AgroPredict: An Intelligent Agricultural Diagnosis System Integrating Computer Vision and Generative AI

AgroPredict is an end-to-end intelligent agricultural diagnosis system integrating computer vision, machine learning, and generative AI, designed specifically for corn farmers. It enables crop disease diagnosis, yield loss prediction, and provides personalized actionable agronomic recommendations through a single leaf image and environmental sensor data, helping to address traditional agricultural pain points.

Global food security is receiving increasing attention. Traditional agriculture relies on manual inspections to detect diseases, which often misses the optimal prevention and control window. Accurate diagnosis requires the experience of professional agronomists, and ordinary farmers face high technical barriers. There is an urgent need for AI technology to empower agricultural production.

Intelligent Disease Diagnosis

Using computer vision technology to analyze leaf images, identify potential disease types, and lower the threshold for professional knowledge.

Yield Loss Prediction

Combining disease severity, spread trends, and environmental data, it estimates yield losses through machine learning models to help farmers plan response measures in advance.

Generative AI Agronomic Recommendations

Dynamically generates personalized recommendations based on diagnosis results and environmental conditions, including pesticide types, application doses, and prevention measures.

The system integrates leaf images and environmental sensor data (temperature, humidity, soil pH value, etc.) to form a crop health profile. The technology stack includes:

• Computer Vision: Leaf image feature extraction and disease identification
• Machine Learning: Yield prediction and trend analysis
• Generative AI: Natural language agronomic recommendation generation The integration of multiple technologies forms a complete closed loop from perception to decision-making.

• Early Disease Detection: Instant diagnosis supports early intervention for diseases
• Lower Technical Barriers: Popularize digital professional diagnosis capabilities to ordinary farmers
• Data-Driven Decision Making: Optimize resource allocation based on data to reduce blind investment
• Knowledge Inheritance: Recommendations come with principle explanations to help farmers accumulate agricultural knowledge

Challenges: Insufficient data quality and diversity, difficulty in adapting to edge devices, need for optimized user interaction, and incomplete integration with existing agricultural processes. Directions: Expand multi-crop support, multi-language localization, enhance offline operation capabilities, and deeply integrate agricultural IoT devices.

AgroPredict represents an important direction for the intelligentization of agricultural technology, helping to improve agricultural production efficiency and reduce food losses. In the future, the participation of open-source communities will accelerate technology popularization, and similar systems are expected to play a key role in global food security assurance.