Agricultural Timing System Using Crowdsourced Crop Data
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Solution Overview
Problem
Existing agricultural models fail to accurately predict optimal times for operations like hay baling due to inaccuracies in weather forecasts and a lack of real-time crop condition data, leading to suboptimal timing decisions.
Innovation Solution
A method and system that utilize a computing device with a time estimator algorithm to calculate a predicted optimal time for agricultural operations by integrating current crop data, weather forecasts, and crowdsourcing data from nearby fields, adjusting the timing based on real-time activities and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional agricultural models use weather forecasts to predict optimal timing, then the system can provide advance planning guidance, but the accuracy deteriorates when forecasts are inaccurate
Solution Approach 1:
The system implements feedback by collecting actual crop condition data from sensors and operational timing data from multiple fields, then using this feedback to continuously refine and adjust the predictive model. The machine learning algorithm learns from the discrepancy between predicted and actual optimal timings across the agricultural network, improving forecast accuracy over time.
Solution Approach 2:
The patent introduces an intermediary layer of distributed sensors and data collection points across multiple fields that mediate between raw weather data and the final timing recommendation. This intermediary network of field-specific data collection provides localized real-time conditions that bridge the gap between general forecasts and field-specific optimal timing.
2Measurement precision
If the system collects real-time crop condition data from sensors, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The system employs universal, multi-functional sensor nodes that can measure multiple crop parameters (moisture, temperature, growth stage) and environmental conditions simultaneously. These standardized sensor units can be deployed across multiple fields, reducing overall system complexity through reuse of proven components while maintaining high measurement precision.
Solution Approach 2:
The sensors are designed to be self-calibrating and self-diagnosing, automatically adjusting to local conditions and detecting their own status. This self-service capability reduces the need for complex external calibration systems and manual maintenance, thereby lowering device complexity while preserving measurement accuracy.
3Reliability
If the system integrates crowdsourcing data from multiple fields, then the predictive accuracy improves, but the quantity of data to process increases
Solution Approach 1:
The patent segments the large volume of crowdsourcing data into field-specific, crop-type-specific, and operation-type-specific datasets. This segmentation allows the system to process and analyze only relevant portions of data for each prediction task, reducing computational burden while maintaining the benefits of aggregated multi-field insights.
Solution Approach 2:
The system implements partial data processing by selectively incorporating only the most relevant features and parameters from the crowdsourcing data that have the highest impact on prediction accuracy. Rather than processing all available data equally, the machine learning model focuses on the critical subset of information that drives optimal timing decisions.
Data Source
AI summary
A method of generating a suggested optimal time for performing an agricultural operation in a field includes receiving data related to a current condition of a crop in the field. A predicted optimal time to perform the agricultural operation in the field is calculated using an agricultural model. Crowdsourcing data is received related to agricultural operations occurring within a defined area surrounding the field and within a defined preceding time period. The predicted optimal time to perform the agricultural operation is adjusted based on the crowdsourcing data, to generate the suggested optimal time to perform the agricultural operation. The suggested optimal time is communicated to a communicator located remote from the computing device and then displayed on the communicator.