COTTON PLANT DISEASE DETECTION

Inspiration

The problem COTTON PLANT DISEASE DETECTION solves Plant diseases often cause serious loss of vegetables and crops.

The study of cotton plant disease involves observation of visual patterns on the leaves.

Detection of plant disease is an important part of cultivation as failure will affect the quantity and quality of product and human health.

Using a convolutional neural network, identify the disease-attacked area in the plant leaf.

To identify the disease and classify the disease in the plant leaf.

What it does

To identify the disease and classify the disease in the plant leaf.

How we built it

Technologies we used JavaScript Flask Keras NumPy Python Keras CNN HTML/CSS

Challenges we ran into

Challenges we ran into The project detects the disease which affects plants most especially, plant leaves. The challenges are:

1. Quantify and measure the severity of the disease on plant leaves.
2. Suggest the appropriate quantity and concentration of fungicides to use on plant leaves based on the disease severity.
3. To make the application more user-friendly. The validity and reliability of the results produced by the accuracy systems are a major concern for the users.

Accomplishments that we're proud of

The project presents leaf characteristics analysis using image processing techniques for automated vision systems used in the agricultural field.

What we learned

detects the disease which affects plants most especially, plant leaves.

What's next for Beneficial use // COTTON PLANT DISEASE DETECTION

Built With

• cnn
• css
• flask
• html
• keras
• numpy
• python