Irrigation Control System with Dynamic Crop Growth Model Reset
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional agricultural irrigation systems face challenges in accurately predicting and adjusting irrigation needs due to factors like pest infestation, disease, and abnormal temperatures, which can affect crop growth and water requirements, leading to over or under-watering.
Innovation Solution
An irrigation system that incorporates sensors and remote imaging systems to detect significant crop events, allowing the control system to modify or reset crop growth models and adjust irrigation schedules dynamically, ensuring precise water delivery based on real-time crop data and aerial imagery analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional crop growth modeling is used for irrigation scheduling, then irrigation can be controlled based on crop maturity and health, but accuracy deteriorates when significant crop events like pest infestation, disease, or abnormal temperatures occur
Solution Approach 1:
The system continuously monitors crop growth data from sensors and aerial imagery, compares actual growth against predicted growth from the crop growth model, and detects deviations indicating significant crop events. This feedback loop enables the system to identify when pest infestation, disease, or abnormal temperatures are affecting crop growth, triggering model resets to maintain irrigation scheduling accuracy.
Solution Approach 2:
The system proactively detects significant crop events by monitoring for deviations in crop growth patterns before they severely impact yield. By identifying pest infestations, diseases, or temperature stress early through growth model comparisons, the system can reset the crop growth model and adjust irrigation schedules in advance, preventing further crop damage and maintaining optimal growth conditions.
2Reliability
If irrigation schedules are adjusted frequently to respond to changing crop conditions, then crop health and water conservation improve, but system complexity and data processing requirements increase
Solution Approach 1:
The crop growth model serves as an intermediary that translates complex sensor data and aerial imagery into actionable irrigation scheduling decisions. By comparing predicted growth against actual growth, the model simplifies the detection of significant crop events and generates clear signals for irrigation adjustments, reducing the complexity of real-time control decisions while maintaining reliable crop health monitoring.
Data Source
AI summary
An irrigation system includes a plurality of mobile support towers driven my motors; a fluid-carrying conduit supported by the mobile towers; a number of water-emitters connected to the conduit; one or more valves which can be opened or closed to control fluid flow through the water emitters; and a control system. The control system controls the speed of the mobile towers and the flow of water through the water emitters in accordance with one or more irrigation scheduling plans. The control system also receives crop growth data from one or more sensors and aerial image data from one or more remote imaging systems and detects significant crop events from the data and improves irrigation scheduling in response to such detections.