Visão computacional para classificar a maturação dos frutos de café no processo de colheita mecanizada

Abstract

Coffee is one of the most commercialized and consumed agricultural products in the world, fundamental for the socioeconomic development of Brazil. Coffee harvesting is an essential process in the production chain and accounts for approximately half of total production costs. In this sense, this research aimed to classify coffee fruits according to the degree of maturation during the mechanized harvesting process using computer vision techniques. Videos of the harvested coffee fruits were obtained during the mechanized harvesting process in the 2022 harvest. Data collection took place on the Arabica species, variety Bourbon Amarelo, cultivated at Fazenda Cafua in the municipality of Ijaci, located in the southern region of Minas Gerais. For the collection of images, a device installed on the transverse mat of the harvester was developed, with a camera attached to a support to reduce the effects of the vibration of the coffee harvester and with a LED lighting system for illuminating the fruits during harvesting. obtaining the videos. Two approaches were used to process the collected images, (i) with the development of an algorithm using computer vision techniques and (ii) using a state-of-the-art object detection algorithm, YOLOv7. The computer vision algorithm was able to detect and classify coffee fruits according to the following degrees of maturation: unripe and ripe. The average precision for the unripe and mature coffee maturation classes was 72% and 70%. With algorithm it was not possible to classify the fruits of the class too ripe. The object detection algorithm called YOLOv7 was implemented for the detection and classification of coffee fruits into three classes: unripe, ripe and overripe. The YOLOv7 network showed superior capacity with F1-score values of 90%, 95% and 75% for the unripe, mature and overripe classes, respectively. With the classification of the maturation of the harvested coffee fruits, it is possible to obtain an index of fruit maturation during the mechanized harvesting process. In addition, the results of this study can contribute to the development of an embedded system to be used in data collection during mechanized coffee harvesting.