scholarly journals DeepCov19Net: Automated COVID-19 Disease Detection with a Robust and Effective Technique Deep Learning Approach

2022 ◽  
Author(s):  
Fatih Demir ◽  
Kürşat Demir ◽  
Abdulkadir Şengür
Keyword(s):  
Deep Learning ◽  
Disease Detection ◽  
Learning Approach ◽  
Effective Technique

Plant Disease Detection based on Deep Learning Approach

2021 ◽  
Author(s):  
Faizan Akhtar ◽  
N. Partheeban ◽  
A. Daniel ◽  
Srinivasan Sriramulu ◽  
Saloni Mehra ◽  
...  
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Disease Detection ◽  
Learning Approach

A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves

2021 ◽  
Vol 183 ◽  
pp. 106042
Author(s):  
Jaemyung Shin ◽  
Young K. Chang ◽  
Brandon Heung ◽  
Tri Nguyen-Quang ◽  
Gordon W. Price ◽  
...  
Keyword(s):  
Deep Learning ◽  
Powdery Mildew ◽  
Disease Detection ◽  
Learning Approach ◽  
Powdery Mildew Disease ◽  
Rgb Image

scholarly journals Mobile-based Deep Learning Models for Banana Disease Detection

2020 ◽  
Vol 10 (3) ◽  
pp. 5674-5677 ◽  
Author(s):  
S. L. Sanga ◽  
D. Machuve ◽  
K. Jomanga
Keyword(s):  
Deep Learning ◽  
Food Security ◽  
Early Detection ◽  
Smallholder Farmers ◽  
Disease Detection ◽  
Learning Approach ◽  
Learning Models ◽  
Banana Production ◽  
Pests And Diseases ◽  
Learning Architectures

In Tanzania, smallholder farmers contribute significantly to banana production and Kagera, Mbeya, and Arusha are among the leading regions. However, pests and diseases are a threat to food security. Early detection of banana diseases is important to identify the diseases before too much damage is done on the plants. In this paper, a tool for early detection of banana diseases by using a deep learning approach is proposed. Five deep learning architectures, namely Vgg16, Resnet18, Resnet50, Resnet152 and InceptionV3 were used to develop models for banana disease detection, achieving all high accuracies, varying from 95.41% for InceptionV3 to 99.2% for Resnet152. InceptionV3 was selected for mobile deployment because it demands much less memory. The developed tool was capable of detecting diseases with a confidence of 99% of the captured leaves from the real environment. This tool will help smallholder farmers conduct early detection of banana diseases and improve their productivity.

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