Early detection of nicosulfuron toxicity and physiological prediction in maize using multi-branch deep learning models and hyperspectral imaging-Reference-Cited by-同舟云学术

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Early detection of nicosulfuron toxicity and physiological prediction in maize using multi-branch deep learning models and hyperspectral imaging

Published:2024-08 Issue: Volume:474 Page:134723
ISSN:0304-3894
Container-title:Journal of Hazardous Materials
language:en
Short-container-title:Journal of Hazardous Materials

Author:

Xiao Tianpu,Yang Li,Zhang Dongxing,Cui Tao,Zhang Xiaoshuang,Deng Ying,Li Hongsheng,Wang Haoyu

Publisher

Elsevier BV

Reference40 articles.

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2. Long-term trends in the intensity and relative toxicity of herbicide use;Kniss;Nat Commun,2017

3. Impact of climate change factors on weeds and herbicide efficacy;Aruna;Adv Agron,2016

4. Evaluation of the management practice for controlling herbicide runoff from paddy fields using intermittent and spillover-irrigation schemes;Watanabe;Paddy Water Environ,2006

5. Application of a plant biostimulant to improve maize (Zea mays) tolerance to metolachlor;Panfili;J Agric Food Chem,2019

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