What are the applications of crop model in agriculture and applications of remote sensing in crop health monitoring and land use mapping?
Crop models can be utilized in yield prediction as well as resource allocation( fertilizers, pesticides etc.) in specific locations of land where crops are not yielding to the maximum. When it comes to applications of RS in crop health monitoring, using high-resolution imageries (UAV imagery) you can easily come up with a model to detect the anomalies caused by diseases through utilization of machine learning algorithms. In the case of land use mapping I, RS can be utilized to identify the lands under agricultural use and it can further be advanced to identify the types of crops grown in a particular parcel of land after doing binary classification (agricultural and Non-agricultural land)
Crop models are a formal way to present quantitative knowledge about how a crop grows in interaction with its environment. Using weather data and other data about the crop environment, these models can simulate crop development, growth, yield, water, and nutrient uptake. Crop growth model is a very effective tool for predicting possible impacts of climatic change on crop growth and yield. The tests were made to reflect the model response when used to predict yield under changing climate condition and different field parameters than those encountered during model formulation.Crop model simulations are subject to considerable uncertainties with respect to model implementations and process representation, and thus vary significantly at field and global scale. On a global scale, detailed data are often not available on basic management options, such as sowing dates and variety selection. Crop weather analysis model : These models are based on the product of two or more factors each representing the functional relationship between a particular plant response i.e., crop yield and the variations in selected weather variables at different crop development stages. Remote sensing can be used to monitor the health and growth of crops by analyzing spectral data obtained from satellites, airborne sensors, or ground-based instruments. This information can help farmers identify areas of their fields that may need additional attention or water, fertilizer, or pest management. Remote sensing provides multi-spectral, and multi temporal satellite images for accurate mapping. Land cover/Land use mapping provide basic inventory of land resources. This mapping can be local or regional in scope; it depends on user's objective and requirement. Remote sensing provides multi-spectral, and multi temporal satellite images for accurate mapping. Land cover/Land use mapping provide basic inventory of land resources. This mapping can be local or regional in scope; it depends on user's objective and requirement. Vegetation extraction from remote sensing imagery is the process of extracting vegetation information by interpreting satellite images based on the interpretation elements such as the image color, texture, tone, pattern and association information, etc.
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- Agricultural Science
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