Year: 2024
Type: article
Abstract: Early detection of plant diseases is of great importance for agricultural production and plant health. Early detection is important to prevent the spread of diseases and reduce agricultural losses. The aim of this study is to use artificial intelligence technologies for the early detection of diseased fig plants and reduce agricultural losses. The fig leaf dataset used in the study has two classes: healthy and diseased leaves. There are a total of 2321 images in the dataset. Among these images, there are 1350 images representing diseased leaves and 971 images representing healthy leaves. The dataset is divided into 80% training data and 20% test data. DarkNet-19, ResNet50, VGG-19, VGG-16, ShuffleNet, GoogLeNet, MobileNet-v2, EfficientNet-b0, and DarkNet-53 algorithms were used to analyze the fig leaves dataset using a MATLAB graphical user interface (GUI). The classification accuracy values of each algorithm are as follows: DarkNet-19 90.3%, ResNet50 90.95%, VGG-19 93.32%, VGG-16 92.89%, ShuffleNet 89.44%, GoogLeNet 87.5%, MobileNet-v2 87.5%, EfficientNet-b0 85.56%, and DarkNet53 91.59%. These results evaluate the usability and performance of different algorithms for the early detection of plant diseases. The research emphasizes the importance of the effective use of artificial intelligence technologies in the agricultural industry.
Institution: Selçuk University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4405677236