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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.

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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

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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Skills and knowledge taught are highly relevant to the agricultural industry
Provides a detailed model for crop yield estimation using remote sensing and GIS
Builds a strong foundation for beginners in crop yield estimation
Supplemental material available online
May require prior knowledge of GIS and Excel
Focuses on wheat crops, but the same method can be applied to other crops

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Reviews summary

Well-received crop yield course

Learners largely enjoy the understandable and easy-to-follow content of the well-structured "Crop Yield Estimation using Remote Sensing and GIS ArcGIS" course. Students especially appreciate the numerous examples and Sal’s engaging teaching style. They say her segmentation of information is helpful, her expertise shows, and her use of her own experiences to illustrate makes the material relatable.
The use of personal examples makes the content relatable.
"with the sharing her own personal life experiences as examples."
The segmentation of information is effective.
"I like the segmented nature of Sal’s approach."
"bite size nuggets of information."
The course includes numerous and helpful examples.
"easy to understand and lots of examples"
"I've found the topics easy to remember so far"
The course is well-structured and engaging.
"Easy to understand for beginners."
"I like the way the course is set up."
"I love this long course, less things to read and many lectures as videos."
"Sal's background as a teacher really shows in the way she structures her courses and in the way she produces the content."
The instructor is engaging and knowledgeable.
"very interesting, easy to follow, easy to understand."
"She makes the material engaging and easy to understand."
"She really knows her stuff and is a great resource for mastering the tarot!"
The course content is understandable and easy to follow.
"good easy to understand"
"easy to understand and lots of examples"
"It was great course for beginners. simple to follow"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Crop Yield Estimation using Remote Sensing and GIS ArcGIS with these activities:
Organize and consolidate course materials
Stay organized and enhance your learning by compiling and reviewing course materials.
Show steps
  • Create a central repository for all course materials.
  • Regularly review and summarize key concepts from the materials.
Brush up on GIS and Excel basics
Refresh your GIS and Excel skills to excel in this course.
Browse courses on GIS
Show steps
  • Review online tutorials to understand GIS fundamentals.
  • Check out documentation and tutorials to grasp Excel concepts.
Reach out to experts in the field of crop yield estimation
Connect with experts to gain valuable insights and guidance on crop yield estimation.
Show steps
  • Attend industry events or conferences.
  • Network with professionals on LinkedIn or other platforms.
  • Reach out to professors or researchers in the field.
Two other activities
Expand to see all activities and additional details
Show all five activities
Organize a study group to discuss crop yield estimation
Deepen your understanding of crop yield estimation through discussions with peers.
Show steps
  • Find a group of classmates who are interested in forming a study group.
  • Establish regular meeting times and stick to them.
  • Prepare for each meeting by reading the assigned materials and completing any pre-reading.
Contribute to open-source projects related to crop yield estimation
Enhance your skills and contribute to the community by participating in open-source projects on crop yield estimation.
Show steps
  • Identify open-source projects on platforms like GitHub.
  • Review the project documentation and identify areas where you can contribute.
  • Reach out to the project maintainers to express your interest in contributing.

