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Early prediction of coffee production per plant using morphological indices.

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Title: Early prediction of coffee production per plant using morphological indices.
Authors: de Castro, Gabriel Dumbá Monteiro1 (AUTHOR) gdumbamonteirodecastro@gmail.com, de Queiroz, Daniel Marçal1 (AUTHOR), Valente, Domingos Sárvio Magalhães1 (AUTHOR), Marin, Diego Bedin2 (AUTHOR), Borges, Ryan Moreira1 (AUTHOR)
Source: Precision Agriculture. Feb2026, Vol. 27 Issue 1, p1-24. 24p.
Abstract: Purpose: Coffee farming plays an essential role in the global economy, making accurate productivity prediction methods indispensable for strategic decision-making in the sector. This study aimed to develop models for early prediction of coffee production per plant based on morphological indices. Methods:Two models were proposed using the following attributes: plant height, canopy width, and the number of fruits on the productive internodes of plagiotropic branches. In Model 1, fruit counts were manually conducted at the 4th and 5th productive nodes of the branches, while in Model 2, the average fruit count from the 1st to the 5th productive nodes was obtained automatically through branch image analysis using Detectron2, an open-source object detection library. Both models were developed using data collected at two distinct periods before harvest—the first five months prior and the second three months prior. The research was conducted in three coffee plots in Viçosa, Minas Gerais, Brazil, where 60 plants were selected to evaluate the production prediction model. During harvest, the production of each plant was individually recorded, enabling validation of the predictions. Results: The results revealed a strong correlation between the models and the field-observed production data, especially for the model based on data collected three months before harvest. Model 1 demonstrated a better fit (R² = 0.889; RMSE = 0.923 L/plant; MAE = 0.635 L/plant), while Model 2 had a lower absolute error (R² = 0.747; RMSE = 0.374 L/plant; MAE = 0.460 L/plant). Additionally, productivity maps were generated for each plot, showing good agreement with field-observed productivity data. Conclusions: It was concluded that the proposed models are promising for application in coffee farming, contributing to early production prediction.Highlights: Development of an automated model for counting coffee fruits using smartphone-acquired digital images. Development of two morphological indices to assess the production capacity of coffee plants. Design of two models to predict individual coffee plant production using morphological attributes. Introduction of a practical automated approach for the early prediction of coffee production per plant to support decision-making in precision coffee farming.Impact: This work contributes to the advancement of Precision Agriculture by presenting an innovative approach to the early prediction of coffee production per plant based on morphological indices and digital images. The developed methodology aims to enhance the precision and efficiency of decision-making in coffee farming, optimizing resource use and by leveraging data-driven technologies. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Purpose: Coffee farming plays an essential role in the global economy, making accurate productivity prediction methods indispensable for strategic decision-making in the sector. This study aimed to develop models for early prediction of coffee production per plant based on morphological indices. Methods:Two models were proposed using the following attributes: plant height, canopy width, and the number of fruits on the productive internodes of plagiotropic branches. In Model 1, fruit counts were manually conducted at the 4th and 5th productive nodes of the branches, while in Model 2, the average fruit count from the 1st to the 5th productive nodes was obtained automatically through branch image analysis using Detectron2, an open-source object detection library. Both models were developed using data collected at two distinct periods before harvest—the first five months prior and the second three months prior. The research was conducted in three coffee plots in Viçosa, Minas Gerais, Brazil, where 60 plants were selected to evaluate the production prediction model. During harvest, the production of each plant was individually recorded, enabling validation of the predictions. Results: The results revealed a strong correlation between the models and the field-observed production data, especially for the model based on data collected three months before harvest. Model 1 demonstrated a better fit (R² = 0.889; RMSE = 0.923 L/plant; MAE = 0.635 L/plant), while Model 2 had a lower absolute error (R² = 0.747; RMSE = 0.374 L/plant; MAE = 0.460 L/plant). Additionally, productivity maps were generated for each plot, showing good agreement with field-observed productivity data. Conclusions: It was concluded that the proposed models are promising for application in coffee farming, contributing to early production prediction.Highlights: Development of an automated model for counting coffee fruits using smartphone-acquired digital images. Development of two morphological indices to assess the production capacity of coffee plants. Design of two models to predict individual coffee plant production using morphological attributes. Introduction of a practical automated approach for the early prediction of coffee production per plant to support decision-making in precision coffee farming.Impact: This work contributes to the advancement of Precision Agriculture by presenting an innovative approach to the early prediction of coffee production per plant based on morphological indices and digital images. The developed methodology aims to enhance the precision and efficiency of decision-making in coffee farming, optimizing resource use and by leveraging data-driven technologies. [ABSTRACT FROM AUTHOR]
ISSN:13852256
DOI:10.1007/s11119-025-10313-6