Manual inventories in fields with unstable connectivity
The operations manager at a palm company faced the challenge of coordinating inventories and crop monitoring in fields with limited connectivity. Manual records and lack of real-time data made decision-making difficult and increased inventory costs, complicating daily pest and disease management for field teams.
Key points
- Manual inventory consumes time and resources.
- Variable connectivity limits access to digital tools.
- Pest and disease monitoring relies on unsystematic observations.
Task-focused app with AI and computer vision
The solution was a mobile application built for specific tasks: identifying, measuring, and syncing crop data from the field. The interface prioritizes camera capture, making it easier to record fruits and disease symptoms using deep learning and computer vision models, even with variable connectivity.
Operational indicators and modules aligned with integrated pest management (IPM) and good agricultural practices connect fieldwork with management decisions. The app enables differentiated profiles for field teams and data sync when network access is available.
Key points
- Simple interface oriented to daily field tasks.
- AI models to identify fruits and diseases from the camera.
- Flexible synchronization for environments with irregular connectivity.
Inventory cost reduction and tools for field teams
Digitalizing inventory and supporting pest management reduced inventory costs to 25% of the manual method. The field team now has AI-assisted tools for identification and disease detection, and can measure and record data for crop and bunch monitoring.
The app includes modules aligned with IPM and good agricultural practices, with synchronization and profiles adapted to field work.
Key points
- Inventory cost reduced to 25% of the manual method.
- AI tools for identification and disease detection.
- Data measurement and recording for crop and bunch monitoring.
Keeping AI effective in the field requires continuous monitoring and updates
For companies operating in rural or low-connectivity environments, digital solutions should prioritize clear tasks and work offline, syncing data when possible. Using AI in the field, especially for computer vision, requires adaptable models and training flows designed for real-world use.
Maintaining these tools useful means monitoring model performance and updating the knowledge base as crop conditions or new pests emerge. Without an AI governance and operations strategy (AI Ops), models can degrade and lose operational relevance.
It is also important to periodically review how teams use the app and whether collected data leads to operational decisions or productivity improvements.
Key points
- Field apps must work with or without connectivity.
- Monitoring and updating AI models is key for effectiveness.
- AI governance ensures technology stays aligned with operations.
