Rice plant disease detection using Convolutional Neural Networks (CNN) and the VGG16 architecture is a popular research topic in the field of computer vision and agriculture. The goal of this research is to develop a model that can accurately identify different types of rice plant diseases based on images of the plant. CNN is a deep learning architecture commonly used for image recognition tasks. The VGG16 architecture is a popular variant of CNN that has achieved high performance on many image classification tasks. To train a model for rice plant disease detection, a dataset of images of healthy and diseased rice plants must be collected and labeled. The dataset should have a sufficient number of examples of each type of disease to ensure that the model can accurately distinguish between them. Once the dataset is prepared, the next step is to train the CNN model using the VGG16 architecture. During training, the model learns to extract features from the images and use them to classify the plants as healthy or diseased. After training, the model can be tested on a separate set of images to evaluate its performance. The model's accuracy can be measured using metrics such as precision, recall, and F1 score. The Rice plant disease detection using CNN and VGG16 algorithms involves collecting and labeling a dataset of rice plant images, training a CNN model using the VGG16 architecture, and evaluating the model's performance on a separate test set. This research has the potential to help farmers detect and treat rice plant diseases early, leading to better crop yields and increased food security. It is an interesting approach to address the issue of rice plant disease detection using drone technology and machine learning algorithms. The proposed Deep Convolutional Neural Network (DCNN) transfer learning-based approach seems to be an effective method to accurately detect and classify six distinct classes of rice plant diseases. The use of IoT and drone technology can enable farmers to monitor their farmlands efficiently, reducing the need for costly manual inspections. The high accuracy of the proposed approach is promising and indicates its potential to be implemented in real-world applications. It is impressive that the proposed approach outperforms similar approaches reported in the literature, highlighting the effectiveness of the proposed modifications. Overall, the proposed approach has the potential to increase crop yield and contribute to food security by enabling early detection and treatment of rice plant diseases.

Machine learning; VGG-16; disease detection; convolutional networks; Plant Village; modern farming.