ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Alma Yolanda Alanis García
Universidad de Guadalajara
0000-0001-9600-779X
Ricardo R. Vázquez
Universidad de Guadalajara
Acerca de
Continuous monitoring of agricultural crops has traditionally been carried out through direct human supervision, either empirically or through instrumentation, this way in which agricultural workers monitor states of their production. This activity is very laborious since it involves constantly taking measurements and visual verifications during practically the entire evolutionary period within the crop to have certainty in the production yields for each established agricultural cycle.This article describes a method that uses a differential neural network that identifies the stages dynamic evolution; available water resource, amount used by plant and amount of material that fulfilled its life cycle, biomass, and that with accurate data it is possible to have better crop performance in each agricultural cycle as well as the care of the inputs and resources related to this important human activity.
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