Authors :
Akshara Avinash Sarode; Sufyain Salim Posharkar; Dr. Aparna Bannore; Sanskar Sandeep Unkule
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/bdf39bbm
DOI :
https://doi.org/10.5281/zenodo.8149167
Abstract :
Sugarcane is an important cash crop
worldwide, and disease outbreaks can cause significant
economic losses. Early detection and timely management
of sugarcane diseases are crucial for maintaining crop
yield and quality. In this project, we propose a deep
learning-based approach for sugarcane disease
prediction using transfer learning and Adam optimizer.
We utilized pre-trained models, including AlexNet,
VGG16, VGG19, MobileNet, DenseNet, ResNet50, and
EfficientNet, to train our model on sugarcane disease
images, including Red Rust, Red Rot, Bacterial Blight,
and Healthy Crops. The proposed model achieved high
accuracy and performance, with ResNet101
outperforming other models. The project demonstrates
the effectiveness of transfer learning in improving the
classification of sugarcane diseases. The developed
system can assist farmers in early detection and timely
treatment of sugarcane diseases, thus increasing crop
yield and reducing losses.
Keywords :
Deep Learning, CNN, Transfer Learning, Alexnet, VGG 16, Adam’s Optimizer, epoch.
Sugarcane is an important cash crop
worldwide, and disease outbreaks can cause significant
economic losses. Early detection and timely management
of sugarcane diseases are crucial for maintaining crop
yield and quality. In this project, we propose a deep
learning-based approach for sugarcane disease
prediction using transfer learning and Adam optimizer.
We utilized pre-trained models, including AlexNet,
VGG16, VGG19, MobileNet, DenseNet, ResNet50, and
EfficientNet, to train our model on sugarcane disease
images, including Red Rust, Red Rot, Bacterial Blight,
and Healthy Crops. The proposed model achieved high
accuracy and performance, with ResNet101
outperforming other models. The project demonstrates
the effectiveness of transfer learning in improving the
classification of sugarcane diseases. The developed
system can assist farmers in early detection and timely
treatment of sugarcane diseases, thus increasing crop
yield and reducing losses.
Keywords :
Deep Learning, CNN, Transfer Learning, Alexnet, VGG 16, Adam’s Optimizer, epoch.
