Back to Search
Start Over
X-FIDO: An effective application for detecting olive quick decline syndrome with deep learning and data fusion
- Source :
- Frontiers in Plant Science, Frontiers in Plant Science, Vol 8 (2017)
- Publication Year :
- 2017
-
Abstract
- We have developed a vision-based program to detect symptoms of Olive Quick Decline Syndrome (OQDS) on leaves of Olea europaea L. infected by Xylella fastidiosa, named X-FIDO (Xylella FastIdiosa Detector for O. europaea L.). Previous work predicted disease from leaf images with deep learning but required a vast amount of data which was obtained via crowd sourcing such as the PlantVillage project. This approach has limited applicability when samples need to be tested with traditional methods (i.e., PCR) to avoid incorrect training input or for quarantine pests which manipulation is restricted. In this paper, we demonstrate that transfer learning can be leveraged when it is not possible to collect thousands of new leaf images. Transfer learning is the re-application of an already trained deep learner to a new problem. We present a novel algorithm for fusing data at different levels of abstraction to improve performance of the system. The algorithm discovers low-level features from raw data to automatically detect veins and colors that lead to symptomatic leaves. The experiment included images of 100 healthy leaves, 99 X. fastidiosa-positive leaves and 100 X. fastidiosa-negative leaves with symptoms related to other stress factors (i.e., abiotic factors such as water stress or others diseases). The program detects OQDS with a true positive rate of 98.60 ± 1.47% in testing, showing great potential for image analysis for this disease. Results were obtained with a convolutional neural network trained with the stochastic gradient descent method, and ten trials with a 75/25 split of training and testing data. This work shows potential for massive screening of plants with reduced diagnosis time and cost.
- Subjects :
- 0106 biological sciences
Computer science
Machine vision
Convolutional neural network
02 engineering and technology
Plant Science
lcsh:Plant culture
transfer learning
01 natural sciences
convolutional neural networks
0202 electrical engineering, electronic engineering, information engineering
lcsh:SB1-1110
Olea europaea
Original Research
Xylella fastidiosa
biology
business.industry
Deep learning
deep learning
Pattern recognition
machine vision
biology.organism_classification
Sensor fusion
Transfer learning
Stochastic gradient descent
020201 artificial intelligence & image processing
Artificial intelligence
business
Transfer of learning
010606 plant biology & botany
Test data
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Frontiers in Plant Science, Frontiers in Plant Science, Vol 8 (2017)
- Accession number :
- edsair.doi.dedup.....08aab7fd7c90f3d41d93124e0ad00225