In recent times, we have been witnessing the emergence of new viruses and diseases that are causing significant damage to both human health and agricultural productivity. The impact of these diseases is especially felt in the farming community, where plant diseases can devastate entire crops and cause significant financial losses. Currently, farmers face the challenge of identifying and treating these diseases, which can be a time-consuming and costly process. Therefore, there is a crucial need to develop a solution that can simplify and reduce the cost of disease detection and treatment in agriculture.
Introducing SwasthKrishi - an innovative solution that simplifies the process of identifying and treating plant diseases, while reducing the effort and cost for farmers. With SwasthKrishi, farmers can simply upload an image of an affected plant's leaf to our website, and our plant disease prediction model will accurately identify the disease and recommend the most suitable treatment methods.
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Our team has developed a website using HTML, CSS, ReactJS, and FastAPI, which includes our advanced plant disease prediction model. Using the website, users can easily upload an image of an affected leaf, which is then sent to our model for analysis. The model accurately predicts the disease and displays the appropriate solution for the user.
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At present, our model can predict diseases in popular plants such as pepper, potato, and tomato. However, we plan to expand its capabilities to predict diseases in many more plants in the near future.
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By providing users with accurate disease predictions and customized treatment methods, our website helps farmers protect their crops and optimize their yields. With the latest technology and a user-friendly interface, our platform empowers farmers with the knowledge and tools they need to improve their crop health and increase their profits.
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Clone repo
git clone https://github.com/ritwik3856/SwasthKrishi.git cd SwasthKrishi -
Install dependent packages
pip install -r requirements.txt
npm run start
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