Crop Yield Estimation using Remote Sensing and GIS ArcGIS

Crop Yield Modelling, Crop identification, Crop type classification, Estimating wheat yield, NDVI, Agricultural GIS

4.13 (79 reviews)
Udemy
platform
English
language
Other
category
372
students
2.5 hours
content
Jan 2023
last update
$49.99
regular price

What you will learn

Crop yield modelling using remote sensing and GIS - ArcGIS

Crop classification using ArcGIS

Crop production estimation before harvest using GIS

Application of GIS for Agriculture analysis

Crop mapping using ArcGIS

Crop yield model development using GIS

Agricultural GIS

Regression equation based modelling in GIS

Validation of developed model

Application of NDVI for crop health analysis

Identify lower and higher yield areas

Crop health estimation using GIS

Description

Crop yield estimation is a critical aspect of modern agriculture. In this course, the wheat crop is covered. The same method applies to all other crops. With the advent of remote sensing and GIS technologies, it has become possible to estimate crop yields using various methodologies. Remote sensing is a powerful tool that can be used to identify and classify different crops, assess crop conditions, and estimate crop yields. One of the most popular methods for crop identification using remote sensing is to relate crop NDVI as a function of yield. This method uses various spectral, textural and structural characteristics of crops to classify them using the machine learning method in ArcGIS. Another popular method for crop condition assessment using remote sensing is crop classification then relate to NDVI index. This method uses indices such as NDVI to assess the health of the crop. Both of these methods are widely used for crop identification and assessment. Crop yield estimation can also be done by using remote sensing data. Yield estimation using remote sensing is done by using statistical methods, such as regression analysis and modelling in GIS and excel, including classification and estimation. One popular method for estimating wheat yield is the crop yield estimation model using classified and modelled data with observed records, as shown in this course. This model uses various remote sensing data to estimate the wheat yield. It is also important to validate the developed model on another nearby study area. That validation of the developed model is also covered in this course. The identification of crops is an important step in estimating crop yields and managing agricultural resources. In summary, remote sensing and GIS technologies are widely used for crop identification, crop condition assessment, and crop yield estimation. They provide accurate and timely information that is critical for managing agricultural resources and increasing crop yields.

Highlights :

  1. Use Machine learning method for crop classification in ArcGIS, separate crops from natural vegetation

  2. The model was developed using the minimum observed data available online

  3. Crop NDVI separation

  4. Crop Yield model development

  5. Crop production calculation from GIS model data

  6. Identify the low and high-yield zones and area calculation

  7. Calculate the total production of the region

  8. Validation of developed model on another study area

  9. Validate production and yield of other areas using a developed model of another area

  10. Convert the model to the ArcGIS toolbox

You must know:

  1. Basics of GIS

  2. Basics of Excel

Software Requirements:

  1. Any version of ArcGIS 10.0 to 10.8

  2. Excel



Content

Introduction

Introduction
Do and do not
Know your crop stage
Software used

Concept and Methodology

Methodology concept
Explore your study area

Data selection and download

Download crop data
Download best satellite image for crop
Procession of satellite image
Separate required shapefile
Cut study area
Important understanding area and correcting image

Crop classification

Crop sampling
Crop classification using ML tool in ArcGIS
NDVI
Crop area verification

Model development and crop separation

Separate crop NDVI
Regression equation development
Model development and yield calculation

Post model yield calculation

Calculate total area crop production
Yield class specific area calculation

Validation of Developed model on another area

Validation Intro
Cut new area
NDVI of validation area
Crop NDVI and running the model
calculate accuracy of validation and yield estimates

Survey discussion

Survey discussion

Congratulation and Next

What is next
Bonus Lecture

Screenshots

Crop Yield Estimation using Remote Sensing and GIS ArcGIS - Screenshot_01Crop Yield Estimation using Remote Sensing and GIS ArcGIS - Screenshot_02Crop Yield Estimation using Remote Sensing and GIS ArcGIS - Screenshot_03Crop Yield Estimation using Remote Sensing and GIS ArcGIS - Screenshot_04

Reviews

Sankalp
August 18, 2023
A very goof course to understand how thing work in this domain. Got a lot of understanding from this course.
Nikhil
February 4, 2023
This course was very well structured, and I learned a lot about validation of crop yield, something I had not had experience with before. To match our intense work, it is also great to know the Government is maintaining these records. I classified the initial study area three times, as it does take a lot of effort, particularly differentiating crops and natural vegetation. Thank you !
Adarsh
February 2, 2023
Throughout the course, all steps are explained in detail. The course will serve as a good starting point for anyone looking to conduct crop yield estimations using remote sensing.
HARPINDER
January 29, 2023
An Excellent course for crop yield estimation from satellite images. Thanks to the Instructor for sharing the knowledge.
Jasper
January 29, 2023
This really helping me for my project. Thanks lakhwinder sir for such a nice course on Agriculture remote sensing.

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5055216
udemy ID
1/2/2023
course created date
1/29/2023
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