Recognition and classification system for trinitario cocoa fruits according to their ripening stage based on the Yolo v5 algorithm

Abstract

The objective of this research is the recognition and classification of the ripening state of trintario cocoa, based on the artificial vision technique YOLO-v5, executed in the Google Colab and MiniConda environment. The methodology contemplates preprocessing, processing and post-processing; in the first one, data acquisition, annotation and augmentation are performed; in the second one, the neural network architecture and the execution code are precise; finally, the model accuracy is determined and inferences are made through image and video tests in real time. The database contains 1286 training images collected in VRAEM fields, which were augmented using the novel Mosaic-12 method, which consists of improving the data with respect to the 4-mosaic model. The accuracy results for the model trained with the improved database is 60.2% and for the model with the unimproved database is 56%, confirming the technical value of the proposed method, achieving the recognition and classification of Trinitario cocoa according to its ripening stage in real time.