A survey of deep learning techniques for weed detection from images-Reference-Cited by-同舟云学术

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A survey of deep learning techniques for weed detection from images

Published:2021-05 Issue: Volume:184 Page:106067
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Hasan A S M Mahmudul,Sohel Ferdous,Diepeveen Dean,Laga Hamid,Jones Michael G.K.

Publisher

Elsevier BV

Subject

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

Reference176 articles.

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

2. Learning semantic graphics using convolutional encoder-decoder network for autonomous weeding in paddy field;Adhikari;Front. Plant Sci.,2019

3. Visual features based boosted classification ol weeds for real-time selective herbicide sprayer systems;Ahmad;Comput. Ind.,2018

4. Real-time machine-learning based crop/weed detection and classification for variable-rate spraying in precision agriculture;Alam,2020

5. A state-ol-the-art survey on deep learning theory and architectures;Alom;Electronics,2019

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1. Development and evaluation of a machine vision and deep learning-based smart sprayer system for site-specific weed management in row crops: An edge computing approach;Journal of Agriculture and Food Research;2024-12

2. Field-based multispecies weed and crop detection using ground robots and advanced YOLO models: A data and model-centric approach;Smart Agricultural Technology;2024-12

3. Real-time lettuce-weed localization and weed severity classification based on lightweight YOLO convolutional neural networks for intelligent intra-row weed control;Computers and Electronics in Agriculture;2024-11

4. Lightweight cabbage segmentation network and improved weed detection method;Computers and Electronics in Agriculture;2024-11

5. Barley disease recognition using deep neural networks;European Journal of Agronomy;2024-11

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