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Uploading Images
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Predicting Results
Inspiration
Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recommended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farmers will easily find the diseases based on the early symptoms.
What it does
By uploading images of various Plant Leaves, it detects its type of disease with the help of trained Machine Learning model.
How we built it
Created a Machine Model for training dataset of different diseases of plant. Trained Machine Learning is store in .h5 model. Designed UI for our project using html/css. Using Postman we tested the API request GET and POST for prediction. Using Python, API request were handled using flask. Entire process was tested.
Challenges we ran into
Collecting dataset Images of Plant Leaves with various disease. Handling errors in python
Accomplishments that we're proud of
Build a successful Machine Learning Model. Integrated ML model with frontend successfully
What we learned
About API fetching request. Building ML models.
What's next for Plant Disease Detection
Integrate with Live Camera using OpenCV Improving Accuracy of the Model.
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