Crop Management System for Yield Goal Optimization
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Solution Overview
Problem
Agricultural growers face challenges in realizing a return on investment for crop inputs due to unawareness of factors limiting crop output, leading to inefficiencies in crop management.
Innovation Solution
A system and method that uses a computing device to set yield goals, analyze tissue samples and seed populations, predict growth plans for water and nutrient applications, identify potential shortcomings in these plans, and offer growers options to adjust yield goals or alter equipment operations to meet these goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If growers apply more crop inputs to increase output, then crop yield may improve, but the return on investment does not justify the costs
Solution Approach 1:
The system applies local quality by providing site-specific crop management recommendations based on spatially variable field data. It divides the field into zones with different characteristics and provides tailored input recommendations for each zone, ensuring that inputs are applied only where needed and at optimal rates, thereby maximizing yield return on investment.
Solution Approach 2:
The system performs preliminary action by analyzing field data, tissue samples, and environmental factors before the growing season or early in the season to predict yield potential and identify limiting factors. This allows growers to make informed decisions about input application timing and rates, optimizing the return on investment before resources are committed.
2Productivity
If growers apply more crop inputs to increase output, then crop yield may improve, but resource misallocation occurs
Solution Approach 1:
The system prevents resource misallocation by providing location-specific recommendations that match input application to actual field conditions. It identifies zones with different nutrient needs, soil characteristics, and yield potentials, ensuring that inputs are applied precisely where they will be most effective, thereby eliminating waste and misallocation.
Solution Approach 2:
The system uses feedback from tissue samples, soil tests, and environmental monitoring to continuously refine input recommendations. By analyzing actual crop response and field conditions, the system adjusts recommendations to optimize resource use efficiency, preventing both under-application and over-application of inputs.
3Ease of operation
If traditional crop management methods are used, then操作简单性 is maintained, but growers are not aware of factors limiting crop output
Solution Approach 1:
The system acts as an intermediary between complex field data and the grower, automatically analyzing soil tests, tissue samples, weather data, and field history to identify limiting factors. It translates complex agricultural science into simple, actionable recommendations that maintain ease of operation while eliminating information loss about yield-limiting factors.
Solution Approach 2:
The system enables self-service by providing automated analysis and recommendations that empower growers to make informed decisions without requiring expert knowledge. It handles the complexity of data interpretation and presents clear guidance, maintaining operational simplicity while improving awareness of limiting factors through automated information synthesis.
Data Source
AI summary
Embodiments provided herein include systems and methods for controlling crop management equipment. One embodiment includes providing a yield goal for a field from a grower, receiving tissue samples for at least one crop in the field and a seed population for the field, and interpolating individual field grid point values of the field. Some embodiments include predicting a growth plan of water and nutrient applications to meet the yield goal, based on determined sufficiency levels and individual field grid point values of the field, predicting whether the growth plan includes a component that is unlikely to be met, and in response to predicting that the growth plan includes the component that is unlikely to be met, providing the component that is unlikely to be met to a user. Some embodiments include providing an option for the grower to alter the yield goal such that the component is likely to be met.