Work

Developing machine learning models from multisourced real-world datasets to enhance smart-farming practices

Year: 2025

Type: article

Abstract: Smart farming technologies empower farmers to achieve sustainability by enabling data-driven decision-making. For instance, smart farming can contribute to farm sustainability through optimized pest control. By utilizing pest risk prediction models, farmers can conserve resources and minimize environmental impact by avoiding unnecessary treatments. However, effective crop pest control relies on timely treatments at specific phenological stages. Therefore, the ability to accurately predict phenological development becomes a crucial factor in increasing farm sustainability. This paper describes the design, development and evaluation of a set of Machine Learning models that predict the phenology of grapevines. The models were trained on multisourced data that combine 9 different datasets with different temporal and spatial resolutions. The authors evaluated and compared different machine learning algorithms to predict 9 different phenological stages of grapevines. The models that performed best also included data derived from Sentinel-2 images, which suggests that multispectral satellite images could be used to monitor and predict woody plant phenology. A key contribution of our proposal is the combination of multiple data sources and a fine-grained prediction aimed at distinguishing among 9 phenological states.

Source: Computers and Electronics in Agriculture

DOI: 10.1016/j.compag.2025.110018

Authors: Francisco José Lacueva-Pérez, Rafael del Hoyo-Alonso, Gorka Labata-Leazaún, Juan José Barriuso-Vargas, Sergio Ilarri-Artigas

Institutions: Universidad de Zaragoza, Instituto Tecnológico de Aragón, Instituto de Investigación en Ingeniería de Aragón

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $3,870