Recognition of Maize Phenology in Sentinel Images with Machine Learning.

ABSTRACT: The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image processing and the application of machine learning algorithms are technological strategies with which developed countries tend to implement better public policies regarding the efficient use of water. The purpose of this research was to determine the main indicators and characteristics that allow us to discriminate the phenological stages of maize crops (Zea mays L.) in Sentinel 2 satellite images through supervised classification models. The training data were obtained by monitoring cultivated plots during an agricultural cycle. Indicators and

SUBMITTER: Murguia-Cozar A 

PROVIDER: S-EPMC8747376 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature