Effective Date: 01 June 2025
At Agrinofy, we leverage Artificial Intelligence (AI) and Machine Learning (ML) models to provide data-driven agricultural insights and services. However, we make the following disclaimer regarding the accuracy of AI-generated outputs:
1. No Guarantee of 100% Accuracy
While our AI systems are trained using high-quality agricultural datasets and are continually monitored for performance, no AI system is infallible. Predictions, recommendations, and diagnostics (e.g., crop health, pest detection, seed planning) are probabilistic and may not always reflect actual field conditions.
2. Advisory in Nature
All insights generated by Agrinofy’s platforms are advisory only and are not a substitute for expert human judgment, local agronomic knowledge, or on-ground field validation.
3. Environmental Factors
Weather patterns, soil variability, disease outbreaks, and other external factors may significantly impact the reliability of AI-based recommendations.
4. User Responsibility
Users must exercise due diligence before acting on AI-generated advice. Agrinofy will not be held liable for direct or indirect loss, damages, or adverse agricultural outcomes arising from reliance on AI predictions.
5. Third-party Tools
Some services may integrate third-party AI models or APIs. We disclaim responsibility for the accuracy or outcomes from such third-party services.
🔁 AI Model Retraining Policy
Effective Date: 01 June 2025
To ensure ongoing accuracy, relevance, and performance of our AI systems, Agrinofy maintains the following AI Model Retraining Policy:
1. Scheduled Retraining
- AI models are retrained quarterly using updated data to reflect seasonal, regional, and climate variability.
- High-use modules (e.g., pest & disease detection) may be retrained monthly based on usage and feedback volume.
2. Retraining Triggers
- A significant decline in model performance (as indicated by accuracy metrics).
- Introduction of new crops, geographies, or pest/disease categories.
- Feedback loops from farmers or field experts indicate systemic errors.
- Inclusion of newly available datasets (e.g., satellite imagery, IoT sensor data).
3. Data Sources
Retraining data is sourced from:
- Partner research institutions
- Verified user feedback and survey data
- Public agricultural databases (e.g., FAO, IRRI)
- Real-time sensor and drone data (if applicable)
4. Ethical & Regulatory Compliance
All AI model training adheres to ethical AI principles and respects:
- Data privacy (GDPR/CCPA compliant)
- Bias mitigation practices
- Explainability and transparency requirements
5. Model Versioning
Agrinofy maintains version control of all AI models. Users may be notified when a major update to an AI module occurs.