Field-Level Harvest Timing Using Crop Moisture Prediction
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Solution Overview
Problem
Current agricultural models lack the ability to accurately diagnose and predict field-level weather conditions, leading to inefficiencies in harvest operations due to limited geographic representativeness and insufficient integration of weather analysis, which affects crop moisture, temperature, and soil conditions, resulting in reduced crop revenue and logistical challenges.
Innovation Solution
A system and method that applies real-time, field-level weather simulation and prediction to precision agriculture models, combining location-tagged data communication and user feedback to generate harvest advisory outputs, utilizing physical, empirical, and artificial intelligence models to analyze crops, plants, and soils, and predict optimal harvest times and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time field-level weather simulation and prediction is applied to precision agriculture models, then harvest decision accuracy is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments weather data collection and modeling into field-level specific models rather than using uniform regional models. Each field has its own microclimate model that processes local weather data, soil characteristics, and crop-specific parameters separately, enabling precise harvest timing decisions for individual fields while managing computational complexity through modular processing.
Solution Approach 2:
The system adds the temporal dimension by implementing continuous real-time weather simulation and prediction capabilities. Instead of static weather data, the system models weather conditions across multiple time points to predict future field conditions, enabling proactive harvest scheduling that accounts for evolving weather patterns and their impact on crop moisture content.
2Measurement precision
If in-situ monitoring of field conditions is performed regularly, then harvest timing accuracy is improved, but equipment and labor resources increase
Solution Approach 1:
The system implements self-service monitoring by deploying automated field sensors that continuously measure soil moisture, air temperature, humidity, and other relevant parameters without requiring manual intervention. These automated monitoring systems eliminate the need for frequent manual field visits while providing continuous real-time data for harvest decision-making.
Solution Approach 2:
The system replaces manual mechanical monitoring methods with electronic and computational systems. Instead of physically measuring field conditions through manual sampling and observation, the system uses electronic sensors, wireless communication networks, and computational models to automatically track and predict field conditions, significantly reducing labor requirements.
3Stability of the object's composition
If harvest operations are delayed to achieve optimal moisture levels, then product storage stability is improved, but crop revenue decreases due to overly dry conditions and weight loss
Solution Approach 1:
The system performs preliminary weather simulation and prediction to forecast future field conditions before harvest operations begin. By modeling how weather patterns will affect crop moisture content over time, the system can identify the optimal harvest window in advance, allowing farmers to schedule harvest operations that achieve target moisture levels without excessive delays that would cause over-drying and weight loss.
Solution Approach 2:
The system implements continuous feedback loops that monitor actual field conditions against predicted conditions and adjust harvest recommendations accordingly. Real-time measurements of crop moisture content, soil conditions, and weather parameters feed back into the models to refine predictions and provide dynamic harvest timing guidance, ensuring optimal moisture levels are achieved without unnecessary delays.
Data Source
AI summary
A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analysis.