Crop Yield Estimation Using Remote Sensing And Gis Arcgis

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

What you'll 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

Requirements

You must know basics of ArcGIS

You must know your study area well

You must know the crop growth stages

You must know the basics of excel

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 :Use Machine learning method for crop classification in ArcGIS, separate crops from natural vegetation The model was developed using the minimum observed data available onlineCrop NDVI separationCrop Yield model developmentCrop production calculation from GIS model dataIdentify the low and high-yield zones and area calculationCalculate the total production of the regionValidation of developed model on another study area Validate production and yield of other areas using a developed model of another areaConvert the model to the ArcGIS toolboxYou must know:Basics of GISBasics of ExcelSoftware Requirements: Any version of ArcGIS 10.0 to 10.8Excel

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Do and do not

Lecture 3 Know your crop stage

Lecture 4 Software used

Section 2: Concept and Methodology

Lecture 5 Methodology concept

Lecture 6 Explore your study area

Section 3: Data selection and download

Lecture 7 Download crop data

Lecture 8 Download best satellite image for crop

Lecture 9 Procession of satellite image

Lecture 10 Separate required shapefile

Lecture 11 Cut study area

Lecture 12 Important understanding area and correcting image

Section 4: Crop classification

Lecture 13 Crop sampling

Lecture 14 Crop classification using ML tool in ArcGIS

Lecture 15 NDVI

Lecture 16 Crop area verification

Section 5: Model development and crop separation

Lecture 17 Separate crop NDVI

Lecture 18 Regression equation development

Lecture 19 Model development and yield calculation

Section 6: Post model yield calculation

Lecture 20 Calculate total area crop production

Lecture 21 Yield class specific area calculation

Section 7: Validation of Developed model on another area

Lecture 22 Validation Intro

Lecture 23 Cut new area

Lecture 24 NDVI of validation area

Lecture 25 Crop NDVI and running the model

Lecture 26 calculate accuracy of validation and yield estimates

Section 8: Survey discussion

Lecture 27 Survey discussion

Section 9: Congratulation and Next

Lecture 28 What is next

Lecture 29 Bonus Lecture

Agriculture engineers,Civil engineers,Crop analysist,Agency working for crop insurance,Govt sector agriculture scientists,Water resource engineers,Irrigation engineers,Master students of GIS,PhD students of IIT NIT or University