CROP DISEASE PREDECTOR: DEVELOP AN APPLICATION THAT PREDICTS CROP DESASES BASED ON SYMPTOMS SEEN IN CROP
DOI:
https://doi.org/10.62643/Keywords:
Plant leaf disease, Convolutional neural network (CNN), Semantic segmentation, Encoder-decoder, Detection, Classification, Dara Sciecne, Artificial Intelligence (AI)Abstract
Plants are the major source of food for all kinds of living beings. With the increase in population, it is now more important to keep this supply continue. To cop-up with such high demand, it is very important keep the plants healthy from various kinds of diseases. The detection of disease is sometimes very difficult for even experienced farmers. Latest technologies like Deep Learning and Image Processing have made it significantly easy to detect and cure such plant diseases earlier to reduce loss. In this project, we proposed a system that is capable of detecting disease in leaf. We will be using back and forward propagation to train our neural network. Data set of resolution 250*250 images are being used in this project. Our goal is to find a suitable and efficient model that can predict the disease in the plant. For this project, we’ll be using creating different models using Keras to develop different models and train them with the data set for various types of leaves taken from different plants. Data collected from various models then will be analyzed and an efficient model will be suggested.
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