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Sugarcane Leaf Disease Classification Using Deep Learning | AI for Agriculture

Discover how deep learning and AI technology can help farmers detect and manage sugarcane leaf diseases early with high accuracy. This project uses convolutional neural networks and transfer learning to classify six major sugarcane diseases from leaf images.

In this video:

Overview of sugarcane leaf diseases affecting crop health

Dataset preparation and image preprocessing techniques

Building and training a ResNet-based CNN model for classification

Model evaluation: accuracy, confusion matrix, and prediction examples

Demo of the Streamlit web app allowing real-time disease diagnosis from leaf photos

Tips and remedies associated with each disease to help farmers take timely action

Why watch?
Gain insights into applying advanced AI models to real-world AgriTech problems. Whether you’re a machine learning enthusiast or an agricultural professional, learn how technology can transform farming practices.

Chapters:
0:00 Introduction
1:30 Dataset & Preprocessing
3:45 Model Architecture & Training
7:20 Evaluation & Results
10:15 Streamlit App Demo
12:30 Remedies and Recommendations
14:00 Conclusion

Useful links:

Code & Dataset: nirmalsankalana/sugarcane-leaf-disease-dataset

Streamlit App

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#SugarcaneDisease #DeepLearning #AgriTech #TensorFlow #MachineLearning #AIForGood