Field evaluation of a deep learning-based smart variable-rate sprayer for targeted application of agrochemicals-Reference-Cited by-同舟云学术

Field evaluation of a deep learning-based smart variable-rate sprayer for targeted application of agrochemicals

Published:2023-02 Issue: Volume:3 Page:100073
ISSN:2772-3755
Container-title:Smart Agricultural Technology
language:en
Short-container-title:Smart Agricultural Technology

Author:

Farooque Aitazaz A.,Hussain Nazar,Schumann Arnold W.,Abbas Farhat,Afzaal Hassan,McKenzie-Gopsill Andrew,Esau Travis,Zaman Qamar,Wang Xander

Publisher

Elsevier BV

Reference36 articles.

1. Crop losses to pests;Oerke;J. Agric. Sci.,2006

2. Principles of Weed Science;Rao,2000

3. Development and evaluation of a general model for yield loss assessment in potatoes;Shtienberg;Phytopathology,1990

4. Potatoes and human health;Camire,2016

5. Effect of various densities of lamb's-quarters and it's emergence times on physiological characteristics of lamb's-quarters and marigold flower yield;Mirshekari;World Appl. Sci. J.,2012

Cited by 16 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. 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. Advances in ground robotic technologies for site-specific weed management in precision agriculture: A review;Computers and Electronics in Agriculture;2024-10

3. Advances in Sustainable Crop Management: Integrating Precision Agriculture and Proximal Sensing;AgriEngineering;2024-09-02

4. Computer vision in smart agriculture and precision farming: Techniques and applications;Artificial Intelligence in Agriculture;2024-09

5. Advancing precision agriculture: domain-specific augmentations and robustness testing for convolutional neural networks in precision spraying evaluation;Neural Computing and Applications;2024-08-12