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Bangroo Shabir's Projects

alues icon alues

Agricultural Land Use Evaluation System

aqp icon aqp

Algorithms for Quantitative Pedology

bigspatialdatar icon bigspatialdatar

Slides and lecture notes on how to process large spatial data with R

climater icon climater

An R 📦 for getting point and gridded climate data by AOI

ee_bionet icon ee_bionet

The Google Earth Engine implementation of the BioNet algorithm to estimate biophysical parameters along with their uncertainties.

facynation icon facynation

Forecasting Crop Yields on National scales (FACYNation)

geemap icon geemap

A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets

geocompr icon geocompr

Open source book: Geocomputation with R

jcolors icon jcolors

A set of color palettes I like (or can at least tolerate)

modistools icon modistools

Interface to the MODIS Land Products Subsets Web Services

opengeohub_2019 icon opengeohub_2019

Ressources for the session on machine learning and remote sensing at the OpenGeoHub Summer school in Münster 2019

opengeohub_2021 icon opengeohub_2021

Material for the tutorial on "Mapping the Area of Applicability of spatial prediction models" at the OpenGeoHub Summer School 2021

optimal_vcmax_r icon optimal_vcmax_r

calculate optimal vcmax in R as in Smith et al. Photosynthetic capacity is optimized to the environment. Ecology Letters.

performance icon performance

:muscle: Models' quality and performance metrics (R2, ICC, LOO, AIC, BF, ...)

plant-disease-detection icon plant-disease-detection

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.

python-geospatial icon python-geospatial

A collection of Python packages for geospatial analysis with binder-ready notebook examples

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