Tomato plant disease detection using transfer learning with C-GAN synthetic images
Tomato plant disease detection using transfer learning with C-GAN synthetic images
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DOI：
10.1016/j.compag.2021.106279
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发表时间：
2021-06-29
影响因子：
8.3
通讯作者：
Vankudothu, Swetha
中科院分区：
文献类型：
--
作者：
Abbas, Amreen;Jain, Sweta;Vankudothu, Swetha
植物疾病和有害昆虫是农业部门的一个重大威胁。因此，早期发现和诊断这些疾病至关重要。深度学习方法的持续发展极大地帮助了植物疾病的检测，提供了一种具有非常精确结果的有力工具，但深度学习模型的准确性取决于用于训练的标记数据的数量和质量。在本文中，我们提出了一种基于深度学习的番茄病害检测方法，该方法利用条件生成对抗网络（C-GAN）来生成番茄植物叶片的合成图像。此后，使用迁移学习在合成图像和真实的图像上训练DenseNet 121模型，以将番茄叶图像分类为十种疾病。该模型已经在公开的PlantVillage数据集上进行了广泛的训练和测试。对番茄叶片图像进行5类、7类和10类分类，该方法的分类准确率分别达到99.51%、98.65%和97.11%。所提出的方法显示了其优越性，比现有的方法。
Plant diseases and pernicious insects are a considerable threat in the agriculture sector. Therefore, early detection and diagnosis of these diseases are essential. The ongoing development of profound deep learning methods has greatly helped in the detection of plant diseases, granting a vigorous tool with exceptionally precise outcomes but the accuracy of deep learning models depends on the volume and the quality of labeled data for training. In this paper, we have proposed a deep learning-based method for tomato disease detection that utilizes the Conditional Generative Adversarial Network (C-GAN) to generate synthetic images of tomato plant leaves. Thereafter, a DenseNet121 model is trained on synthetic and real images using transfer learning to classify the tomato leaves images into ten categories of diseases. The proposed model has been trained and tested extensively on publicly available PlantVillage dataset. The proposed method achieved an accuracy of 99.51%, 98.65%, and 97.11% for tomato leaf image classification into 5 classes, 7 classes, and 10 classes, respectively. The proposed approach shows its superiority over the existing methodologies.