Plant Disease Detector
Plant disease detection using image processing and machine learning is a very useful application in agriculture I used CNN and created a neural network with training accuracy 95% and testing accuracy.
Created on 16th March 2023
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Plant Disease Detector
Plant disease detection using image processing and machine learning is a very useful application in agriculture I used CNN and created a neural network with training accuracy 95% and testing accuracy.
The problem Plant Disease Detector solves
By detecting diseases in plants at an early stage, farmers can take immediate action to control the spread of the disease and prevent crop loss. Here are some ways in which plant disease detection can be useful:
Early detection With plant disease detection, diseases can be detected at an early stage before they become severe. This allows farmers to take preventive measures before the disease spreads to other parts of the plant or to other plants.
Improved crop yield: By detecting diseases early and taking necessary steps to control them, farmers can improve crop yield and quality.
Reduced use of pesticides: With plant disease detection, farmers can reduce the use of pesticides by targeting the use only to infected areas. This leads to a reduction in the use of harmful chemicals and saves costs.
Cost-effective: Plant disease detection is a cost-effective solution compared to traditional methods of disease detection. It does not require the use of expensive equipment and can be implemented using simple and affordable devices such as smartphones.
Challenges I ran into
Model accuracy is so much challenging. I increased accuracy from 79 to 94 % without using any pretrained model. It takes lots of time in neural networks to process 5 epoches. Connecting flask with javascript and html as backend is also not an eassy work.
Tracks Applied (1)
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Technologies used