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

A smart system for the automatic evaluation of green olives visual quality in the field

(2020) Rafael R. Sola-Guirado et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

On line detection of defective apples using computer vision system combined with deep learning methods

(2020) Shuxiang Fan et al.   JOURNAL OF FOOD ENGINEERING

Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques

(2020) Sandra Munera et al.   POSTHARVEST BIOLOGY AND TECHNOLOGY

Detection of Strawberry Diseases Using a Convolutional Neural Network

(2020) Jia-Rong Xiao et al.   Plants-Basel

Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks

(2019) Guotai Wang et al.   NEUROCOMPUTING

Hyperspectral fruit and vegetable classification using convolutional neural networks

(2019) Jan Steinbrener et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Image-based deep learning automated sorting of date fruit

(2019) Amin Nasiri et al.   POSTHARVEST BIOLOGY AND TECHNOLOGY

Detection of anthracnose in tea plants based on hyperspectral imaging

(2019) Lin Yuan et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Deep learning models for plant disease detection and diagnosis

(2018) Konstantinos P. Ferentinos   COMPUTERS AND ELECTRONICS IN AGRICULTURE

A comparison of deep networks with ReLU activation function and linear spline-type methods

(2018) Konstantin Eckle et al.   NEURAL NETWORKS

Evaluation of a Pomegranate Peel Extract as an Alternative Means to Control Olive Anthracnose

(2017) Sonia Pangallo et al.   PHYTOPATHOLOGY

Strawberry foliar anthracnose assessment by hyperspectral imaging

(2016) Yu-Hui Yeh et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

(2016) Srdjan Sladojevic et al.   Computational Intelligence and Neuroscience

ImageNet Large Scale Visual Recognition Challenge

(2015) Olga Russakovsky et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

Visible–NIR reflectance spectroscopy and manifold learning methods applied to the detection of fungal infections on citrus fruit

(2015) D. Lorente et al.   JOURNAL OF FOOD ENGINEERING

Growth Simulation and Discrimination of Botrytis cinerea, Rhizopus stolonifer and Colletotrichum acutatum Using Hyperspectral Reflectance Imaging

(2015) Ye Sun et al.   PLoS One

La Antracnosis del olivo y su efecto en la calidad del aceite

(2014) J. Moral et al.   GRASAS Y ACEITES

Development of a Hyperspectral Computer Vision System Based on Two Liquid Crystal Tuneable Filters for Fruit Inspection. Application to Detect Citrus Fruits Decay

(2013) J. Gómez-Sanchis et al.   Food and Bioprocess Technology

Species of theColletotrichum gloeosporioidesandC. boninensecomplexes associated with olive anthracnose

(2013) L. Schena et al.   PLANT PATHOLOGY

Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers

(2013) J. Gómez-Sanchis et al.   POSTHARVEST BIOLOGY AND TECHNOLOGY

Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment

(2011) D. Lorente et al.   Food and Bioprocess Technology

Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables

(2010) Sergio Cubero et al.   Food and Bioprocess Technology

Assessing the Susceptibility of Olive Cultivars to Anthracnose Caused by Colletotrichum acutatum

(2009) J. Moral et al.   PLANT DISEASE

Olive classification according to external damage using image analysis

(2008) M.T. Riquelme et al.   JOURNAL OF FOOD ENGINEERING