Repositorio
Institucional

Cravero, Ania

Pardo, Sebastian

Sepulveda, Samuel

Munoz, Lilia

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

MDPI

AGRONOMY-BASEL

en

Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address problems such as farmers' decision making, water management, soil management, crop management, and livestock management. Crop management includes yield prediction, disease detection, weed detection, crop quality, and species recognition. On the other hand, livestock management considers animal welfare and livestock production. The purpose of this paper is to synthesize the evidence regarding the challenges involved in implementing machine learning in agricultural Big Data. We conducted a systematic literature review applying the PRISMA protocol. This review includes 30 papers published from 2015 to 2020. We develop a framework that summarizes the main challenges encountered, machine learning techniques, and the leading technologies used. A significant challenge is the design of agricultural Big Data architectures due to the need to modify the set of technologies adapting the machine learning techniques as the volume of data increases.

artículo de revisión

10.3390/agronomy12030748

PDF

Big Data

machine learning

agriculture

challenges

systematic literature review

PRECISION AGRICULTURE

OPPORTUNITIES

Agronomía

Ciencias de las plantas

Big Data

aprendizaje automático

agricultura

desafíos

revisión sistemática de la literatura

AGRICULTURA DE PRECISIÓN

OPORTUNIDADES

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

artículo de revisión

version publicada

CC BY 4.0

CC BY 4.0

acceso abierto

acceso abierto

WOS:000775411000001