Predictive Soil Property Map for Agricultural Machine Control
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Solution Overview
Problem
Agricultural ground engaging machines face challenges in maintaining optimal performance due to variations in soil properties such as moisture, temperature, and nutrient levels, leading to suboptimal seed placement and tillage quality, which existing control systems struggle to address effectively due to latency in sensor readings and machine responses.
Innovation Solution
The system generates predictive soil property maps using in-situ sensors and information maps to model relationships between soil characteristics and properties, allowing for proactive control of downforce, tool position, and seed delivery systems, thereby optimizing operations across varying field conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time sensor readings are used to control ground engaging tools, then responsiveness to soil conditions is improved, but latency in sensor readings and machine responses causes suboptimal performance
Solution Approach 1:
The system performs preliminary actions by generating predictive maps of soil properties before the ground engaging tools actually encounter those conditions. In-situ sensors detect soil properties ahead of time, and predictive models forecast future soil conditions along the machine's path, allowing the control system to prepare appropriate tool settings in advance rather than reacting with delayed real-time adjustments.
Solution Approach 2:
Predictive models serve as intermediaries between raw sensor data and control actions. The models process sensor readings and generate predictive maps that bridge the time gap between detection and response, translating current sensor data into forecasts of future soil conditions that the control system can act upon immediately.
2Reliability
If ground engaging tools maintain fixed parameters during operation, then machine stability is improved, but variations in soil properties lead to suboptimal seed placement and tillage quality
Solution Approach 1:
The system implements dynamic control by continuously adjusting ground engaging tool parameters based on real-time and predictive soil condition data. Instead of fixed settings, the control system varies tool depth, downforce, and position dynamically as the machine moves across the field, adapting to changing soil properties while maintaining operational stability through automated feedback control.
Solution Approach 2:
The control system applies local quality by customizing tool parameters for specific geographic locations based on predictive soil maps. Each section of the field receives tailored tool settings appropriate to its predicted soil conditions, allowing optimal seed placement and tillage quality in each local area rather than using uniform settings across the entire field.
3Measurement precision
If predictive models are generated using in-situ sensors and information maps, then soil property prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The predictive model generator serves multiple functions: it processes data from various in-situ sensors, integrates information from existing field maps, generates predictive soil property maps, and outputs control recommendations. This multi-functional approach consolidates what could be separate complex systems into a unified platform that handles data acquisition, processing, prediction, and control guidance.
Data Source
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AI summary
An agricultural ground engaging machine (100) is disclosed. The agricultural ground engaging machine (100) comprising: a control system (314); a communication system (306) that receives an information map (358) that includes values of a characteristic corresponding to different geographic locations in a field; an in-situ sensor (308) that detects a value of a soil property corresponding to a geographic location in the field; a predictive model generator (310) that generates a predictive soil property model that models a relationship between characteristic values and values of the soil property based on the value of the soil property detected by the in-situ sensor (308) corresponding to the geographic location and a value of the characteristic in the information map (358) at the geographic location; and a predictive map generator (312) that generates a functional predictive soil property map of the worksite, that maps predictive values of the soil property to the different geographic locations in the worksite, based on the values of the characteristic in the information map (358) and based on the predictive soil property model. Furthermore, a method of controlling an agricultural ground engaging machine (100) is disclosed.