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Performance of deep learning vs machine learning in plant leaf disease detection

机译：植物叶病检测中深度学习的性能VS机学习

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摘要

Plants are recognized as essential as they are the primary source of humanity's energy production since they are having nutritious, medicinal, etc. values. At any time between crop farming, plant diseases can affect the leaf, resulting in enormous crop production damages and economic market value. Therefore, in the farming industry, identification of leaf disease plays a crucial role. It needs, however, enormous labor, greater preparation time, and comprehensive plant pathogen knowledge. For the identification of plant disease detection various machine learning (ML) as well as deep learning (DL) methods are developed & examined by various researchers, and many of the times they also got significant results in both cases. Motivated by those existing works, here in this article we are comparing the performance of ML (Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent (SGD)) & DL (Inception-v3, VGG-16, VGG-19) in terms of citrus plant disease detection. The disease classification accuracy (CA) we received by experimentation is quite impressive as DL methods perform better than that of ML methods in case of disease detection as follows: RF-76.8% SGD-86.5% SVM87% VGG-19-87.4% Inception-v3-89% VGG-16-89.5%. From the result, we can tell that RF is giving the least CA whereas VGG-16 is giving the best in terms of CA.
机译：植物被认为是必不可少的，因为它们是人类能源产生的主要来源，因为它们具有营养，药用等价值。在作物养殖之间的任何时候，植物疾病都会影响叶子，导致巨大的作物生产损害和经济市场价值。因此，在农业行业中，叶疾病的鉴定起着至关重要的作用。然而，它需要巨大的劳动力，更高的准备时间和综合植物病原体知识。为了鉴定植物疾病检测各种机器学习（ML）以及深度学习（DL）方法是由各种研究人员进行的，并且许多次数在这两种情况下也有很多次数。这些现有工程的激励，这里在本文中，我们正在比较ML的性能（支持向量机（SVM），随机森林（RF），随机梯度下降（SGD））和DL（Inception-V3，VGG-16，VGG -19）在柑橘植物疾病检测方面。我们通过实验收到的疾病分类准确度（CA）是令人印象深刻的，因为DL方法比ML方法更好，如疾病检测，如下所示：RF-76.8％> SGD-86.5％> SVM87％> VGG-19-87.4 ％> inception-v3-89％> vgg-16-89.5％。从结果中，我们可以告诉RF给出了CA的最低，而VGG-16则在CA方面给出最佳。

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