ABSTRACT Pest in the plant is a major challenge in the agriculture sector. Hence, early and accurate detection and classification of pests could help in precautionary measures while substantially reducing economic losses. Recent developments in deep convolutional neural network (CNN) have drastically improved the accuracy of image recognition systems. In this paper, we have presented a transfer learning of pre-trained deep CNN-based framework for classification of pest in tomato plants. The dataset for this study has been collected from online sources that consist of 859 images categorized into 10 classes. This study is first of its kind where: (i) dataset with 10 classes of tomato pest are involved; (ii) an exhaustive comparison of the performance of 15 pre-trained deep CNN models has been presented on tomato pest classification. The experimental results show that the highest classification accuracy of 88.83% has been obtained using DenseNet169 model. Further, the encouraging results of transfer learning-based models demonstrate its effectiveness in pest detection and classification tasks.

中文翻译：

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基于迁移学习的番茄病虫害分类框架

摘要 植物中的害虫是农业部门的主要挑战。因此，及早准确地检测和分类有害生物有助于采取预防措施，同时大大减少经济损失。深度卷积神经网络 (CNN) 的最新发展极大地提高了图像识别系统的准确性。在本文中，我们提出了一种基于深度 CNN 的预训练框架的迁移学习，用于番茄植物害虫分类。本研究的数据集是从在线资源中收集的，该数据集由分为 10 类的 859 张图像组成。这项研究是同类研究中的第一个：（i）涉及 10 类番茄害虫的数据集；(ii) 对 15 个预先训练的深度 CNN 模型在番茄害虫分类方面的性能进行了详尽的比较。实验结果表明，使用DenseNet169模型获得了88.83%的最高分类准确率。此外，基于迁移学习的模型令人鼓舞的结果证明了其在害虫检测和分类任务中的有效性。