About Prompt
- Prompt Type – Dynamic
- Prompt Platform – ChatGPT, Grok, Deepseek, Gemini, Copilot, Midjourney, Meta AI and more
- Niche – Yield Forecasting
- Language – English
- Category – Precision Agriculture
- Prompt Title – AI Prompt for Predicting Crop Yield Based on Weather and Soil Data
Prompt Details
**Prompt Type:** Dynamic
**Target Audience:** All AI Platforms (adaptable for specific platforms with minor modifications)
**Niche:** Yield Forecasting for Precision Agriculture
**Goal:** Predict crop yield for a specific geographic area and crop type using historical and real-time weather and soil data.
**Prompt Structure:**
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Predict the yield of [Crop Type] in [Geographic Area] for the [Harvest Year] based on the provided weather and soil data. Consider the growth stages of the crop and the specific impact of various weather parameters and soil properties on yield. Output the predicted yield in [Units] per [Area Unit] along with a confidence level (percentage) for the prediction. Also, identify the key contributing factors influencing the yield prediction, both positive and negative.
**Data Input Format:**
* **Geographic Area:** [Latitude, Longitude] or [Polygon Coordinates] or [Place Name] (e.g., “Iowa, USA”)
* **Crop Type:** [Specific Crop Name] (e.g., “Corn”, “Soybean”, “Wheat”)
* **Harvest Year:** [YYYY] (e.g., “2024”)
* **Units:** [Units of Yield] (e.g., “bushels”, “tons”, “kilograms”)
* **Area Unit:** [Unit of Area] (e.g., “acre”, “hectare”)
* **Weather Data (Historical and Real-Time):**
* **Date:** [YYYY-MM-DD]
* **Temperature (Min/Max/Average):** [Degrees Celsius]
* **Precipitation:** [Millimeters]
* **Solar Radiation:** [MJ/m²/day]
* **Humidity:** [%]
* **Wind Speed:** [Meters/second]
* **Evapotranspiration:** [Millimeters] (Optional)
* **Other Relevant Weather Parameters:** [Specify units] (e.g., Growing Degree Days)
* **Soil Data:**
* **Soil Type:** [Soil Classification] (e.g., “Clay Loam”)
* **Soil pH:** [pH value]
* **Organic Matter:** [%]
* **Nitrogen Content:** [ppm]
* **Phosphorus Content:** [ppm]
* **Potassium Content:** [ppm]
* **Soil Moisture:** [%] (Real-time preferred, historical acceptable)
* **Other Relevant Soil Properties:** [Specify units] (e.g., Field Capacity, Wilting Point)
**Output Format:**
* **Predicted Yield:** [Numeric value] [Units] per [Area Unit]
* **Confidence Level:** [Percentage] (e.g., “95%”)
* **Key Contributing Factors:**
* **Positive Factors:** [List of factors and their impact] (e.g., “Optimal rainfall during the flowering stage”)
* **Negative Factors:** [List of factors and their impact] (e.g., “High temperatures during grain filling”)
**Optional Enhancements (Adapt as needed):**
* Specify the growth stages of the crop (e.g., vegetative, reproductive, ripening) and request yield predictions for each stage.
* Request a breakdown of yield components (e.g., number of kernels per ear, kernel weight).
* Request spatial variability in yield prediction within the given geographic area.
* Provide historical yield data for the same geographic area and crop type to improve prediction accuracy.
* If using a specific AI platform, tailor the prompt to utilize its specific functionalities (e.g., in-built weather data access, specific data formatting requirements).
* If available, incorporate management practices data (e.g., planting date, fertilizer application, irrigation) for enhanced prediction accuracy.
**Example:**
Predict the yield of Corn in Iowa, USA for the 2024 harvest year based on the provided historical weather data from 2019-2023 and real-time weather and soil data from the current growing season. Consider the growth stages of the crop and the specific impact of weather parameters like temperature, precipitation, and solar radiation on yield. Output the predicted yield in bushels per acre along with a confidence level for the prediction. Also, identify the key contributing factors influencing the yield prediction. [Insert weather and soil data in the specified format].
“`
This dynamic prompt allows customization based on the specific needs of the user. It provides a structured format for data input and output, making it easier for the AI model to understand and process the request. The inclusion of optional enhancements allows for increased detail and accuracy in yield prediction. By following best practices for prompt engineering, this prompt maximizes the effectiveness of AI models in the yield forecasting niche for precision agriculture.