Greenhouse Crop Scheduling With Demand Forecasting and Pallet Loading
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Solution Overview
Problem
Conventional approaches to 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
1Measurement precision
If conventional approaches are used for sowing and harvesting in greenhouses, then operational simplicity is maintained, but produce delivery accuracy and waste prevention deteriorate
Solution Approach 1:
The system segments the greenhouse management process into distinct functional modules: demand receiving module, prediction module, instruction generation module, and execution module. Each module handles a specific aspect of crop management, allowing the complex system to be broken down into manageable, specialized components that can be developed and maintained independently while working together to achieve precise produce delivery.
Solution Approach 2:
The system performs preliminary actions by receiving demand information in advance and using the prediction module to forecast future produce requirements before the actual harvesting period. This allows the system to proactively generate sowing and harvesting instructions that ensure adequate produce availability while preventing waste, rather than reacting to shortages or excesses after they occur.
2Adaptability or versatility
If conventional sowing and harvesting methods are used, then operational simplicity is maintained, but responsiveness to demand fluctuations deteriorates
Solution Approach 1:
The system implements feedback mechanisms where the prediction module continuously receives demand information from the demand receiving module and adjusts harvesting instructions accordingly. The system monitors actual produce delivery outcomes and uses this feedback to refine future predictions and instructions, enabling adaptive responsiveness to demand fluctuations while maintaining a manageable control structure through iterative improvement.
Solution Approach 2:
The system embraces dynamics by making the prediction module and instruction generation adaptable to changing conditions. The prediction algorithms can adjust to seasonal variations, market demand changes, and environmental factors, allowing the greenhouse operations to dynamically respond to fluctuating demands rather than relying on static, predetermined schedules.
3Loss of substance
If conventional harvesting approaches are used, then operational simplicity is maintained, but produce waste increases
Solution Approach 1:
The system performs preliminary actions by generating precise harvesting instructions in advance based on predicted demand and actual orders. This allows the greenhouse to prepare exactly the right amount of produce for delivery, avoiding both shortages and excesses that would lead to waste. The preliminary planning ensures that harvesting efficiency is maintained while minimizing unnecessary production and subsequent waste.
Solution Approach 2:
The system changes key parameters such as sowing dates, harvesting times, and quantities based on real-time demand information and predictions. By dynamically adjusting these parameters, the system optimizes produce delivery to match actual market needs, thereby reducing waste from overproduction while maintaining high harvesting efficiency through precise, data-driven instructions.
4Adaptability or versatility
If conventional environmental monitoring is used, then system simplicity is maintained, but responsiveness to environmental changes deteriorates
Solution Approach 1:
The system implements multi-functional monitoring that serves multiple purposes: tracking environmental conditions, predicting their impact on crop growth, and adjusting harvesting instructions accordingly. This universal approach allows a single integrated system to handle various environmental factors (temperature, humidity, light) and translate them into actionable insights for optimize produce delivery, reducing the need for separate specialized systems while enhancing environmental adaptability.
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.