Application of deep convolutional neural networks for the detection of anthracnose in olives using VIS/NIR hyperspectral images

•Anthracnose is a fungal disease caused by several species of Colletotrichum.•HSI combined with CNN were applied to detect early stage of olive Anthracnose.•Image segmentation into several classes is a key task in computer vision techniques.•CNN ResNet101 architecture allowed obtaining good results...

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Bibliographic Details
Published in:Computers and electronics in agriculture 2021-08, Vol.187, p.106252, Article 106252
Main Authors: Fazari, Antonio, Pellicer-Valero, Oscar J., Gómez-Sanchıs, Juan, Bernardi, Bruno, Cubero, Sergio, Benalia, Souraya, Zimbalatti, Giuseppe, Blasco, Jose
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer vision
Economic impact
Fungi
Hyperspectral imaging
Image acquisition
Medical imaging
Neural networks
Olea europaea L
Olives
Quality inspection
Signs and symptoms
Spectral imaging
Visual observation
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Summary:•Anthracnose is a fungal disease caused by several species of Colletotrichum.•HSI combined with CNN were applied to detect early stage of olive Anthracnose.•Image segmentation into several classes is a key task in computer vision techniques.•CNN ResNet101 architecture allowed obtaining good results even with small dataset. Anthracnose is one of the primary diseases that affect olive production before and after harvest, causing severe damage and economic losses. The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) and convolutional neural networks (CNN). The olives were artificially inoculated with the fungus. Hyperspectral images (450–1050 nm) of each olive were acquired until visual symptoms of the disease were observed, in some cases up to 9 days. The olives were classified into two classes: control, inoculated with water, and fungi composed of olives inoculated with the fungus. The ResNet101 architecture was chosen and adapted to process 61-band hyperspectral images with only two classes. The result showed that the applied model is very effective in detecting infected olives since the sensitivity of the method was very high from the beginning (85% on day 3 and 100% onwards). From a commercial point of view, these results align with the need to detect the maximum number of infected fruits.
ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2021.106252