About Prompt
- Prompt Type – Dynamic
- Prompt Platform – ChatGPT, Grok, Deepseek, Gemini, Copilot, Midjourney, Meta AI and more
- Niche – Plant Health Monitoring
- Language – English
- Category – Precision Agriculture
- Prompt Title – AI Prompt for Crop Disease Detection from Field Images
Prompt Details
This prompt is designed for dynamic generation and usage across various AI platforms for precision agriculture applications. It aims to detect and analyze crop diseases from field images, providing detailed insights for effective disease management.
**Prompt Template:**
“`
Analyze the provided image of [Crop Name] taken on [Date] at [Time] in [Location (GPS coordinates if available)] using [Imaging Technique (e.g., RGB, multispectral, hyperspectral)] for signs of disease.
**Image Input:** [Image URL or File Path]
**Specific Instructions:**
1. **Disease Identification:** Detect and identify the presence of any diseases affecting the [Crop Name]. Provide the scientific name (Latin binomial) of the detected disease(s) if possible, along with the common name(s). If multiple diseases are present, list each separately. If no disease is detected, state “No disease detected.”
2. **Severity Estimation:** Estimate the severity of each identified disease on a scale of 0-4, where:
* 0: No disease
* 1: Mild symptoms (<10% affected area)
* 2: Moderate symptoms (10-25% affected area)
* 3: Severe symptoms (25-50% affected area)
* 4: Very severe symptoms (>50% affected area)
3. **Location of Disease (Optional, if platform supports):** If possible, indicate the location of the disease on the image. This can be in the form of bounding boxes, segmentation masks, or textual descriptions like “top leaves,” “lower stem,” etc.
4. **Disease Characteristics (Optional):** Describe the visual characteristics of the detected disease(s), such as lesions, discoloration, wilting, etc. Include specific details like color, shape, size, and texture. This information helps validate the identification and aids in differentiating between similar diseases.
5. **Recommendations (Optional):** If the platform supports it, suggest appropriate management strategies based on the detected disease(s) and severity. This can include recommendations for chemical treatments (with precautions), cultural practices, biological control methods, or further diagnostic testing. Clearly indicate if recommendations are general or location-specific. Include disclaimers about consulting with local experts for tailored advice.
6. **Confidence Level:** Provide a confidence score (percentage) for each identified disease, reflecting the certainty of the AI’s analysis.
**Output Format:**
Preferably JSON format for easy integration with other systems. Example:
“`json
{
“image”: “[Image URL or File Path]”,
“crop”: “[Crop Name]”,
“date”: “[Date]”,
“time”: “[Time]”,
“location”: “[Location]”,
“imaging_technique”: “[Imaging Technique]”,
“diseases”: [
{
“scientific_name”: “[Scientific Name]”,
“common_name”: “[Common Name]”,
“severity”: “[Severity Score (0-4)]”,
“location”: “[Location on Image (Optional)]”,
“characteristics”: “[Description of Visual Characteristics (Optional)]”,
“recommendations”: “[Management Recommendations (Optional)]”,
“confidence”: “[Confidence Score (Percentage)]”
},
{
// … additional diseases
}
]
}
“`
If JSON output is not possible, provide a clear, structured textual report containing the same information.
**Dynamic Elements:**
This prompt template is designed to be dynamic. The following elements should be replaced with specific values for each image analysis:
* `[Crop Name]`
* `[Date]`
* `[Time]`
* `[Location (GPS coordinates if available)]`
* `[Imaging Technique (e.g., RGB, multispectral, hyperspectral)]`
* `[Image URL or File Path]`
By dynamically populating these elements, this prompt can be adapted to analyze a wide range of field images and cater to specific needs in precision agriculture. It encourages the AI to provide detailed, structured outputs facilitating automated data processing, disease monitoring, and decision-making. Remember to adjust the optional instructions based on the capabilities of the specific AI platform being used.
“`
This detailed prompt provides a comprehensive framework for analyzing crop diseases from field images. The dynamic nature and structured output format make it suitable for various AI platforms and facilitate integration with other precision agriculture tools. Remember to tailor the optional sections based on the specific capabilities of your chosen AI platform and always validate AI-generated insights with expert knowledge.