Abstract:
Rice plant disease detection and monitoring is a critical issue. An accurate and timely
detection of diseases in rice plants can help farmers in applying timely treatment on the
plants and thereby can reduce the economic losses substantially. So, we want to develop a
system on python which can detect the disease of rice plant. We studied various techniques
and algorithms likes Linear Regression, Logistic Regression, CNN architectures like
Resnet, AlexNet, LeeNet, VggNet and KNN regarding this issue. After many discussion
and comparison between these machine learning algorithms we chose CNN architecture.
We use sequential model based on CNN architecture because this architecture performs
best for image classification and detection compared to other architecture or algorithm.
We use a large amount of dataset for training our model and for detection. The results show
that our proposed method successfully classify and find out the rice leaf diseases based on
image processing techniques. Our experimental results show that we achieve 97% test
accuracy with our proposed model, while other models have less than 80% accuracy