PREDICTIVE MODELS OF CHLOROPHYLL CONTENT IN SUGARCANE SEEDLINGS USING SPECTRAL IMAGES
Author:
Affiliation:
1. Federal University of Minas Gerais, Brazil
2. Federal University of the São Francisco Valley, Brazil
3. Federal Institute of Triangulo Mineiro, Brazil
Publisher
FapUNIFESP (SciELO)
Subject
Agricultural and Biological Sciences (miscellaneous)
Reference29 articles.
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3. Development of pre-sprouted seedlings (PSS) of sugarcane under different amounts of filter cake and application modes;Aquino GS;Ciências Agrárias,2018
4. Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat;Beresel JP;Computers and Electronics in Agriculture,2017
5. Water stress reveals differential antioxidant responses of tolerant and non-tolerant sugarcane genotypes;Boaretto LF;Plant Physiology and Biochemistry,2014
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3. Simultaneous retrieval of sugarcane variables from Sentinel-2 data using Bayesian regularized neural network;International Journal of Applied Earth Observation and Geoinformation;2023-02
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