- Fresno
- Agriculture
- 15 weeks
- B2B SaaS
- Python, TensorFlow, PyTorch, Scikit-learn, AWS, PostgreSQL, Apache Spark
Project Brief
A big agricultural cooperative aimed to use artificial intelligence to improve crop output forecast, resource allocation, and overall farm management techniques. Their objective was to create an artificial intelligence powered precision agricultural platform that would offer farmers relevant insights and data-driven suggestions.
Client Background
The cooperative, representing millions of farmers across various commodities, struggled with unexpected weather patterns, pests, and illnesses. They recognised AI’s ability to analyse massive volumes of data and develop prediction models to assist farmers make better decisions.
Key Challenges:
Solution:
1. Discovery and Planning
During the discovery phase, we consulted with agronomists, data scientists, and farmers to learn their unique requirements and difficulties. We analysed current data sources, identified significant traits, and determined the extent of the AI models that would be constructed.
2. Development
We built the AI-powered platform on AWS, taking advantage of their scalable cloud architecture and machine learning capabilities. We collaborated extensively with the cooperative's IT staff to guarantee a flawless connection with their existing systems. Farmers received comprehensive instruction on how to utilise the platform and analyse AI-generated information.
3. Implementation
We collaborated together with the client's IT team to implement the EAM system throughout their asset portfolio. To ensure a smooth transition, data was migrated from legacy systems, users were thoroughly trained, and rigorous testing was performed. We also supplied continuous support and maintenance to resolve any difficulties that arose.
Tools & Technology Used
Python
Programming Language
TensorFlow
ML Framework
AWS
Cloud Services
PostgreSQL
Database
Apache Spark
Data Processing
Features:
Yield Prediction
The platform accurately predicts crop yields depending on weather, soil, and historical data.
Crop Health Monitoring
Crop Health Monitoring analyses satellite imagery and sensor data to identify early indicators of crop stress or illness, allowing farmers for prompt treatments.
Irrigation Optimization
Recommends optimal irrigation schedules based on weather forecasts, soil moisture levels, and crop water requirements, conserving water resources.
Fertilizer Recommendation
Optimise irrigation schedules depending on weather, soil moisture, and crop water needs to conserve water resources.