An image segmentation method based on deep learning for damage assessment of the invasive weed Solanum rostratum Dunal-Reference-Cited by-同舟云学术

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An image segmentation method based on deep learning for damage assessment of the invasive weed Solanum rostratum Dunal

Published:2021-09 Issue: Volume:188 Page:106320
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Wang Qifan,Cheng Man,Xiao Xuepeng,Yuan Hongbo,Zhu Jiajun,Fan Caihu,Zhang Jinlin

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference30 articles.

1. Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure;Abdalla;Comput. Electron. Agric.,2019

2. Bag-of-visual-words-augmented Histogram of Oriented Gradients for efficient weed detection;Abouzahir;Biosyst. Eng.,2021

3. Classification of crops and weeds from digital images: A support vector machine approach;Ahmed;Crop Prot.,2012

4. Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network;Asad;Inf. Process. Agric.,2020

5. Weed density classification in rice crop using computer vision;Ashraf;Comput. Electron. Agric.,2020

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1. Research on improvement strategies for a lightweight multi-object weed detection network based on YOLOv5;Crop Protection;2024-12

2. New segmentation approach for effective weed management in agriculture;Smart Agricultural Technology;2024-08

3. SoybeanTracer: An In-Field scene property-based framework for high-throughput soybean canopy coverage extraction and evaluation;Computers and Electronics in Agriculture;2024-05

4. Enhanced deep learning technique for sugarcane leaf disease classification and mobile application integration;Heliyon;2024-04

5. WeedVision: A single-stage deep learning architecture to perform weed detection and segmentation using drone-acquired images;Computers and Electronics in Agriculture;2024-04

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