AI-Driven Coffee Cultivation Support System
Budget: $250 – $750 USD
We are building Version 1.0 of an Agricultural AI Backend (Microservices architecture) specifically for coffee tree cultivation in Vietnam. The system requires 3 core REST APIs. All API JSON outputs must be in Vietnamese.
The 3 Required APIs:
Pest & Disease Detection (Computer Vision): Uses YOLOv8/EfficientNet to analyze leaf photos and identify specific coffee diseases (rust, pink disease, stem cracking) with >90% accuracy.
Chemical Compliance Assistant (Vision + RAG): Takes an image of a chemical bottle, uses OCR to extract text, and uses a RAG pipeline (Langchain + Gemini/OpenAI) to check against our provided allowed/banned database. Must trigger a system action payload to "lock cultivation" if banned.
Yield Prediction (Machine Learning): Uses XGBoost to process real-time IoT data (soil moisture, rainfall, etc.) and historical data to predict harvest yield.
Tech Stack Requirements:
Python, FastAPI
YOLOv8, XGBoost
LangChain, Vector DB (FAISS/ChromaDB)
Docker & AWS EC2 (g4dn.xlarge) deployment
What We Provide:
Proprietary coffee disease images + public datasets
CSV database of permitted/banned chemicals
IoT data formats and historical yield sample data
Cloud infrastructure costs (AWS/API keys are paid by us separately)
The 3 Required APIs:
Pest & Disease Detection (Computer Vision): Uses YOLOv8/EfficientNet to analyze leaf photos and identify specific coffee diseases (rust, pink disease, stem cracking) with >90% accuracy.
Chemical Compliance Assistant (Vision + RAG): Takes an image of a chemical bottle, uses OCR to extract text, and uses a RAG pipeline (Langchain + Gemini/OpenAI) to check against our provided allowed/banned database. Must trigger a system action payload to "lock cultivation" if banned.
Yield Prediction (Machine Learning): Uses XGBoost to process real-time IoT data (soil moisture, rainfall, etc.) and historical data to predict harvest yield.
Tech Stack Requirements:
Python, FastAPI
YOLOv8, XGBoost
LangChain, Vector DB (FAISS/ChromaDB)
Docker & AWS EC2 (g4dn.xlarge) deployment
What We Provide:
Proprietary coffee disease images + public datasets
CSV database of permitted/banned chemicals
IoT data formats and historical yield sample data
Cloud infrastructure costs (AWS/API keys are paid by us separately)