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

• Published in: Frontiers in plant science Vol. 8; p. 1741
• Main Authors: Cruz, Albert C, Luvisi, Andrea, De Bellis, Luigi, Ampatzidis, Yiannis
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 10-10-2017
• Subjects: convolutional neural networks; deep learning; machine vision; Olea europaea; Plant Science; transfer learning; Xylella fastidiosa; deep learning; transfer learning; convolutional neural networks; Olea europaea; Xylella fastidiosa; machine vision
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2017.01741

We have developed a vision-based program to detect symptoms of Olive Quick Decline Syndrome (OQDS) on leaves of
L. infected by
, named X-FIDO (
FastIdiosa Detector for
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
-positive leaves and 100
-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.