Deep learning-based segmentation for disease identification-Reference-Cited by-同舟云学术

Deep learning-based segmentation for disease identification

Published:2023-07 Issue: Volume:75 Page:102000
ISSN:1574-9541
Container-title:Ecological Informatics
language:en
Short-container-title:Ecological Informatics

Author:

Mzoughi Olfa,Yahiaoui Itheri

Funder

Prince Sattam bin Abdulaziz University Deanship of Scientific Research

Deanship of Scientific Research, King Saud University

Prince Sattam bin Abdulaziz University

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Ecological Modeling,Modeling and Simulation,Ecology,Ecology, Evolution, Behavior and Systematics

Reference49 articles.

1. An instance segmentation model for strawberry diseases based on mask r-cnn;Afzaal;Sensors,2021

2. Ahmed, N., Asif, H.M., Saleem, G., 2021. Leaf image-based plant disease identification using color and texture features, ArXiv abs/2102.04515.

3. Symptom based automated detection of citrus diseases using color histogram and textural descriptors;Ali;Comput. Electron. Agric.,2017

4. Solving current limitations of deep learning based approaches for plant disease detection;Arsenovic;Symmetry,2019

5. Plant leaf disease classification using efficientnet deep learning model;Atila;Ecol. Inform.,2021

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