In this comprehensive piece, I delve into the fascinating world of vegetation indices and demonstrate how I skillfully calculated them using the powerful programming language, Python. primarily i used multispectral bands for indices calculation then after that i applied ML in RGB images for classification and i also used DL models for the further process . Additionally, I share my expertise in crafting GIS course content that is sure to captivate learners. Furthermore, I take pride in detailing my proficiency in LiDAR data processing using Python alongside other cutting-edge processing software. I have had the opportunity to develop training content for a variety of purposes, including drone and GIS training courses that we provided to SIS. Additionally, I took responsibility for writing the TPM for DGCA remote pilot courses, which I highlighted in this presentation. By having a hand in both content development and course management, I ensure a comprehensive and well-rounded approach to training.
2. Content
Vegetation analysis
01 • Code for Vegetation Indices
Training Course ppt
02 • Course content for training Centre
GIS Course syllabus
03 • GIS Course Content PPT
SIS Course
04 • SIS training course content ppt
05 TPM writing • Training procedure manual for RPTO
Paras-Agri solution • PPT about Agri drone both hardware
and software solution
06
07 ML/DL • Vegetation health analysis by using
ML/DL model
08 Post creation • Content for the Agri’s social media
post
09 Lidar Data processing • Lidar data processing for vegetation
classification
4. Course Content PPT
Syllabus
Content
Script
Recording
PPT for drone pilot training
PPT’s Content
Wrote script for entire course
Script Writing
Video recording yet to be done
Video recording
Designed the syllabus according
to the DTC 02
DTC 02
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14. GIS Course Content
Syllabus
Design the Syllabus for the
selected topics with the
help of various references
Course Structure
Design the course
structure of GIS courses
for colleges
Content
Wrote the whole
content for given
syllabus
21. SIS Training Course
Designed the course structure
according to training schedule
Course Structure
Prepared the questionnaire for
the training
Q/A
Designed the syllabus according
to the course structure
Syllabus
Wrote the content of Course
according to the designed
syllabus
Course Content
26. Vegetation Health Analysis
DATA
Used PAS- multispectral data for
the processing
RESULT Compared the result with RGB
INDICES NDVI, NDRE, MSAVI2, NDWI, OSAVI
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28. Vegetation Indices
MSAVI2
NDWI
NDRE
NDVI
Read the data and get the information
about pixels and bands.
Reading data
Visualize the indices with help of colormap.
Visualization
Calculate the each indices with the
help of their respective formulas.
Calculation
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32. ML/DL
Use of ML
Tried to classify
vegetation with the
help of ML model,
Our model did the
classification for
different landcover.
Used the unsupervised classification
for the classification
Unsupervised Classification
Used the K-means model for
clustering
K-means Model
Identification of different landcover
using classification
Result
35. Lidar Data processing
Install the lidar packages like lidar, laspy. It took lots of
time because of compatibility issues
Lidar package
Read the data file of las format and get the
information about specifications and features.
Reading Data
It is in the process , we are using algorithm for the
segmentation.
Tree segmentation algorithm
Filtering the data, ground point removal and
normalization.
Data Pre-processing
We took small sections from the whole data for
our process.
Subset of data
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38. PAS-Agri Solution
Gave its features
details in brief like
spraying system, return
to home, geofencing,
autonomous flight
Features
PAS-Agri
specifications in
details
Specifications
Either it will be a camera
or tank, we have
different payloads as per
requirement
Payloads
Either for a vegetation
survey or for a chemical
spraying, what we will
provide as a service
Deliverables
Calculate vegetation
indices with the help of
PAS-multispectral
camera
Vegetation Indices
Introduced DL model for
vegetation analysis
Deep Learning Model
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41. Writing training script for
GIS course, work is in the
process
Wrote
training
script of the
entire
course for
drone pilots.
Preparing
Questionnaire
for GIS course