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via Udemy |
Go to Course: https://www.udemy.com/course/crop-yield-estimation-using-remote-sensing-and-gis-arcgis/
Certainly! Here's a comprehensive review and recommendation for the "Crop Yield Estimation using Remote Sensing and GIS ArcGIS" course on Coursera: --- **Course Review: Crop Yield Estimation using Remote Sensing and GIS ArcGIS** The "Crop Yield Estimation using Remote Sensing and GIS ArcGIS" course is an invaluable resource for anyone interested in modern agriculture, remote sensing, and geographic information systems (GIS). This course provides an in-depth overview of how advanced geospatial tools can be leveraged to estimate crop yields, with a focus on wheat but applicable to other crops as well. **Overview and Content:** This course expertly combines theory and practical applications, illustrating how remote sensing data—particularly spectral indices like NDVI—can be used to assess crop health and classify different vegetation types via machine learning techniques within ArcGIS. One of the key strengths of this course is its hands-on approach, guiding learners through the development of crop yield models using classified remote sensing data, regression analysis, and GIS-based modeling. Participants will learn how to: - Use remote sensing to identify and separate crop types from natural vegetation - Develop and validate crop yield estimation models - Calculate regional crop production and identify high- and low-yield zones - Convert models into user-friendly ArcGIS toolboxes for repeated use **Strengths:** - Practical focus on machine learning techniques in ArcGIS for crop classification - Step-by-step guidance on model development, validation, and application - Use of publicly available data for model training, making it accessible - Emphasis on model validation across different study areas enhances robustness - Clear instructions suitable for those with basic GIS and Excel skills **Who Should Enroll?** This course is particularly valuable for agricultural scientists, remote sensing specialists, GIS professionals, and students interested in precision agriculture. A basic understanding of GIS and Excel will help learners maximize the benefits of this training. **Recommendations:** I highly recommend this course for individuals interested in integrating geospatial technologies into agricultural management. The skills gained will enable you to develop reliable crop yield estimation models, improve resource allocation, and contribute to sustainable agriculture practices. **Final Verdict:** A well-structured, practical, and highly applicable course that bridges the gap between remote sensing technology and crop yield management. Its focus on wheat provides a solid foundation for applying similar methodologies to other crops, making it a versatile addition to any agricultural or geospatial toolkit. --- Feel free to ask if you'd like a shorter summary or assistance with specific aspects of the course!
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