Deskripsi
Nowadays, the process of identifying plants is still manual and fraught with difficulties owing to human nature. Human nature contains a flaw that renders the desired outcome ineffectual. Another issue is that strawberry plant diseases such as tipburn and leaf spots can damage growth and crop quality and impact the agricultural economy. So the researchers created a Deep Learning model using the Convolutional Neural Network (CNN) VGG16 Algorithm and a dataset of 2897 photos to classify tipburn, leaf spot, and, the healthy state of strawberry plant leaves. To minimize overfitting in classification training, the training dataset will be included. This is done so that the model can recognize the fundamental variance of the strawberry leaf picture object and achieve training and validation accuracy of 95.05% and 97.4%, respectively. Thus, the training loss value is 19.68%, whereas the validation loss value is just 7.54%. The finding accuracy was greater than 90% for both training and validation parameters. This research is expected to be valuable in giving information on the process of data augmentation and disease classification in strawberry plants
