This paper presents an innovative approach to rapid grapevine health diagnosis using digital imaging and deep learning. The authors developed a user-friendly, open-source software called AI GrapeCare, which utilizes RGB imagery and hybrid deep networks to detect and prevent grapevine diseases. The study involved combining convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks (VGG16, VGG19, ResNet50, and ResNet101V2) to enhance disease detection accuracy. A gray level co-occurrence matrix (GLCM) was used to measure textural characteristics, and a dataset of real-life grape leaf images was created to improve plant disease identification. Data augmentation techniques were applied to address the issue of limited images, and the augmented dataset was used to train the models. The results showed that the combined CNNRGB-LSTMGLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed other models with a validation accuracy of 96.6%, classification precision of 96.6%, recall of 93.4%, and F-measure of 93.4%. The software developed through this approach holds promise as a rapid tool for diagnosing grapevine diseases in less than one minute, with potential for future expansion to include various types of trees. The study highlights the effectiveness of deep learning in precision agriculture and the importance of integrating multimodal data and advanced deep network architectures for improved disease detection and management.This paper presents an innovative approach to rapid grapevine health diagnosis using digital imaging and deep learning. The authors developed a user-friendly, open-source software called AI GrapeCare, which utilizes RGB imagery and hybrid deep networks to detect and prevent grapevine diseases. The study involved combining convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks (VGG16, VGG19, ResNet50, and ResNet101V2) to enhance disease detection accuracy. A gray level co-occurrence matrix (GLCM) was used to measure textural characteristics, and a dataset of real-life grape leaf images was created to improve plant disease identification. Data augmentation techniques were applied to address the issue of limited images, and the augmented dataset was used to train the models. The results showed that the combined CNNRGB-LSTMGLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed other models with a validation accuracy of 96.6%, classification precision of 96.6%, recall of 93.4%, and F-measure of 93.4%. The software developed through this approach holds promise as a rapid tool for diagnosing grapevine diseases in less than one minute, with potential for future expansion to include various types of trees. The study highlights the effectiveness of deep learning in precision agriculture and the importance of integrating multimodal data and advanced deep network architectures for improved disease detection and management.