Published 1/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.92 GB | Duration: 2h 39m
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
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