an efficient research subject. In connection with this interest, deep learning architectures show a remarkable era in various fields of image processing and computer vision, including image classification, function detection, and image pattern recognition. In this study, we examine many aspects of convolutional neural networks for image pattern recognition. We examine the early and late fusion of multiple pattern recognition classifiers using various plant leaves. Commonly, it considers disease discovery with the diagnostic technologies available. In standard cases, planters usually do not discover the disease. Therefore, plant leaf disease detection is a significant research problem, and one of their goals is to uncover an effective way to identify leaf image disease. The article has made a potential effort to find a process that should be able to expose plant leaf disease using early and late fusion of two classifiers: modified Optimized Deep Neural Network (ODNN) with different parameters of evolutionary optimization of Grasshopper algorithm (GOA), Speeded Up Robust Features (SURF) and Convolutional Neural Network (CNN) that could support the system to achieve excellent performance. Classification quality parameters are determined, and research to explain the validation of the model has been carried out.

中文翻译：

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融合进化优化和深度神经网络的植物健康混合智能技术

在过去十年中，植物叶病鉴定一直是一个高效的研究课题。结合这种兴趣，深度学习架构在图像处理和计算机视觉的各个领域展现了非凡的时代，包括图像分类、功能检测和图像模式识别。在这项研究中，我们研究了用于图像模式识别的卷积神经网络的许多方面。我们检查了使用各种植物叶子的多种模式识别分类器的早期和晚期融合。通常，它考虑使用可用的诊断技术发现疾病。在标准情况下，种植者通常不会发现这种疾病。因此，植物叶片病害检测是一个重要的研究问题，他们的目标之一是发现一种识别叶片图像病害的有效方法。这篇文章做出了潜在的努力来寻找一个应该能够使用两个分类器的早期和晚期融合来暴露植物叶病的过程：改进的优化深度神经网络（ODNN）具有不同参数的 Grasshopper 算法（GOA）的进化优化， Speeded Up Robust Features (SURF) 和卷积神经网络 (CNN) 可以支持系统实现出色的性能。确定了分类质量参数，并进行了解释模型验证的研究。Speeded Up Robust Features (SURF) 和卷积神经网络 (CNN) 可以支持系统实现出色的性能。确定了分类质量参数，并进行了解释模型验证的研究。Speeded Up Robust Features (SURF) 和卷积神经网络 (CNN) 可以支持系统实现出色的性能。确定了分类质量参数，并进行了解释模型验证的研究。