This study presents a novel approach for rapid grapevine health diagnosis using digital imaging and deep learning. The research aims to develop an intelligent system, supported by user-friendly, open-source software named AI GrapeCare, to detect and prevent grapevine diseases. The system utilizes RGB imagery and hybrid deep networks, including convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks such as VGG16, VGG19, ResNet50, and ResNet101V2. A gray level co-occurrence matrix (GLCM) is used to measure textural characteristics. The plant disease detection platform (PDD) created a dataset of real-life grape leaf images from vineyards to improve plant disease identification. Data augmentation techniques were applied to address the issue of limited images. The augmented dataset was used to train the models, enhancing their ability to accurately identify and classify plant diseases in real-world scenarios. The combined CNN_RGB-LSTM_GLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed other models, achieving a validation accuracy of 96.6%, classification precision of 96.6%, recall of 96.6%, and F-measure of 96.6%. The software developed through this approach holds great promise as a rapid tool for diagnosing grapevine diseases in less than one minute. The framework has potential for future expansion to include various types of trees, aiding farmers in early detection of tree diseases and enabling preventive measures. The study also highlights the effectiveness of deep learning in plant disease diagnosis, with the proposed model achieving high accuracy in classifying grapevine diseases. The AI GrapeCare software was developed to analyze digital images of grape health, utilizing Python and various libraries for image processing and machine learning. The software provides a user-friendly interface for loading and analyzing images, extracting features, and assessing grape health conditions. The system's performance was evaluated using metrics such as precision, recall, accuracy, intersection over union, and F-measure, demonstrating its effectiveness in grapevine disease diagnosis. The study concludes that the proposed hybrid approach, combining deep learning networks and GLCM features, is a promising solution for rapid and accurate grapevine health diagnosis.This study presents a novel approach for rapid grapevine health diagnosis using digital imaging and deep learning. The research aims to develop an intelligent system, supported by user-friendly, open-source software named AI GrapeCare, to detect and prevent grapevine diseases. The system utilizes RGB imagery and hybrid deep networks, including convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks such as VGG16, VGG19, ResNet50, and ResNet101V2. A gray level co-occurrence matrix (GLCM) is used to measure textural characteristics. The plant disease detection platform (PDD) created a dataset of real-life grape leaf images from vineyards to improve plant disease identification. Data augmentation techniques were applied to address the issue of limited images. The augmented dataset was used to train the models, enhancing their ability to accurately identify and classify plant diseases in real-world scenarios. The combined CNN_RGB-LSTM_GLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed other models, achieving a validation accuracy of 96.6%, classification precision of 96.6%, recall of 96.6%, and F-measure of 96.6%. The software developed through this approach holds great promise as a rapid tool for diagnosing grapevine diseases in less than one minute. The framework has potential for future expansion to include various types of trees, aiding farmers in early detection of tree diseases and enabling preventive measures. The study also highlights the effectiveness of deep learning in plant disease diagnosis, with the proposed model achieving high accuracy in classifying grapevine diseases. The AI GrapeCare software was developed to analyze digital images of grape health, utilizing Python and various libraries for image processing and machine learning. The software provides a user-friendly interface for loading and analyzing images, extracting features, and assessing grape health conditions. The system's performance was evaluated using metrics such as precision, recall, accuracy, intersection over union, and F-measure, demonstrating its effectiveness in grapevine disease diagnosis. The study concludes that the proposed hybrid approach, combining deep learning networks and GLCM features, is a promising solution for rapid and accurate grapevine health diagnosis.