枯萎病
变压器
深度学习
人工智能
农业
机器学习
计算机科学
农学
工程类
生物
电压
生态学
电气工程
作者
Li-Hua Li,Radius Tanone
标识
DOI:10.1109/imcom56909.2023.10035609
摘要
One of Indonesia's mainstay agricultural products is the potato. Disease prevention is essential for maintaining stable potato production. One technique for detecting disease in potatoes is to determine whether potato leaves are diseased (early blight or late blight) or healthy. Deep Learning models have been widely developed and used to classify disease recognition in potato leaves in the industrial era 4.0. Swin Transformer is a deep learning model based on transformers that is more efficient and accurate at solving classification problems. The Swin Transformer, a transformer based deep learning approach, is used in this study to identify diseases of the potato leaf. Moreover, several metrics including Precision, Recall, Accuracy, and F1 score, are used to assess the experimental results of the model we use. In terms of accuracy, the value obtained when training with this model is 97.70%. These findings indicate that using the Swin Transformer model to identify potato leaf diseases could be a new trend in agricultural research.
科研通智能强力驱动
Strongly Powered by AbleSci AI