Harvest Planning for a Regional Fruit Cooperative

Turning scattered spreadsheets into harvest-ready insights for small farm. With zero new infrastructure.

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

Crop Loss Reduced by 15%

Better alignment with buyer orders and weather trends led to more precise harvesting and less spoilage.
Faster Sales Planning

Instead of last-minute rushes, farms contacted buyers 4–5 days in advance with realistic quantity estimates, improving pricing and relationships.
Stronger Collaboration

Farmers began syncing harvest dates and pooling logistics when needed, based on shared data they trusted.
Zero New Tech Infrastructure

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.”

Schedule a Free Session with our Data Experts

Flexible, fast, and focused — Sphere solves your tech challenges as you scale.

Luke Suneja

Client Partner

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