This study implements a methodology based on advanced technologies and collaboration with agricultural sector experts to analyze the growth and development of cacao fruits. A field image repository was built to document the progress and harvest stages from CCN51 and ICS95 cocoa varieties, enabling the creation of a structured database. A labeling process was then carried out with the guidance of cultivation specialists, ensuring proper fruit categorization. Using this database, experiments were conducted with YOLO architecture to classify images into different both stages. Additionally, the model was tested in two computational architectures to ensure repeatability and reproducibility of results. It was found that for Google Colab the precision was 75% for ripe and 77% for progress compared to Tayra HPC node results with 77% in ripe 72% in progress. The application of these technologies and bioinformatics tools, specifically to cacao cultivation, significantly contributes to improving agricultural yield and increasing production. Furthermore, they generate valuable information for the application to identify cocoa ripeness and support cocoa farmers to have harvest planning strategies.