Penulis: Moechammad Sarosa, Puteri Nurul Ma’rifah, Mila Kusumawardani, Dimas Firmanda Al Riza

Nama Jurnal: AES International Journal of Artificial Intelligence (IJ-AI)

Tahun: 2024

Volume: 14

Issue: 1

Halaman: 222-230

Deskripsi: The shapes of leaves distinguish the Indonesian grape variants. The grape leaves might look the same at first glance, but there are differences in leaf shapes and characteristics when observed closely. This research uses a deep learning method combined with the fasterregion-based convolutional neural network (R-CNN) algorithm with the Inception network architecture, ResNet V2, ResNet-152, ResNet-101, and ResNet-50, and uses COCO weights trained to classify five grape varieties through leaf images. The study collected 500 images to be used as an independent dataset. The results show that network improvements can effectively improve operating efficiency. There are also limitations to training scores because the F1 score value tends to stabilize or decrease at a certain point. In the Inception ResNet V2 architecture, with the highest average F1 score of 92%, the average computing time for training and testing is longer than other network architectures. This suggests that the algorithm can classify types of grapes based on their leaves.

Pendahuluan: Grapes belong to the Vitaceaefamily [1], which are known to have a number of health benefits [2], [3]. It is imperative to understand the grape variant to determine the best cultivation technique, possible quality, and commercial value [4], [5]. Grape growers are working on ensuring a precise identification of grapes varieties, as well as determining how to grow cuttings based on each variety and calculating its supply price. The varieties of grapes can be distinguished on the basis of their leaf shapes [6]. Grape growers are trying to find the best way to find a precise identification of grape varieties, as well as to determine the best cultivation technique for different types of grape variants with the best commercial values. Various studies have designed different methods of classifying leaves of various types of plants over the last few years. Some of these methods are mask algorithmregion-based convolutional neural network(R-CNN)and VGG16 used to distinguish leaf shapes [7], the convolutional neural network (CNN) technique [8], CNN to analyze leaf disease [9], [10], and the standard ResNet-50 CNN model’s attention residual learning strategy (AResNet-50)[11].Deep learning techniques, which help classify objects more accurately, are used by most studies to classify plant leaves. Using deep learning methods for image recognition and also classification, has widely spread in research [12]–[14]. Another classification method, CNNis one of the most common and widely used deep learning models that have been proven to have good performance due to their excellent capability of learning properties of an object using a large number of network architectures [15], [16]. Meanwhile, a new method suggested by Liuet al.[17], Faster R-CNN, is currently being developed.Grape leaf variants are 223identified using a deep learning method with the Faster R-CNN algorithm combined with the Inception ResNet V2, ResNet-152, ResNet-101, and ResNet-50 network architecture and use pre-trained COCO weights. This research employs five types of grape leaves, namely academic, jupiter, local, taldun, and transfiguration.

Kata Kunci: Grape plant, Grape varieties, Inception ResNet, Leaf detection, ResNet