Plant Disease Detection from Images
This work addresses the problem of reducing reliance on professional help for plant disease detection, making it more accessible, but it is incremental as it applies existing methods to a specific domain.
The research tackled plant disease detection from leaf images by developing a deep learning model using transfer learning with ResNet architectures, achieving state-of-the-art results on the dataset used.
Plant disease detection is a huge problem and often require professional help to detect the disease. This research focuses on creating a deep learning model that detects the type of disease that affected the plant from the images of the leaves of the plants. The deep learning is done with the help of Convolutional Neural Network by performing transfer learning. The model is created using transfer learning and is experimented with both resnet 34 and resnet 50 to demonstrate that discriminative learning gives better results. This method achieved state of art results for the dataset used. The main goal is to lower the professional help to detect the plant diseases and make this model accessible to as many people as possible.