Several countries are most reliant on agriculture either in terms of employment opportunities, national income, availability of a raw material, food production, to name but a few. However, it faces a big challenge such as climate changes, diseases, pets, weeds etc. Therefore, last decade has provided a machine learning-based solution to the agricultural community, which helped farmers to identify the diseases at the early stages. In this article, our focus is on grape diseases, and proposes a novel framework to identify and classify the selected diseases at the early stages. A deep learning-based solution is embedded into a conventional architecture for optimal performance. Three primary steps are involved; (a) feature extraction after applying transfer learning on pre-trained deep models, AlexNet and ResNet101, (b) selection of best features using proposed Yager Entropy along with Kurtosis (YEaK) technique, (c) fusion of strong features using proposed parallel approach and later subject to classification step using least squared support vector machine (LS-SVM). The simulations are performed on infected grape leaves obtained from the plant village dataset to achieving an accuracy of 99%. From the simulation results, we sincerely believe that our proposed approach performed exceptionally compared to several existing methods.

中文翻译：

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用于葡萄叶病识别的熵控制深度特征选择框架

一些国家在就业机会、国民收入、原材料供应、粮食生产等方面最依赖农业。然而，它面临着气候变化、疾病​​、宠物、杂草等巨大挑战。因此，过去十年为农业社区提供了基于机器学习的解决方案，帮助农民及早识别疾病。在本文中，我们的重点是葡萄病害，并提出了一个新的框架来识别和分类早期选定的病害。基于深度学习的解决方案嵌入到传统架构中以获得最佳性能。涉及三个主要步骤；(a) 在预训练的深度模型 AlexNet 和 ResNet101 上应用迁移学习后的特征提取，(b) 使用提出的 Yager Entropy 和峰度 (YEaK) 技术选择最佳特征，(c) 使用提出的并行方法融合强特征，然后使用最小二乘支持向量机 (LS-SVM) 进行分类步骤。对从植物村数据集中获得的受感染葡萄叶子进行模拟，达到 99% 的准确度。从模拟结果来看，我们真诚地相信我们提出的方法与几种现有方法相比表现异常。对从植物村数据集中获得的受感染葡萄叶子进行模拟，达到 99% 的准确度。从模拟结果来看，我们真诚地相信我们提出的方法与几种现有方法相比表现异常。对从植物村数据集中获得的受感染葡萄叶子进行模拟，达到 99% 的准确度。从模拟结果来看，我们真诚地相信我们提出的方法与几种现有方法相比表现异常。