Using deep transfer learning for image-based plant disease identification

By Junde Chen, Jinxiu Chen, Defu Zhang, Yuandong Sun, Y. A. Nanehkaran. Computers and Electronics in Agriculture, 2020

874 citations recorded by the source corpus.

Abstract Plant diseases have a disastrous impact on the safety of food production, and they can cause a significant reduction in both the quality and quantity of agricultural products. In severe cases, plant diseases may even cause no grain harvest entirely. Thus, the automatic identification and diagnosis of plant diseases are highly desired in the field of agricultural information. Many methods have been proposed for solving this task, where deep learning is becoming the preferred method due to the impressive performance. In this work, we study transfer learning of the deep convolutional neural networks for the identification of plant leaf diseases and consider using the pre-trained model learned from the typical massive datasets, and then transfer to the specific task trained by our own data. The VGGNet pre-trained on ImageNet and Inception module are selected in our approach. Instead of starting the training from scratch by randomly initializing the weights, we initialize the weights using the pre-trained networks on the large labeled dataset, ImageNet. The proposed approach presents a substantial performance improvement with respect to other state-of-the-art methods; it achieves a validation accuracy of no less than 91.83% on the public dataset. Even under complex background conditions, the average accuracy of the proposed approach reaches 92.00% for the class prediction of rice plant images. Experimental results demonstrate the validity of the proposed approach, and it is accomplished efficiently for plant disease detection.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/26a735338bf7fbbfd393dccc554b7300d17c3d17

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Plant Science

1. Kemal Akyol
2. Emine Uçar
3. Shujuan Zhang
4. Lili Li
5. Bin Wang
6. T. Gechev
7. Murat Uçar
8. Ümit Atila

Citations in the corpus

• Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “[70] proposed an improved VGG model (INC-VGGN) based on the VGG model framework by introducing two Inception modules, adding a pooling layer, and modifying the activation function.”
• Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach (2021, methodology): “Rice-and maize-leaf diseases were identified by Chen et al. [2] using the INC-VGGN method.”
• An advanced deep learning models-based plant disease detection: A review of recent research (2023, background): “In addition, (Chen et al., 2020) demonstrate that transfer learning can accurately diagnose rice lesions photos in complicated situations with an average accuracy of 94 percent, exceeding standard training.”
• Plant leaf disease classification using EfficientNet deep learning model (2021, background): “In another study, Chen et al. performed maize plant and rice plant disease classification by modifying the VGGNet module (Chen et al., 2020d).”