A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production
Metadata
Show full item recordEditorial
Tech Science Press
Materia
Cherry coffee Production estimation Learner Approaches Time series Weather data Crop management data
Date
2021-10-11Referencia bibliográfica
Rodríguez, J. P... [et al.] (2022). A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production. CMC-Computers, Materials & Continua, 70(3), 4725–4743. DOI:[10.32604/cmc.2022.019135]
Sponsorship
Departamento Administrativo de Ciencia, Tecnologia e Innovacion Colciencias; Innovaccion-Cauca (SGR-Colombia) 4633 04C-2018Abstract
Coffee plays a key role in the generation of rural employment in
Colombia. More than 785,000 workers are directly employed in this activity,
which represents the 26% of all jobs in the agricultural sector. Colombian
coffee growers estimate the production of cherry coffee with the main aim of
planning the required activities, and resources (number of workers, required
infrastructures), anticipating negotiations, estimating, price, and foreseeing
losses of coffee production in a specific territory. These important processes
can be affected by several factors that are not easy to predict (e.g., weather
variability, diseases, or plagues.). In this paper, we propose a non-destructive
time series model, based on weather and crop management information, that
estimate coffee production allowing coffee growers to improve their management
of agricultural activities such as flowering calendars, harvesting seasons,
definition of irrigation methods, nutrition calendars, and programming the
times of concentration of production to define the amount of personnel
needed for harvesting. The combination of time series and machine learning
algorithms based on regression trees (XGBOOST, TR and RF) provides very
positive results for the test dataset collected in real conditions for more than a
year. The best results were obtained by the XGBOOST model (MAE = 0.03;
RMSE = 0.01), and a difference of approximately 0.57% absolute to the main
harvest of 2018.