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Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network by Ilham Fadilah is a document available to read on EtoBox.

What is Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network about?

This article presents a deep learning approach using Convolutional Neural Networks (CNN) for the detection and classification of tomato crop diseases. The proposed model, which includes two convolutional and two pooling layers, achieved a training accuracy of 98% and a testing accuracy of 88.17%, outperforming pre-trained models like InceptionV3, ResNet 152, and VGG19. The study highlights the importance of timely disease detection in agriculture to improve crop yield and reduce economic losses for farmers.

Author

Ilham Fadilah

Language

EN