Cargando…

Sugarcane nitrogen nutrition estimation with digital images and machine learning methods

The color and texture characteristics of crops can reflect their nitrogen (N) nutrient status and help optimize N fertilizer management. This study conducted a one-year field experiment to collect sugarcane leaf images at tillering and elongation stages using a commercial digital camera and extract...

Descripción completa

Detalles Bibliográficos
Autores principales:	You, Hui, Zhou, Muchen, Zhang, Junxiang, Peng, Wei, Sun, Cuimin
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2023
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495321/ https://www.ncbi.nlm.nih.gov/pubmed/37697060 http://dx.doi.org/10.1038/s41598-023-42190-2

Ejemplares similares

Estimating canopy nitrogen concentration of sugarcane crop using in situ spectroscopy
por: Reyes-Trujillo, Aldemar, et al.
Publicado: (2021)

SE-VisionTransformer: Hybrid Network for Diagnosing Sugarcane Leaf Diseases Based on Attention Mechanism
por: Sun, Cuimin, et al.
Publicado: (2023)

Effects of sugarcane variety and nitrogen application level on the quality and aerobic stability of sugarcane tops silage
por: Gu, Qichao, et al.
Publicado: (2023)

Data‐driven modeling based on kernel extreme learning machine for sugarcane juice clarification
por: Meng, Yanmei, et al.
Publicado: (2019)

Nitrogen fixation in grasses - gluconacetobacter activates genes in sugarcane
por: Carvalho, Thais LG, et al.
Publicado: (2014)

Biofertilizers can enhance nitrogen use efficiency of sugarcane
por: Qiu, Zhiguang, et al.
Publicado: (2022)

Fertilizer source and application method influence sugarcane production and nutritional status
por: de Castro, Sérgio Gustavo Quassi, et al.
Publicado: (2023)

Soybean leaf estimation based on RGB images and machine learning methods
por: Li, Xiuni, et al.
Publicado: (2023)

Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for Brassica Campestris ssp. Chinensis L.
por: Xiong, Xin, et al.
Publicado: (2019)

Sugarcane extraction machine injuries of the hand
por: Ahmad, Sharifah Roohi
Publicado: (2015)

Nitrogen supply influences photosynthesis establishment along the sugarcane leaf
por: Bassi, Denis, et al.
Publicado: (2018)

The Physiological and Agronomic Responses to Nitrogen Dosage in Different Sugarcane Varieties
por: Yang, Yingying, et al.
Publicado: (2019)

Estimating leaf area index of maize using UAV-based digital imagery and machine learning methods
por: Du, Liping, et al.
Publicado: (2022)

Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework
por: Shen, Longzhu Q., et al.
Publicado: (2020)

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance
por: Aono, Alexandre Hild, et al.
Publicado: (2020)

Nitrogen fertilizer dose alters fungal communities in sugarcane soil and rhizosphere
por: Paungfoo-Lonhienne, Chanyarat, et al.
Publicado: (2015)

Effect of biosolids on the nitrogen and phosphorus contents of soil used for sugarcane cultivation
por: Silva-Leal, Jorge Antonio, et al.
Publicado: (2021)

Effects of Sugarcane and Soybean Intercropping on the Nitrogen-Fixing Bacterial Community in the Rhizosphere
por: Liu, Yue, et al.
Publicado: (2021)

Silicon reduces impact of plant nitrogen in promoting stalk borer (Eldana saccharina) but not sugarcane thrips (Fulmekiola serrata) infestations in sugarcane
por: Keeping, Malcolm G., et al.
Publicado: (2014)

Endophytic nitrogen-fixing bacteria DX120E inoculation altered the carbon and nitrogen metabolism in sugarcane
por: Qin, Ying, et al.
Publicado: (2022)

Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
por: Berger, Katja, et al.
Publicado: (2020)

Digital imaging biomarkers feed machine learning for melanoma screening
por: Gareau, Daniel S., et al.
Publicado: (2016)

Effect of endophytic diazotroph Enterobacter roggenkampii ED5 on nitrogen-metabolism-related microecology in the sugarcane rhizosphere at different nitrogen levels
por: Guo, Dao-Jun, et al.
Publicado: (2023)

Physiological and molecular insights into the resilience of biological nitrogen fixation to applied nitrogen in Saccharum spontaneum, wild progenitor of sugarcane
por: Luo, Ting, et al.
Publicado: (2023)

Provision of nitrogen as ammonium rather than nitrate increases silicon uptake in sugarcane
por: Keeping, Malcolm G., et al.
Publicado: (2014)

Prioritizing Crop Management to Increase Nitrogen Use Efficiency in Australian Sugarcane Crops
por: Thorburn, Peter J., et al.
Publicado: (2017)

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery
por: Li, Xiuhua, et al.
Publicado: (2022)

Estimation of deracinated trees area in temperate forest with satellite images employing machine learning methods
por: Doi, Hideyuki, et al.
Publicado: (2021)

Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms
por: Yamashita, Hiroto, et al.
Publicado: (2020)

Chloroplast Chaperonin: An Intricate Protein Folding Machine for Photosynthesis
por: Zhao, Qian, et al.
Publicado: (2018)

Unraveling Nitrogen Fixing Potential of Endophytic Diazotrophs of Different Saccharum Species for Sustainable Sugarcane Growth
por: Singh, Rajesh Kumar, et al.
Publicado: (2022)

Corrigendum: Effect of endophytic diazotroph Enterobacter roggenkampii ED5 on nitrogen-metabolism-related microecology in the sugarcane rhizosphere at different nitrogen levels
por: Guo, Dao-Jun, et al.
Publicado: (2023)

Nitrogen fluxes at the root-soil interface show a mismatch of nitrogen fertilizer supply and sugarcane root uptake capacity
por: Brackin, Richard, et al.
Publicado: (2015)

Identification of Low-Nitrogen-Related miRNAs and Their Target Genes in Sugarcane and the Role of miR156 in Nitrogen Assimilation
por: Gao, Shiwu, et al.
Publicado: (2022)

UAV imagery data and machine learning: A driving merger for predictive analysis of qualitative yield in sugarcane
por: Barbosa Júnior, Marcelo Rodrigues, et al.
Publicado: (2023)

Risk estimation and risk prediction using machine-learning methods
por: Kruppa, Jochen, et al.
Publicado: (2012)

Machine learning assisted Cameriere method for dental age estimation
por: Shen, Shihui, et al.
Publicado: (2021)

Application of Digital Image Based on Machine Learning in Media Art Design
por: Wu, Ciguli
Publicado: (2021)

A New Method for CTC Images Recognition Based on Machine Learning
por: He, Binsheng, et al.
Publicado: (2020)

Machine Learning in Nutrition Research
por: Kirk, Daniel, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_bs03c0p6p42au1h48idsh75cs4