C
- Dotnet
- Machine Learning
- Python
Crop disease diagnosis in agriculture research is crucial. Distinguishing fine-grained crop diseases is essential, as treatment methods vary. We use Image Processing and deep learning to create a system for accurate crop disease identification. Our model, MDFC-ResNet, works across species, coarse-grained, and fine-grained diseases. It incorporates a compensation layer to fuse multidimensional recognition results, outperforming other deep learning models in practical agricultural use.
Module Description
Image Acquisition
Images of crops are captured using cameras or drones.
Preprocessing
The images are preprocessed to remove noise and enhance the features of interest.
Segmentation
The images are segmented into regions of interest, such as leaves, stems, and fruits.
Feature Extraction
Relevant features are extracted from the segmented regions, such as color, texture, and shape.
Classification
The extracted features are used to classify the regions as healthy or diseased using machine learning algorithms
- Agricultural Technology
- MDFC-ResNet Model
