A Framework for Weed Detection in Agricultural Fields Using Image Processing and Machine Learning Algorithms-Reference-Cited by-同舟云学术

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A Framework for Weed Detection in Agricultural Fields Using Image Processing and Machine Learning Algorithms

Published:2022-07-21 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP)
language:
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Author:

Maram Balajee¹,Das Smritilekha²,Daniya T³,Cristin R⁴

Affiliation:

1. School of Engineering and Technology-CSE, Chitkara University,Baddi,Himachal Pradesh

2. Koneru Lakshmaiah Edu. Foundation,Department of CSE,Guntur,Andhra Pradesh,India

3. GMR Institute of Technology,Department of IT,Andhra Pradesh,India

4. GMR Institute of Technology,Department of CSE,Andhra Pradesh,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9862308/9862027/09862451.pdf?arnumber=9862451

Reference22 articles.

1. Hyperspectral imaging combined with machine learning as a tool to obtain high‐throughput plant salt‐stress phenotyping

2. Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

3. Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning

4. Yield Prediction for Tomato Greenhouse Using EFuNN

5. Weed Recognition for Depthwise Separable Network Based on Transfer Learning

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A comprehensive survey on weed and crop classification using machine learning and deep learning;Artificial Intelligence in Agriculture;2024-09

2. Weed detection and classification in sesame crops using region-based convolution neural networks;Neural Computing and Applications;2024-08-01

3. Optimizing Crop Management: Customized CNN for Autonomous Weed Identification in Farming;2024 International Conference on Integrated Circuits and Communication Systems (ICICACS);2024-02-23

4. Analyzing Performance of Masked R-CNN Under the Influence of Distortions;Innovations in Computational Intelligence and Computer Vision;2023

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