click here to download project abstract
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ABSTRACT
Powdery Mildew is a fungal disease that grows on leaves, branches in both field
and greenhouse production of many crop plants, and its infection can reduce the
yield and cause loss. Powdery Mildew is caused due to change in climate
conditions with high humidity and moderate temperature. In this project we will
create a machine learning model which will predict the chances of getting Powdery
Mildew disease is in low, high or medium state based on climate conditions like
temparature, humidity, soil moisture and cloud factor on Sandal Wood trees. In
this project we are predicting the Powdery mildew disease data by using some
machine learning algorithms such as Logistic regression, Decision Tree, Random
Forest, Support Vector Machine and K-Nearest Neighbours. The model with
highest accuracy is taken for web app building as it gives better predictions when
compared with other models and it will suggest the precautions to be taken to
avoid powdery mildew disease. Finally, the web app is deployed in Heroku
platform to run the application on cloud.
