Abstract
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.
Highlights
IntroductionSmart or Precision Agriculture (PA) refers to information and technology-based agricultural management systems (e.g., remote sensing, geographic information systems, global positioning systems, or robotics) for a more efficient soil and crop management based on their identified or estimated conditions [1]
Smart or Precision Agriculture (PA) refers to information and technology-based agricultural management systems for a more efficient soil and crop management based on their identified or estimated conditions [1]
We propose a non-destructive model for the estimation of cherry coffee production based on sensor variables, time series and machine learning (ML) algorithms
Summary
Smart or Precision Agriculture (PA) refers to information and technology-based agricultural management systems (e.g., remote sensing, geographic information systems, global positioning systems, or robotics) for a more efficient soil and crop management based on their identified or estimated conditions [1]. CMC, 2022, vol., no.3 precise and accurate use of operations and resources considering georeferenced information, and monitored characteristics of soil, plant, and climate, reducing costs and waster, and increasing the quality of the final product. Recent advances in sensor technologies, Big Data, Internet of Things (IoT), Artificial Intelligence (AI) and machine learning approaches has shown a great potential to advance PA and achieve accurate predictions [3,4,5]. The importance of coffee production is evidenced in the economic and social level. In Colombia around 540,000 families depend on coffee production as income source [6,7]. The department of Cauca is one of the most relevant regions in coffee production, with more than 93,000 coffee growing families and generating 65,500 agricultural jobs [8]
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