Overview / Usage

Plant disease prediction using AI and ML uses artificial intelligence and machine learning techniques to predict plant diseases accurately. This approach utilizes various technologies, including image processing, data analysis, and predictive modeling, promptly and diagnosing plant diseases. The main goal of this technology is to assist farmers in identifying diseases early on, allowing them to take preventive measures and avoid yield losses.

The process of plant disease prediction involves collecting data from various sources, such as weather reports, soil samples, and images of plants. This data is then analyzed using AI and ML algorithms to detect patterns and identify potential diseases. The models To improve their accuracy, the models are trained on large datasets, including healthy and diseased plants fits of using AI and ML for plant disease prediction are numerous. These techniques can quickly and accurately detect diseases, reducing the need for manual inspection and diagnosis. By identifying diseases early, farmers can take necessary steps to prevent the spread of the disease and protect their crops. This technology can also help farmers reduce the use of pesticides and other chemicals, leading to healthier plants and a cleaner environment.

In conclusion, plant disease prediction using AI and ML is a promising approach to improving crop productivity and reducing losses. As these technologies continue to advance, we can expect to see more accurate and efficient disease detection methods to help farmers manage their crops better and improve food security.

Methodology / Approach

Plant Disease prediction using Pytorch.

Repository

https://github.com/apoorva240/plant_disease

Collaborators