hrshwrdhn

HARSH WARDHAN SINGH's repositories

threedim_shepp_logan

this matlab function used to generate a three dimension Shepp Logan Phantom, data is based on A. C. Kak and Malcolm Slaney, Principles of Computerized Tomographic Imaging,

Language:MATLAB100

twodim_shepplogan

Filtered Back Projection | TWO DIMENSIONAL TOMOGRAPHY : straight path

Language:MATLAB100

Automatic-leaf-infection-identifier

Automatic detection of plant diseases

Language:PythonMIT000

blast-ct

Brain Lesion Analysis and Segmentation Tool for Computed Tomography

Language:Jupyter NotebookApache-2.0000

blob_art_3d

A Blob based Algebraic reconstruction Technique for 3-D computerized tomography

Language:MATLABMIT000

DeepLung

WACV18 paper "DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification"

Language:Jupyter NotebookApache-2.0000

DeepWeeds

A Multiclass Weed Species Image Dataset for Deep Learning

Language:C++Apache-2.0000

imgaug

Image augmentation for machine learning experiments.

Language:PythonMIT000

luna16_multi_size_3dcnn

An implement of paper "Multi-level Contextual 3D CNNs for False Positive Reduction in Pulmonary Nodule Detection"

Language:Python000

NAS-Lung

3D NAS for Pulmonary Nodules Classification

MIT000

Plant Disease Detection is one of the mind-boggling issues when we talk about using Technology in Agriculture. Although researches have been done to detect whether a plant is healthy or diseased using Deep Learning and with the help of Neural Network, new techniques are still being discovered. For Fewer Data Classical Machine Learning Models are said to outstand given the data is pre-processed well. On the same theory here is my approach for Detecting whether a plant leaf is healthy or unhealthy by utilizing the classical Machine Learning Models, Pre-processing the Image Data. The data was fed to 7 Machine Learning Models with 10 fold cross-validation out of which Random Forest Classifier outperformed all the other models giving an accuracy of 97% on the test set.

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