Background

The severity of diseases and threats found in different crop varieties is one of the primary causes of degradation in the agricultural economy. Early detection and disease diagnosis in crops will facilitate farmers to improve their livelihood and mankind.

Objective

This study aimed to develop method for disease identification in several seasonal crops during their early stages using deep learning architectures i.e. convolutional neural networks (CNN) and compare the feasibility, accuracy and performance of the proposed network with conventional feature extraction techniques like support vector machine, k-nearest neighbor, genetic algorithm, and artificial neural networks.

Method

This study preferred a database of 600 images i.e. 200 images of individual crop varieties which are labeled with 10 kinds of crop diseases. Each crop varieties have two different kinds of classes i.e. health crop and rusty crop. The CNNs are trained in such a manner that it will be able to detect diseases from infected crop varieties.

Result

Different convolution filters and pooling types of different sizes are used in the proposed work. Max pooling with a filter size of 32*32*3 achieves the accuracy of 92%. Average pool size with a convolution filter size of 64*64*3 achieved maximum accuracy of 93.7% and gains the better results in comparison to other machine learning and feature extraction models.

Conclusions

The contribution of the proposed work could be summarized as: (i) obtained results shows improvement in the feasibility and performance of CNN over other machine learning models. (ii) High performance shows the immediate crop disease identification ability of deep learning techniques over the different feature extraction models.

中文翻译：

---

使用监督深度卷积网络对原始Kharif作物进行分类和鉴定

背景

在不同作物品种中发现的疾病和威胁的严重性是农业经济退化的主要原因之一。作物的早期发现和病害诊断将有助于农民改善生计和人类。

目的

这项研究旨在开发使用卷积神经网络（CNN）的深度学习架构对几种季节性作物进行早期疾病识别的方法，并将该网络与支持向量机，支持向量机等传统特征提取技术的可行性，准确性和性能进行比较。 k最近邻，遗传算法和人工神经网络。

方法

该研究优选一个包含600张图像的数据库，即200种带有10种作物病害标记的单个作物品种的图像。每个作物品种都有两种不同的类别，即保健作物和生锈作物。对CNN进行培训的方式使其能够从受感染的农作物品种中检测出疾病。

结果

在建议的工作中使用了不同的卷积滤波器和不同大小的合并类型。过滤器大小为32 * 32 * 3的最大合并可实现92％的精度。卷积过滤器大小为64 * 64 * 3的平均池大小实现了93.7％的最大精度，并且与其他机器学习和特征提取模型相比，获得了更好的结果。

结论

拟议工作的贡献可以概括为：（i）获得的结果表明，与其他机器学习模型相比，CNN的可行性和性能有所提高。（ii）高性能显示了在不同特征提取模型上深度学习技术对作物病害的即时识别能力。