Farmers and agricultural businesses invest significant resources in crop production, and variations in yield can have substantial financial impacts. If a crop underperforms, it can lead to revenue losses and strain resources.
Size: 1.5 MB
Language: en
Added: Sep 11, 2024
Slides: 14 pages
Slide Content
Crop Yield Predictive Analytics Use Case
Crop Yield Sample Application Description A farming cooperative can determine which crop types are most likely to yield high harvests under specific conditions. In this use case, the predictive model leverages key agricultural and environmental factors to estimate crop yields. By analyzing these variables, along with the type of crop grown, the model accurately predicts the crop yield, enabling farmers to make informed decisions and optimize their agricultural practices for enhanced productivity.
Crop Yield Sample Application Target Crop Yield Per Hector
Crop Yield Sample Application Algorithm(s) Gradient Boosting Regression is the method for predicting crop yield per hectare based on predefined categories. Higher R Square (>=70%) means the results are reliable and accurate. Lower R Square (<70%) means the model needs to be rebuilt using different input parameters.
Crop Yield Sample Application Model Visualization
Crop Yield Sample Application Model Visualization
Crop Yield Sample Application Model Summary
Crop Yield Sample Application Model Summary
Crop Yield Sample Application Interpretation
Crop Yield Sample Application Result Contains Predicted Crop Yield value along with regression residuals that shows comparison with the actual one.
Crop Yield Sample Application Result Crop prediction with probability value can be carried out using the APPLY functionality shown below.
Crop Yield Sample Application Result
Crop Yield Predictive Analytics Use Case For more information, contact us today. www.Smarten.com [email protected] Smarten – Crop Yield Use Case - 2024