Paper Info
Title
Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks
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.
Year
DOI
Venue
2020
10.1109/LGRS.2019.2932385
IEEE Geoscience and Remote Sensing Letters
Keywords
DocType
Volume
Diseases,Image segmentation,Deep learning,Training,Agriculture,Inspection,Image recognition
Journal
17
Issue
ISSN
Citations 
5
1545-598X
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Everton Castelao Tetila1121.67
Bruno Brandoli Machado26710.23
Gabriel Kirsten Menezes300.34
Adair Da Silva Oliveira400.34
Marco Alvarez500.34
Willian Paraguassu Amorim6244.52
Nicolas Alessandro de Souza Belete772.21
Gercina Gonçalves da Silva8171.36
Hemerson Pistori96912.97