“Medicinal plants play a vital role in traditional medicine and have been used for centuries to treat a variety of ailments. Early detection of diseases in medicinal plants is crucial to protect the crop yield and maintain the consistency of the supply of medicinal plants. However, it can be difficult to detect diseases in their early stages, especially in large-scale farming operations. Early disease detection in medicinal plants can help farmers take action to prevent the spread of disease and protect their crop yield. This can also help to ensure a consistent supply of high-quality medicinal plants for medicinal and other uses. Early disease detection in medicinal plants can have a positive impact on the agricultural industry, as well as on the communities that rely on medicinal plants for traditional medicine and other purposes. It can also help to reduce the use of pesticides, which can have negative impacts on the environment and human health. So we have come up with the solution of creating an app that can detect diseases early in these plants. One approach to early disease detection in medicinal plants is to use convolutional neural networks (CNNs) and transfer learning. CNNs are a type of deep-learning algorithm that can be trained to recognize patterns in images. This approach has been found to be highly effective, with an accuracy rate of 92%. Using this technology can help to improve the accuracy of disease detection. Farmers can now detect disease in their medicinal plants with 92% accuracy. A consistent supply of medicines and the protection of medicinal plants depend on early disease detection. Additionally, this technology reduces the use of pesticides, which can be harmful to the environment and humans. “