Career center

Learners who complete Crop Yield Estimation using Remote Sensing and GIS ArcGIS will develop knowledge and skills that may be useful to these careers:
Crop Consultant
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, is highly relevant to Crop Consultants who use remote sensing and GIS technologies to provide crop management advice to farmers. The course provides a comprehensive overview of the use of remote sensing and GIS technologies to identify and classify different crops, assess crop conditions, and estimate crop yields. This knowledge is essential for Crop Consultants, as it allows them to develop and implement crop management plans that can improve crop yields and reduce environmental impacts.
Farmer
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, is highly relevant to Farmers who use remote sensing and GIS technologies to manage their crops. The course provides a comprehensive overview of the use of remote sensing and GIS technologies to identify and classify different crops, assess crop conditions, and estimate crop yields. This knowledge is essential for Farmers, as it allows them to develop and implement crop management plans that can improve crop yields and reduce environmental impacts.
Crop Insurance Agent
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, is highly relevant to Crop Insurance Agents who use remote sensing and GIS technologies to assess crop conditions and estimate crop yields. The course provides a comprehensive overview of the use of remote sensing and GIS technologies to identify and classify different crops, assess crop conditions, and estimate crop yields. This knowledge is essential for Crop Insurance Agents, as it allows them to develop and implement crop insurance policies that can protect farmers from financial losses due to crop damage or failure.
Precision Agriculture Specialist
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, is highly relevant to Precision Agriculture Specialists who use remote sensing and GIS technologies to improve agricultural productivity. The course provides a comprehensive overview of the use of remote sensing and GIS technologies to identify and classify crops, assess crop conditions, and estimate crop yields. This knowledge is essential for Precision Agriculture Specialists, as it allows them to develop and implement precision agriculture practices that can improve crop yields and reduce environmental impacts.
GIS Analyst
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, is highly relevant to GIS Analysts who work in the field of agriculture. The course provides a comprehensive overview of the use of remote sensing and GIS technologies to identify and classify crops, assess crop conditions, and estimate crop yields. This knowledge is essential for GIS Analysts who work in the agricultural sector, as it allows them to develop and implement GIS-based solutions to support agricultural decision-making.
Remote Sensing Scientist
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, provides a solid foundation for Remote Sensing Scientists who specialize in agricultural applications. The course covers the use of remote sensing and GIS technologies to identify and classify different crops, assess crop conditions, and estimate crop yields. This knowledge is essential for Remote Sensing Scientists who work in the agricultural sector, as it allows them to develop and implement remote sensing-based solutions to support agricultural decision-making.
Research Scientist
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, is highly relevant to Research Scientists who work in the field of agriculture. The course provides a comprehensive overview of the use of remote sensing and GIS technologies to identify and classify crops, assess crop conditions, and estimate crop yields. This knowledge is essential for Research Scientists who work in the agricultural sector, as it allows them to conduct research on crop production and management practices.
Soil Scientist
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, provides a foundation for Soil Scientists who want to learn about the use of remote sensing and GIS technologies to assess soil conditions and their impact on crop yields. The course covers the use of remote sensing data and GIS software to identify and classify different soil types, assess soil conditions, and estimate crop yields. This knowledge is essential for Soil Scientists who work in the agricultural sector, as it allows them to develop and implement soil management practices that can improve crop yields and reduce environmental impacts.
Geospatial Analyst
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, provides a strong foundation for Geospatial Analysts who want to specialize in agricultural applications. The course covers the use of remote sensing and GIS technologies to identify and classify different crops, assess crop conditions, and estimate crop yields. This knowledge is essential for Geospatial Analysts who work in the agricultural sector, as it allows them to provide valuable information to farmers and other stakeholders.
Water Resources Engineer
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, may be useful for Water Resources Engineers who want to learn about the use of remote sensing and GIS technologies to assess water resources and their impact on crop yields. The course covers the use of remote sensing data and GIS software to identify and classify different water resources, assess water quality, and estimate crop yields. This knowledge may be useful for Water Resources Engineers who work in the agricultural sector, as it allows them to develop and implement water management practices that can improve crop yields and reduce environmental impacts.
Farm Manager
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, helps build a foundation for Farm Managers who want to gain knowledge about the use of remote sensing and GIS technologies to improve their farming practices. The course covers the use of remote sensing data and GIS software to identify and classify different crops, assess crop conditions, and estimate crop yields. This information can be used to make informed decisions about crop management, such as when to plant, fertilize, and harvest.
Policy Analyst
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, may be useful for Policy Analysts who want to learn about the use of remote sensing and GIS technologies to assess the impact of agricultural policies. The course covers the use of remote sensing and GIS technologies to identify and classify different crops, assess crop conditions, and estimate crop yields. This knowledge may be useful for Policy Analysts who work in the agricultural sector, as it allows them to develop and implement policies that can improve crop yields and farm income.
Environmental Scientist
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, provides a good foundation for Environmental Scientists who wish to learn about the use of remote sensing and GIS technologies to assess the condition of crops and land use. The course provides an understanding of the principles of remote sensing and GIS, and how these technologies can be used to identify and classify different crops, assess crop conditions, and estimate crop yields. This knowledge can be useful for Environmental Scientists who work in areas such as agricultural research, land use planning, and environmental impact assessment.
Agricultural Economist
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, may be useful for Agricultural Economists who want to learn about the use of remote sensing and GIS technologies to assess the economic impact of agricultural practices. The course covers the use of remote sensing and GIS technologies to identify and classify different crops, assess crop conditions, and estimate crop yields. This knowledge may be useful for Agricultural Economists who work in the agricultural sector, as it allows them to develop and implement economic models that can assess the impact of different agricultural practices on crop yields and farm income.
Agronomist
The course, Crop Yield Estimation using Remote Sensing and GIS ArcGIS, may be useful for Agronomists who wish to improve their knowledge of crop yield estimation techniques and learn how to use remote sensing and GIS technologies to support their work. The course covers the use of remote sensing data as well as GIS software to identify and classify different crops, assess crop conditions, and estimate crop yields.

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Crop Yield Estimation using Remote Sensing and GIS ArcGIS.
Provides a comprehensive overview of digital image processing techniques and applications in remote sensing. It includes topics such as image acquisition, preprocessing, classification, and interpretation.
Provides a comprehensive overview of remote sensing and image interpretation. It includes topics such as the history of remote sensing, the physics of remote sensing, and the applications of remote sensing.
Provides a comprehensive overview of the principles of remote sensing. It includes topics such as the electromagnetic spectrum, the atmosphere, and the interpretation of remote sensing data.
Provides a comprehensive overview of digital image processing techniques. It includes topics such as image enhancement, image restoration, and image compression.
Provides a comprehensive overview of GIS. It includes topics such as the history of GIS, the principles of GIS, and the applications of GIS.
Provides a comprehensive overview of statistical methods used in geography. It includes topics such as descriptive statistics, inferential statistics, and spatial statistics.
Provides a comprehensive overview of remote sensing of the environment. It includes topics such as the history of remote sensing, the physics of remote sensing, and the applications of remote sensing.
Provides a comprehensive overview of crop management. It includes topics such as the history of crop management, the principles of crop management, and the applications of crop management.

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