Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks
Document Type
Article
Date of Original Version
5-1-2020
Abstract
Plant diseases are a crucial issue in agriculture. An accurate and automatic identification of leaf diseases could help to develop an early response to reduce economic losses. Recent research in plant diseases has adopted deep neural networks. However, such research has used the models as a black-box passing the labeled images through the networks. This letter presents an analysis of the network weights for the automatic recognition of soybean leaf diseases applied to images taken straight from a small and cheap unmanned aerial vehicle (UAV). To achieve high accuracy, we evaluated four deep neural network models trained with different parameters for fine-tuning (FT) and transfer learning. Data augmentation and dropout were used during the network training to avoid overfitting. Our methodology consists of using the SLIC method to segment the plant leaves in the top-view images obtained during the flight. We tested our data set created from real flight inspections in an end-to-end computer vision approach. Results strongly suggest that the FT of parameters substantially improves the identification accuracy.
Publication Title, e.g., Journal
IEEE Geoscience and Remote Sensing Letters
Volume
17
Issue
5
Citation/Publisher Attribution
Tetila, Everton C., Bruno B. Machado, Gabriel K. Menezes, Adair Da Silva Oliveira, Marco Alvarez, Willian P. Amorim, Nícolas A. De Souza Belete, Gercina Gonçalves Da Silva, and Hemerson Pistori. "Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks." IEEE Geoscience and Remote Sensing Letters 17, 5 (2020): 903-907. doi: 10.1109/LGRS.2019.2932385.