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. 2017 Oct 10:8:1741.
doi: 10.3389/fpls.2017.01741. eCollection 2017.

X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion

Albert C Cruz  1 , Andrea Luvisi  2 , Luigi De Bellis  2 , Yiannis Ampatzidis  3   4
Affiliations

X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion

Albert C Cruz et al. Front Plant Sci. .

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.

Keywords: Olea europaea; Xylella fastidiosa; convolutional neural networks; deep learning; machine vision; transfer learning.

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Figures

FIGURE 1
FIGURE 1
General system overview. A picture of an affected leaf is processed remotely by a server using deep learning. The user receives a report on the diagnosis of the specimen in the field.
FIGURE 2
FIGURE 2
System overview. Red: focus of work.
FIGURE 3
FIGURE 3
A general overview of the idea of Abstraction-Level Framing. After the convolutional, filtering portion of a convolutional neural network, each fully connected layer receives additional features at increasing levels of abstraction.
FIGURE 4
FIGURE 4
Examples images of Olea europaea L. used in this study. (A) Healthy control (asymptomatic leaves); (B) OQDS-symptomatic leaves (Xylella fastidiosa-positive samples); (C) X. fastidiosa-negative samples showing another pathogen/disorder. Note that the images given here are the native, original aspect ratio of O. europaea L. leaves. It is easy to distinguish healthy leaves from non-healthy leaves (A vs. B or C). However, tissue desiccation on the leaf tip, if it occurs, is not exclusive to OQDS (C1, C4, and C6), thus it is challenging to detect the difference between OQDS and non-OQDS samples (B vs. C).
FIGURE 5
FIGURE 5
(A) Accuracy, (B) Matthew’s Correlation Coefficient (MCC), (C) F1-Score, (D) precision and (E) recall of predicting symptoms of Olive Quick Decline Syndrome (OQDS) in images of X. fastidiosa-positive leaves of O. europaea L. amongst healthy controls (asymptomatic leaves) or X. fastidiosa-negative leaves showing other disorders. Higher is better for all metrics.
FIGURE 6
FIGURE 6
The advantage of including shape and texture. From left to right: The original image, the segmentation map (from which shape features such as moments are extracted), and the edge map. (A,B) The healthy leaves (asymptomatic), (C,D) OQDS leaves, and (E,F) non-OQDS leaves. Healthy leaves do not have any notable features in either the segmentation or edge maps. OQDS results in a more yellow leaf causing a distinct shape in the segmentation map, and note the subtle lines in the dead area of the leaf in (D). While other pathogens/disorders cause yellow leaves, it does not occur as orderly as leaf scorch, and dead areas do not have the distinctive subtle lines as in (D). Note that these images have been resized to 256 × 256 as a part of Step (3) in Figure 2.
FIGURE 7
FIGURE 7
Examples of images that were misclassified more than once across the 10 validation folds. (A–E) Images of OQDS leaves. (F–J) Images of leaves with pathogens/diseases other than OQDS.
FIGURE 8
FIGURE 8
Screen shots of the X-FIDO program. (A) The program is simple to operate and consists of three commands: New experiment; Open image, which prompts the user to open an image, automatically processes the image and logs the confidence scores; and Save results, which saves all logged confidence scores to a comma-separated value (CSV) file. In this sub-figure, the program correctly classified a healthy control. (B) A non-OQDS pathogen/disorder. (C) OQDS (X. Fastidiosa). (D) A very challenging sample of a non-OQDS image that resembles OQDS because of very faint leaf tip desiccation, and a healthy control because of verdancy.
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