Deep Learning for Plant Disease Identification from Disease Region Images

2020 ◽  
pp. 65-75
Author(s):  
Aliyu Muhammad Abdu ◽  
Musa Mohd Mokji ◽  
Usman Ullah Sheikh
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Disease Identification ◽  
Disease Region

A comparative study of fine-tuning deep learning models for plant disease identification

2019 ◽  
Vol 161 ◽  
pp. 272-279 ◽  
Author(s):  
Edna Chebet Too ◽  
Li Yujian ◽  
Sam Njuki ◽  
Liu Yingchun
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Plant Disease ◽  
Fine Tuning ◽  
Learning Models ◽  
Disease Identification

scholarly journals Plant disease identification using explainable 3D deep learning on hyperspectral images

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Koushik Nagasubramanian ◽  
Sarah Jones ◽  
Asheesh K. Singh ◽  
Soumik Sarkar ◽  
Arti Singh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Hyperspectral Images ◽  
Disease Identification

Plant disease identification from individual lesions and spots using deep learning

2019 ◽  
Vol 180 ◽  
pp. 96-107 ◽  
Author(s):  
Jayme Garcia Arnal Barbedo
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Disease Identification

scholarly journals A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification

2021 ◽  
Vol 11 (4) ◽  
pp. 1878
Author(s):  
Zhirui Luo ◽  
Qingqing Li ◽  
Jun Zheng
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Plant Disease ◽  
Deep Neural Networks ◽  
Economic Losses ◽  
Significant Delay ◽  
Disease Identification

Transfer learning using pre-trained deep neural networks (DNNs) has been widely used for plant disease identification recently. However, pre-trained DNNs are susceptible to adversarial attacks which generate adversarial samples causing DNN models to make wrong predictions. Successful adversarial attacks on deep learning (DL)-based plant disease identification systems could result in a significant delay of treatments and huge economic losses. This paper is the first attempt to study adversarial attacks and detection on DL-based plant disease identification. Our results show that adversarial attacks with a small number of perturbations can dramatically degrade the performance of DNN models for plant disease identification. We also find that adversarial attacks can be effectively defended by using adversarial sample detection with an appropriate choice of features. Our work will serve as a basis for developing more robust DNN models for plant disease identification and guiding the defense against adversarial attacks.

Classification of plant disease using SVM and deep learning

2021 ◽  
Author(s):  
K. Thaiyalnayaki ◽  
Christeena Joseph
Keyword(s):  
Deep Learning ◽  
Plant Disease
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