Sugarcane-Leaf-Disease-Detection

This project allows the detection of abnormalities that maybe present on the leaves of the Sugarcane plant and classify/recognize the disease accordingly.

Dataset:

Link to Dataset: https://www.kaggle.com/datasets/roshitab/sugarcane-leaf-disease-dataset

• Created by visiting a local sugarcane farm and collecting on-ground images.
• It consists of 224 images of healthy and diseased crop leaves
• The dataset has 3 classes - Healthy, Red Rot, Red Rust.
  • Healthy: 75 Images
  • Red Rot: 74 Images
  • Red Rust: 75 Images

Methodology:

Transfer Learning approach was implemented with the help of DenseNet201 architecture. To improve the accuracy of the model, Support Vector Machine (SVM) was incorporated in the final layer of the model. An Accuracy of 98% and a Validation Accuracy of 97.78% was obtained.

Results: