Subsoil Sensor Prediction of Soil Density and Crop Yield
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Solution Overview
Problem
Current methods for monitoring and managing subsoil crops lack effective means to predict and maintain optimal soil density, leading to potential yield reductions due to soil congestion, nutrient deficiencies, and pest infestations.
Innovation Solution
A system comprising subsoil sensors that monitor soil conditions, a server with machine learning capabilities for predicting soil congestion, and a soil management robot that generates and executes maintenance plans to ensure loose, nutrient-rich soil and optimal growth conditions for subsoil crops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If soil sampling and above-ground monitoring are used to monitor subsoil crop health, then monitoring can be performed, but soil density prediction and prevention of soil congestion are not achieved
Solution Approach 1:
The patent replaces traditional mechanical soil sampling methods with acoustic sensing technology. Acoustic sensors detect sound waves generated by crop roots growing in the soil, and machine learning models analyze these acoustic signals to predict soil density and crop health conditions without physical soil disturbance. This substitution enables non-invasive, continuous monitoring that provides real-time soil density information while maintaining crop yield reliability.
Solution Approach 2:
The patent introduces acoustic signals as an intermediary between the crop root system and the monitoring system. Instead of directly measuring soil properties through sampling, the system uses acoustic waves generated by root growth as a mediator to indirectly detect soil density, moisture content, and nutrient availability. This intermediary approach enables continuous monitoring without disrupting the soil-crop system.
2Adaptability or versatility
If traditional monitoring methods are used, then current technology is sufficient, but soil congestion and nutrient deficiencies cannot be predicted
Solution Approach 1:
The patent creates a multi-functional monitoring system where acoustic sensors serve multiple purposes: detecting soil density, monitoring moisture content, assessing nutrient availability, and predicting crop health issues. The machine learning model integrates these various measurements to provide comprehensive soil condition prediction. This universal approach enables the system to adapt to different soil types and crop conditions while maintaining a unified monitoring platform.
Solution Approach 2:
The patent replaces complex mechanical soil sampling and analysis equipment with acoustic sensing and computational modeling. Instead of using multiple specialized devices for different soil measurements, the system uses acoustic signal analysis combined with machine learning to infer multiple soil properties simultaneously, reducing device complexity while enhancing prediction capabilities.
3Productivity
If no soil density prediction is performed, then current management practices continue, but crop yields reduce due to soil congestion
Solution Approach 1:
The patent implements preliminary action by using machine learning models to predict soil density and congestion conditions before they severely impact crop growth. The system continuously monitors acoustic signals and forecasts potential soil congestion issues, allowing farmers to take preventive measures such as adjusting irrigation, applying nutrients, or modifying cultivation practices before yield reduction occurs. This proactive approach maintains high productivity by preventing harmful conditions rather than reacting to them after damage occurs.
Solution Approach 2:
The patent establishes a feedback loop where acoustic sensor data continuously informs soil condition assessment and prediction. The machine learning model analyzes incoming acoustic signals, compares them against historical data and predictions, and provides real-time feedback on soil density and congestion risks. This feedback mechanism enables dynamic adjustment of agricultural practices to prevent soil congestion and maintain optimal conditions for maximum crop yield.
Data Source
AI summary
An approach to the prediction of soil density and subsoil crop growth may be provided. The approach may include subsoil sensor which can monitor changes in soil pressure and moisture conditions. The sensor data can be sent to a computer module which can process the data using a machine learning model predicting the soil density around a subsoil crop and the yield of the subsoil crop. A soil maintenance plan can be generated from the soil density prediction and/or the crop yield prediction. The soil maintenance plan can be sent to soil management robots, which can execute the soil maintenance plan.