AI Crop Attribute Prediction via Intelligent Sampling
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Solution Overview
Problem
Crop attributes vary significantly due to site-specific conditions and cropping systems, making it challenging to predict and achieve specific attributes in plant growth, such as improved taste, nutrition, and sustainability, across different farming operations.
Innovation Solution
An intelligent sampling technique is used to identify optimal genetics, environment, and management practices, combined with AI/ML models to predict the probability of growing crops with desired attributes, score performance, and identify limiting factors, thereby optimizing cropping systems and reducing variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional farming methods are used without AI/ML modeling, then operational simplicity is maintained, but crop attribute prediction accuracy and consistency deteriorate due to site-specific variability
Solution Approach 1:
The system performs preliminary data collection and model training during the planning phase, building predictive models before actual crop growth. This allows the system to pre-identify optimal genetics, environment, and management practices, so that when planting occurs, predictions are already available without requiring complex real-time analysis
Solution Approach 2:
The patent introduces an AI/ML model as an intermediary between raw farming data and decision-making. This model acts as a mediator that processes complex site-specific conditions (soil, weather, topography) and translates them into actionable predictions about crop attributes, simplifying the overall system while improving accuracy
2Measurement precision
If comprehensive data collection from all fields is performed, then prediction accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system applies local quality by collecting and processing data specific to each field's unique characteristics (soil type, topography, microclimate) rather than treating all fields uniformly. This allows the AI/ML model to make accurate predictions using only the relevant local data needed for each specific location, avoiding unnecessary processing of unrelated data from other fields
Solution Approach 2:
The patent segments the farming operation into discrete field-level units, each with its own data collection and prediction process. This segmentation allows parallel processing of multiple fields independently, reducing overall processing time while maintaining comprehensive coverage across all operations
3Stability of the object's composition
If site-specific conditions are not considered, then farming operations become standardized and simpler, but crop attribute variability increases and desired attributes cannot be achieved
Solution Approach 1:
The system changes parameters by using AI/ML models to identify and adjust multiple cropping system parameters (genetics, plant population, fertility management, weed control) based on site-specific conditions. This allows the system to adapt to local variations while maintaining consistent desired attributes through coordinated parameter optimization
Solution Approach 2:
The patent implements dynamics by making the farming system adaptive rather than static. The AI/ML model continuously learns from data and adjusts recommendations based on changing site-specific conditions, allowing the system to maintain crop attribute consistency despite environmental variations across different fields and seasons
Data Source
AI summary
The present invention identifies the optimal genetics, environment, and management practices and predicts the probability of growing a crop with the desired attributes, quantifies the attribute, scores relative performance, and identifies actions management can take to increase probability of growing plants with specific attributes. The present invention uses an improved technique of data acquisition known as intelligent sampling. Intelligent sampling functions by identifying a minimal dataset that is used to train the model disclosed herein while still achieving acceptable accuracy.