Crop Yield Modelling, Crop identification, Crop type classification, Estimating wheat yield, NDVI, Agricultural GIS
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.
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.
Course Content
- Introduction –> 4 lectures • 5min.
- Concept and Methodology –> 2 lectures • 5min.
- Data selection and download –> 6 lectures • 42min.
- Crop classification –> 4 lectures • 38min.
- Model development and crop separation –> 3 lectures • 21min.
- Post model yield calculation –> 2 lectures • 19min.
- Validation of Developed model on another area –> 5 lectures • 22min.
- Survey discussion –> 1 lecture • 4min.
- Congratulation and Next –> 2 lectures • 1min.
Requirements
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 online
- Crop NDVI separation
- Crop Yield model development
- Crop production calculation from GIS model data
- Identify the low and high-yield zones and area calculation
- Calculate the total production of the region
- Validation of developed model on another study area
- Validate production and yield of other areas using a developed model of another area
- Convert the model to the ArcGIS toolbox
You must know:
- Basics of GIS
- Basics of Excel
Software Requirements:
- Any version of ArcGIS 10.0 to 10.8
- Excel