Centralized Greenhouse Analytics System for Hydroponic Yield Forecasting
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Solution Overview
Problem
Manual planning in greenhouse food production is fragmented, failing to provide real-time, data-driven decision-making for fast-moving consumer products like produce, leading to inefficiencies in demand planning, order fulfillment, and resource management.
Innovation Solution
A centralized planning and analytics system that integrates data from greenhouses with purchasing and accounting systems, utilizing a processor with machine learning algorithms to optimize pond maps, reduce waste, and enhance automation, analytics, food safety, and maintenance functions, including real-time demand forecasting and cross-supply planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual planning processes are used in greenhouse food production, then operational simplicity is maintained, but real-time data-driven decision-making capability deteriorates
Solution Approach 1:
The patent merges multiple previously separate systems (greenhouse operations, purchasing, accounting, forecasting) into a single centralized planning platform. This integration allows real-time data sharing across all functions, eliminating information silos and enabling comprehensive data-driven decision-making without requiring separate manual processes for each function.
Solution Approach 2:
The centralized planning platform serves multiple functions simultaneously: it handles greenhouse operations management, purchasing coordination, accounting integration, demand forecasting, and order fulfillment. This multi-functional design consolidates what were previously separate systems into one universal platform that provides real-time insights across all business areas.
2Productivity
If centralized real-time planning systems are implemented, then decision-making speed is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating demand forecasts, pre-planning harvest schedules, and pre-coordinating purchase orders before actual operations begin. The forecasting module continuously predicts future demand and adjusts planting and harvesting plans in advance, allowing the system to proactively respond to market conditions rather than reactively managing orders as they arrive.
Solution Approach 2:
The platform implements continuous feedback loops where actual harvest data, sales information, and inventory levels are automatically fed back into the planning system. This real-time feedback allows the system to adjust forecasts, reorder points, and scheduling decisions dynamically, improving order fulfillment efficiency while managing complexity through automated closed-loop control rather than manual intervention.
3Quantity of substance
If manual tracking and planning methods are used, then system simplicity is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time data and forecasted demand. Harvest plans are continuously optimized to match predicted market requirements, and purchasing decisions are adjusted based on actual inventory levels and forecasted sales. This dynamic planning allows the system to efficiently allocate water and other resources to high-demand crops while reducing or eliminating water usage for low-demand or excess produce, thereby improving overall resource utilization efficiency.
Data Source
AI summary
A system for centralized planning and analytics for greenhouse growing of hydroponic produce, the system comprising: at least one greenhouse; an imaging system; a climate module; a forecasting system; a storage medium; a processor which comprises at least a machine learning algorithm which improves the accuracy and the yield forecast; and a network which provides a communication pathway for information to move between at least two of the group consisting of: the greenhouse, the imaging system, the climate module, the storage medium and the processor; wherein the forecasting system generates crop analytics from the imaging system; generates climate trends from the climate module disposed in each the at least one greenhouse; and uses a machine learning algorithm to determine yield forecast of the hydroponic produce.