Harvest Planning for
a Regional Fruit Cooperative
- Client
- Regional network of fruit farms
- Industry
- Agriculture
- Service
- Data Integration & Cleaning|Forecasting Model Development|Analytics Dashboard Setup|Custom Rule Engine & Alerting
Overview
A regional network of 12 family-owned fruit farms in eastern Europe faced a recurring seasonal challenge: aligning their raspberry and apple fruit harvests with short, volatile market windows. Weather shifts, unstructured data, and guesswork-based planning often resulted in missed demand, unharvested crops, or rushed sales at low prices.
Working with Sphere, the cooperative implemented a lightweight AI and data analytics platform that helped them forecast local market demand, anticipate peak harvest windows, and make better decisions with the data they already had—no sensors, drones, or advanced infrastructure required.
Challenges
The cooperative faced rising inefficiencies as production scaled — without the tools to forecast demand, coordinate harvests, or prevent waste. Data existed, but it wasn’t being used to drive decisions.
- Unpredictable Buyer Demand: Last-minute retailer orders made it difficult to plan harvesting and sales in advance.
- Reactive Planning: Without forecasts or alerts, farms missed chances to optimize pricing or pool logistics.
- Siloed Data Across Farms: Each farm managed records separately, with no shared view of yields, pricing, or timing.
- Frequent Overstock and Spoilage: Crops were harvested based on habit, often exceeding demand and leading to waste.
Our Solution
Sphere helped the cooperative build a simple, cloud-based data insights tool using open-source AI components and Excel-friendly dashboards.
Data Aggregation and Normalization
Connected and cleaned up spreadsheets from each farm — historical yield logs, past sales, and buyer order records. Combined them with public data like weather forecasts and local market pricing trends into a shared workspace.
Lightweight AI Forecasts
Trained a time-series model to predict short-term demand and harvest volume for each fruit type, with only a few weeks of input data needed.
Weekly Harvest & Sales Planner
Created a shared dashboard (via Google Data Studio) with harvest timing predictions, estimated buyer demand, and “traffic light” alerts (green: harvest as planned; yellow: monitor; red: risk of oversupply).
Rule-Based Notifications
Set up simple rules — e.g., if predicted yield exceeds demand by >10%, flag in dashboard and suggest early sales or pre-harvest buyer outreach. Alerts sent via WhatsApp or email to farm leads.
Key Achievements
- Better alignment with buyer orders and weather trends led to more precise harvesting and less spoilage.
- Instead of last-minute rushes, farms contacted buyers 4–5 days in advance with realistic quantity estimates, improving pricing and relationships.
- Farmers began syncing harvest dates and pooling logistics when needed, based on shared data they trusted.
- No cameras, no IoT, no dev team — just smart use of spreadsheets, open tools, and simple predictive models.
Result
This case proves that AI doesn’t have to be complex to be useful. With just basic data, a little modeling, and a simple dashboard, a group of small farms turned scattered spreadsheets into actionable decisions. Our client didn’t need new tech — they used only the right questions, the right signals, and a partner to help them translate data into impact. As one farmer put it: “We used to guess. Now we know when and how much to pick.”
