Identification types of plant using convolutional neural network - Notonegoro, Radityo Hendratmojo Jati; Hustinawaty, Hustinawaty (Institute of Advanced Engineering and Science, 2025)
Artificial intelligence can be implemented in fields that related to environmental education by providing knowledge for taxonomy which recognize and identify plant species based on its features. The variety of plant species that inhabit in a certain area allows many plant species to be found that look similar so that difficult to distinguish and recognize a particular plant. Convolutional neural network (CNN) often used in object detection, you only look once (YOLO), one of CNN’s object detections, could identify object in real time and obtained good performance and accuracy in several researched. However, no studies have ever identified a plant from its flowers, leaves, and fruits. Therefore, the main object of this paper is identified types of plant with CNN (YOLOv8). The YOLOv8 model with 0.01 learning rate, 32 batch size, stochastic gradient descent (SGD) optimizer obtained highest precision of 69.62% and F1 score of 61.22%, recall of 54.73%, mAP50 and mAP50 – 90 on the training data of 57.61% and 42.49%.