Forecast yield prediction of coffee crops: a systematic mapping
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Coffee is one of the most traded agricultural products internationally; in Colombia, it is the first non-mining-energy export product. In this context, the prediction of coffee crop yields is vital for the sector since it allows coffee growers to establish crop management strategies, maximizing their profits or reducing possible losses. This paper addresses crucial aspects of coffee crop yield prediction through a systematic literature review of documents consulted in Scopus, ACM, Taylor & Francis, and Nature. These documents were subjected to a filtering and evaluation process to answer five key questions: predictor variables used, target variable, techniques and algorithms employed, metrics to evaluate the quality of the prediction, and types of coffee reported. The results reveal different groups of predictor variables, including atmospheric, chemical, satellite-derived, fertilizer-related, soil, crop management, and shadow factors. The most recurrent target variable is yield, measured in bean weight per hectare or other measures, with one case considering leaf area. Predominant techniques for yield forecasting include linear regression, random forests, principal component analysis, cluster regression, neural networks, classification and regression trees, and extreme learning machines, among others. The most common metrics to evaluate the quality of predictive models include R², RMSE, MAE, MAPE, MRE, error deviation, Pearson's correlation coefficient, and standard deviation. Finally, robusta, arabica, racemosa, and zanguebariae are the most studied coffee varieties.
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Accepted 2023-08-23
Published 2023-06-26
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