Computational predictive model for sustainable optimization of Cocoa harvesting by using advanced technological tools
Creators: name: Candamil Cortés, Mariana S
name: Camargo Rodríguez, Anyela V.
name: Arboleda Valencia, Jorge W.
Publisher: name: F1000 Research Limited
Year: 2025

description: The increasing population and the effects of climate change have placed significant pressure on agricultural production systems, driving the need for innovative approaches in order to produce services that face off societal demands. In Colombia, where agriculture serves as the primary economic sector, several bioeconomical initiatives have concentrated on formulating public policies adapted to the country’s unique climatic and socioeconomic conditions. However, these strategies have not yet fully succeeded in meeting these demands in the most effective way.<br>
In particular, cacao is one of the most significant crops, supporting approximately 38,000 families, useful as an alternative to illicit crop cultivation. For this reason, we focused on analysis and technology transfer strategies aimed at promoting sustainable production, zero-waste practices, and raising awareness about the sustainability of the region’s natural resources. Moreover, a computational model was developed to predict cacao crop harvests based on agro-climatic data and images of different growth stages, including flowering, progress, abortion, and ripeness. <br>
In collaboration with the farm "La Esmeralda," located near Belalcázar, Caldas, a model based on climate data was developed. Fifty trees of interest were tagged, and regular visits to Belalcázar were made every 15 days over a six-month period to collect samples. This process allowed for close monitoring of each fruit, from the flowering stage to harvest. Significantly, considering the abortion of fruits provided valuable insights into the overall health of the trees. This data was crucial for developing a model that tracks climate patterns and their influence on fruit development, helping to better predict the health and productivity of the trees.<br>
A second model was developed using image data, focusing on estimating harvest dates accurately. A total of 530 images were used to train the model, with an additional 65 images for validation and 65 for testing. The process started with data labeling using the CVAT platform, followed by the development of the model by fine-tuning the parameters of YOLO v8.0. Multiple training sessions were conducted, adjusting the number of epochs and other key parameters. The model was successfully trained to detect the optimal harvest time, ensuring that the predictions matched the actual harvest, as confirmed by the regular monitoring at the farm.<br>
It should be noted that these technologies and bioinformatics tools, being focused on omics sciences specifically for cacao data, contribute significantly to designing and implementing alternative solutions aimed at improving crop yields and increasing production. Furthermore, these strategies directly address challenges related to cacao quality, productivity, and sustainability. Finally, our model generates valuable information to promote conservation and novel strategies to mitigate climate change based on data science applied.