Comparison of EfficientNet B5-B6 for Detection of 29 Diseases of Fruit Plants - Terisia, Vany; Hastomo, Widi; Karno, Adhitio Satyo Bayangkari; Sestri, Ellya; Yusuf, Diana; Arman, Shevty Arbekti; Kamilia, Nada (Sainteks, 2023)
In initiatives to meet food needs and enhance the wellbeing of farmers and society at large, crop production performance is essential. For early attempts to be made for quick handling to prevent crop failure, farmers must be able to readily and quickly receive information in order to detect plant illnesses. In this study, two Convolutional Neural Network (CNN) architectures namely, EfficientNet versions B5 and B6 are used to develop a classification model for plant disease using Deep Learning (DL). The 66,556 visuals in the dataset, which is from Kaggle.com, are used. To create a model, the training method uses 57,067 images data and 3,170 image data for validation. The EfficientNet architecture versions B5 and B6 received very good accuracy scores for the total test results, namely 0.9905 and 0.9927. The model testing phase was carried out through testing phases utilising 3.171 images data. Future analysis can compare CNN architectures and try it with different datasets.