A review on the main challenges in automatic plant disease identification based on visible range images, Biosyst. Eng, vol.144, pp.52-60, 2016. ,
Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification, Comput. Electron. Agric, vol.153, pp.46-53, 2018. ,
Factors influencing the use of deep learning for plant disease recognition, Biosyst. Eng, vol.172, pp.84-91, 2018. ,
Annotated plant pathology databases for image-based detection and recognition of diseases, IEEE Lat. Am. Trans, vol.16, pp.1749-1757, 2018. ,
Disease detection on the leaves of the tomato plants by using deep learning, Agro-Geoinformatics, 2017 6th International Conference on, pp.1-5, 2017. ,
Deep learning models for plant disease detection and diagnosis, Comput. Electron. Agric, vol.145, pp.311-318, 2018. ,
A robust deep-learning based detector for real-time tomato plant diseases and pests recognition, Sensors, vol.17, p.2022, 2017. ,
High-performance deep neural networkbased tomato plant diseases and pests diagnosis system with refinement filter bank, Front. Plant Sci, vol.9, 2018. ,
Lifeclef plant identification task, Working Notes of CLEF 2015 -Conference and Labs of the Evaluation forum, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01182795
Deep learning (adaptive computation and machine learning series), Adapt. Computat. Mach. Learn. Series, vol.800, 2016. ,
, Rethinking imagenet pre-training, 2018.
An open access repository of images on plant health to enable the development of mobile disease diagnostics, 2015. ,
What makes imagenet good for transfer learning?, 2016. ,
Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015. ,
An automated detection and classification of citrus plant diseases using image processing techniques: a review, Comput. Electron. Agric, vol.153, pp.12-32, 2018. ,
Automatic plant disease diagnosis using mobile capture devices, Comput. Electron. Agric, vol.138, pp.200-209, 2017. ,
Lifeclef 2015: multimedia life species identification challenges, Experimental IR Meets Multilinguality, Multimodality, and Interaction, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01182782
CS231n Convolutional Neural Networks for Visual Recognition transfer learning, 2019. ,
Deep learning, Nature, vol.521, p.436, 2015. ,
How deep learning extracts and learns leaf features for plant classification, Pattern Recogn, vol.71, pp.1-13, 2017. ,
Identification of apple leaf diseases based on deep convolutional neural networks, Symmetry, vol.10, p.11, 2017. ,
Using deep learning for image based plant disease detection, Front. Plant Sci, vol.7, p.1419, 2016. ,
Potato disease classification using convolution neural networks, Adv. Anim. Biosci, vol.8, pp.244-249, 2017. ,
Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild, Comput. Electron. Agric, 2018. ,
Effects of host variability on the spread of invasive forest diseases, vol.8, p.80, 2017. ,
Deep learning for image-based cassava disease detection, Int. J. Comput. Vision, vol.8, pp.211-252, 1852. ,
, Very deep convolutional networks for largescale image recognition, 2014.
Deep neural networks based recognition of plant diseases by leaf image classification, Computat. Intell. Neurosci, 2016. ,
Going deeper with convolutions, 2014. ,
Rethinking the inception architecture for computer vision, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.2818-2826, 2016. ,
How convolutional neural networks diagnose plant disease, p.9237136, 2019. ,
A comparative study of fine tuning deep learning models for plant disease identification, Comput. Electron. Agric, 2018. ,
A comparative study of finetuning deep learning models for plant disease identification, Comput. Electron. Agric, vol.161, pp.272-279, 2019. ,
Unbiased look at dataset bias, Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp.1521-1528, 2011. ,
Image set for deep learning: field images of maize annotated with disease symptoms, BMC Res. Notes, vol.11, p.440, 2018. ,
How transferable are features in deep neural networks?, Adv. Neural Inform. Process. Syst, pp.3320-3328, 2014. ,
Understanding neural networks through deep visualization, Deep Learning Workshop, International Conference on Machine Learning (ICML), 2015. ,
Adaptive deconvolutional networks for mid and high level feature learning, ICCV, vol.1, p.6, 2011. ,
Learning deep features for scene recognition using places database, Adv. Neural Inform. Process. Syst, pp.487-495, 2014. ,