Various viral diseases affect the growth of the plants that causes a huge loss to farmers. If the viral infection could be noticed at earlier stages, then recovery procedures and respective action can be taken on time. Thus, there is a need for developing automatic viral infection detection methods for monitoring of crops analysing symptoms at different parts of plants. This paper proposes an automatic deep-learning-based viral infection detection method for a leguminous plant, Vigna mungo which is grown largely in the Indian subcontinent. Due to viral infection, some properties of the leaf image changes but the pattern is very random throughout the leaf structure. Hence, it is quite challenging to make an automatic disease detection method and perform the detection tasks in real-time. The collected image dataset of Vigna mungo leaves belonging to different categories are segmented and augmented to introduce more variety in the leaf image dataset. The convolutional neural network VirLeafNet is trained with different leaf images consisting of healthy, mild-infected and severely infected leaves for multiple epochs. The proposed methodology can be integrated with drones for wider crop area analysis. The proposed method is completely automatic, non-destructive and quickly classifies the leaf images of different categories in real-time. All the proposed models achieved high validation accuracy and yielded testing accuracy for VirLeafNet-1, VirLeafNet-2, and VirLeafNet-3 as 91.234%, 96.429%, and 97.403% respectively on different leaves images after extensive testing of the algorithm.

各种病毒性疾病影响植物的生长，给农民造成巨大损失。如果可以在早期发现病毒感染，则可以及时采取恢复措施并采取相应行动。因此，需要开发自动病毒感染检测方法，以监测分析植物不同部位症状的农作物。本文提出了一种基于深度学习的豆科植物Vigna mungo的自动病毒感染检测方法。它主要生长在印度次大陆。由于病毒感染，叶片图像的某些属性发生了变化，但整个叶片结构的模式非常随机。因此，制造一种自动疾病检测方法并实时执行检测任务是非常具有挑战性的。Vigna mungo收集的图像数据集对属于不同类别的叶子进行分割和扩充，以在叶子图像数据集中引入更多的变化。卷积神经网络VirLeafNet用不同的叶片图像训练，这些叶片图像由健康，轻度感染和重度感染的叶片组成，历时多个。所建议的方法可以与无人机集成，以进行更广泛的作物面积分析。所提出的方法是全自动的，无损的并且可以实时地对不同类别的叶片图像进行快速分类。在对算法进行大量测试后，所有提出的模型在不同叶片图像上的VirLeafNet-1，VirLeafNet-2和VirLeafNet-3均实现了较高的验证精度，并且测试精度分别为91.234％，96.429％和97.403％。