Greenhouse Crop Control With AI Demand Forecasting
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Solution Overview
Problem
Conventional approaches to managing greenhouse operations fail to ensure adequate and non-wasteful production and harvesting of crops, while also neglecting to adapt to changing environmental conditions.
Innovation Solution
A networked greenhouse control system that utilizes a demand planning engine to predict crop demand, generate production instructions, and optimize pallet loading, inventory management, and harvest operations, incorporating AI-driven forecasting and real-time data from sensors and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches are used to manage greenhouse operations, then simplicity of operation is maintained, but crop demand prediction accuracy and production optimization deteriorate
Solution Approach 1:
The patent introduces a demand planning engine as an intermediary component that receives order data from an enterprise resource planning system and processes it to generate crop production instructions. This intermediary layer transforms raw order data into actionable production schedules, enabling accurate demand prediction without requiring direct complex integration between all system components.
Solution Approach 2:
The system is divided into distinct functional modules: a demand planning engine for prediction, a production management system for instruction generation, and integration interfaces with enterprise resource planning systems. This segmentation allows each module to specialize in specific tasks, improving overall prediction accuracy while maintaining manageable system complexity through modular architecture.
2Loss of substance
If conventional greenhouse management is used, then operational simplicity is maintained, but crop waste increases due to inadequate production planning
Solution Approach 1:
The demand planning engine performs preliminary analysis of order data and generates crop production instructions before the actual production cycle begins. By predicting demand in advance and creating production schedules beforehand, the system ensures that crops are planted and harvested in optimal quantities, minimizing waste while implementing automation at the planning stage rather than requiring full operational automation.
Solution Approach 2:
The system incorporates feedback loops where production outcomes and actual demand data are fed back into the demand planning engine to continuously refine prediction accuracy. This feedback mechanism enables the system to learn from past performance and improve future predictions, reducing crop waste progressively while maintaining a moderate level of automation that adapts over time.
3Adaptability or versatility
If conventional approaches are used, then system simplicity is maintained, but adaptability to changing environmental conditions deteriorates
Solution Approach 1:
The production management system dynamically adjusts crop production instructions based on real-time environmental data and changing demand conditions. The system can modify planting schedules, harvest timing, and allocation decisions in response to environmental variations, enabling adaptability through dynamic control mechanisms that respond to changing conditions without requiring complete system redesign.
4Measurement precision
If demand prediction is implemented, then crop allocation accuracy improves, but information processing requirements increase
Solution Approach 1:
The demand planning engine extracts only the essential and relevant features from the input order data that are necessary for accurate demand prediction. By identifying and processing only the critical information elements rather than analyzing all available data, the system achieves high allocation accuracy while minimizing unnecessary data processing load and information loss.
Data Source
AI summary
Methods and systems are disclosed configured to control the planting, application of pesticides, and harvesting of greenhouse crops, such as herbs. The greenhouse may include a variety of sensors, such as moisture sensors, ph sensors, and/or CO2 sensors. Unmanned vehicles may be utilized to capture crop images, and a learning engine may be used to determine the size of greenhouse crops. Such sensor data may be used to predict crop availability. A prediction engine may be utilized to predict demand for greenhouse crops using current and historical orders for greenhouse crops. Greenhouse crop production instructions may be generated and transmitted to a greenhouse computer system to cause crops to be sown or harvested. Pallet loading instructions may be generated regarding the loading of specified quantities of crop packs on respective pallets for shipment to a destination.