Soyabean Leaf Detection Using Inception ResNet V2
"An AI-based system for detecting soybean leaf diseases using deep learning techniques including CLAHE preprocessing, attention mechanisms, and InceptionResNetV2."
Problem Statement
Soybean crops are affected by multiple leaf diseases which reduce productivity. Manual detection is time-consuming and not always accurate.
Literature Review / Market Research
Existing systems rely on traditional methods which are less accurate and not robust.
Research Gap / Innovation
There is a need for an automated and efficient system that can detect diseases using deep learning techniques.
System Methodology
Dataset / Input
Zenodo soybean dataset with 8 classes and thousands of labeled images.
Model / Architecture
InceptionResNetV2 with transfer learning and SE (attention) module.
Live Execution
VIEW CODE / DEMOResults & Analysis
The model achieved an accuracy of approximately 77%, demonstrating its capability to classify soybean leaf diseases effectively.
Academic Credits
Project Guide
Dr Tapan Kumar Dey
Team Members
Yogesh Kumar
Hemant Mahala
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