Implementation of YOLOv5 for Real-Time Maturity Detection and Identification of Pineapples

The assurance of fruit freshness during harvest is an enduring challenge that is faced by farmers and suppliers. Traditional methodologies, currently utilized for evaluating fruit freshness, have been characterized by their time-consuming nature, high costs, labor-intensity, and susceptibility to inaccuracies. To address these issues, machine-based detection and sorting systems have been proposed, offering increased efficiency by leveraging technological advancements to analyze fruit attributes such as color, texture, physical appearance, size, and shape. These attributes are critical determinants of fruit quality and value, making accurate fruit evaluation a necessity in the agricultural and food industries. This is particularly true for products such as organic juices and jams, which have significant implications for human health. Providing unfit fruits not only affects the economy adversely but also contributes to augmented carbon dioxide emissions. This work presents a novel technical solution for the detection of pineapple freshness on Vietnamese farms, employing the Fast R-CNN and YOLOv5 techniques. The YOLO model was trained on a diverse dataset, comprised of over a hundred object categories and 50,000 preprocessed images. An aggregated model, combining the outputs of the pre-trained and transfer models, demonstrated improved performance while reducing training time, owing to the extensive training dataset. The classifier displayed an impressive accuracy sensitivity of 94.5% when tested on 50,000 images. Experimental results validate the superior performance of the trained YOLOv5s model, which attains a ripe pineapple recognition accuracy of 98%, outperforming Faster R-CNN by 9.27% and trailing behind YOLOv5x by a mere 0.22%. Additionally, the YOLOv5s model exhibits an impressive detection speed, requiring only 9.2ms to detect a single image-67.88% faster than Faster R-CNN and only 34.06% slower than YOLOv5x. These findings confirm that the YOLOv5s target recognition model meets the requirements for accurate recognition and high-speed processing of ripe pineapples. Its compatibility with agricultural-embedded mobile devices makes it a prime candidate for supporting precision operations in ripe pineapple harvesting machines.