Greenhouse Crop Control Using Demand Forecasting and Sensor Feedback
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Solution Overview
Problem
Conventional methods for managing greenhouse operations, such as sowing and harvesting, fail to ensure adequate and non-wasteful produce delivery while inadequately addressing changing environmental conditions.
Innovation Solution
A networked greenhouse control system that communicates with remote enterprise resource planning systems to predict demand, generate crop production instructions, and optimize pallet loading, using sensors and automated equipment for precise crop management and harvesting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional approaches are used for sowing and harvesting in greenhouses, then operations can be performed with simple methods, but adequate and non-wasteful produce delivery cannot be ensured
Solution Approach 1:
The system segments the greenhouse management into distinct functional modules: demand planning engine for prediction, crop management module for production instructions, harvest management module for picking instructions, and pallet management module for loading instructions. Each module handles specific aspects independently, improving reliability through specialized functionality while managing complexity through modular design.
Solution Approach 2:
The system implements feedback loops where the demand planning engine receives actual sales data and forecast accuracy information to continuously improve predictions. The crop management system receives harvest data to adjust production instructions, and the pallet management system receives shipping data to optimize loading patterns. This feedback mechanism ensures reliable produce delivery by adapting to actual performance.
2Adaptability or versatility
If conventional approaches are used for managing greenhouse operations, then operations can be performed with simple processes, but changing environmental conditions cannot be adequately addressed
Solution Approach 1:
The system dynamically adjusts crop production instructions based on real-time environmental sensor data from greenhouses. The demand planning engine continuously updates predictions as new sales data becomes available, and the crop management module modifies sowing and harvesting instructions in response to changing conditions. This dynamic adaptation enables the system to handle environmental variations without requiring overly complex manual intervention.
3Productivity
If demand planning is implemented to predict crop demand, then produce delivery can be optimized, but system complexity increases
Solution Approach 1:
The demand planning engine performs preliminary demand prediction before the actual harvesting and shipping operations. By forecasting crop demand in advance based on historical sales data and market trends, the system can pre-plan production schedules, harvest timing, and pallet configurations. This preliminary action improves delivery efficiency by preventing last-minute adjustments while keeping the system structure manageable through sequential processing.
4Manufacturing precision
If automated crop management is implemented, then harvesting precision can be improved, but operational complexity increases
Solution Approach 1:
The crop management system generates its own production and harvest instructions automatically based on demand predictions and environmental data. The system self-adjusts sowing schedules, harvest timing, and pallet loading configurations without requiring manual intervention for each decision. This self-service capability maintains harvesting precision while simplifying operations by eliminating the need for complex manual coordination.
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 predication 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.