Crop Sensing Display for Real-Time Harvesting Data
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Solution Overview
Problem
Current crop harvesting machines lack the resolution and sophistication in crop data collection, providing inadequate information for advanced crop management due to limited sensing capabilities, which restricts precise operation and decision-making in real-time.
Innovation Solution
A crop sensing system that includes sensors and a processing unit capable of detecting crop attributes on a row-by-row or plant-by-plant basis, providing enhanced resolution data and enabling adjustments to harvesting operations based on real-time conditions, using sensors like LIDAR, cameras, and strain gauges to derive detailed crop attributes such as yield and mass.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional crop throughput sensors are used to detect ongoing crop yield, then basic harvesting information is obtained, but the data resolution and sophistication are inadequate for advanced crop management
Solution Approach 1:
The harvesting platform is divided into multiple independently controllable row units, each equipped with its own sensors and control systems. This segmentation allows per-row or per-plant crop attribute detection and independent adjustment of harvesting parameters for each row, achieving high-resolution crop data collection without requiring a completely complex monolithic system
Solution Approach 2:
The sensing system is designed to detect multiple crop attributes (yield, mass, moisture content, plant height, etc.) using integrated sensors that can serve multiple functions. The same sensor platform can detect various parameters simultaneously, reducing overall system complexity while providing comprehensive high-resolution crop data
2Loss of information
If detailed per-row or per-plant crop sensing is implemented, then enhanced crop management information is obtained, but the system complexity and cost increase
Solution Approach 1:
Multiple sensing functions are merged into integrated sensor assemblies that can detect various crop attributes simultaneously. The control system merges data from multiple sensors and sources, processing all information through a unified control architecture that manages per-row and per-plant operations, reducing complexity while maintaining comprehensive information collection
Solution Approach 2:
The system incorporates automatic feedback loops where sensors detect crop attributes and the control system automatically adjusts harvesting parameters without manual intervention. The platform self-regulates row unit operations based on real-time sensor data, reducing the need for complex manual control systems while maintaining complete crop attribute monitoring
3Productivity
If real-time crop attribute detection is performed during harvesting, then precise harvesting adjustments are enabled, but the processing time and data handling requirements increase
Solution Approach 1:
The system pre-configures harvesting parameters and sensor thresholds before entering the field. Crop attribute detection and initial data processing occur in real-time during harvesting operations, with pre-programmed response protocols that enable immediate adjustments without extensive post-processing delays, maintaining high harvesting efficiency
Solution Approach 2:
Real-time feedback loops continuously monitor crop attributes and automatically adjust harvesting parameters through integrated control systems. The feedback mechanism processes sensor data instantaneously during operation, enabling precise real-time adjustments without significant time loss, as the system responds automatically rather than requiring batch processing after harvesting
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enhances crop management by providing detailed, real-time data for improved harvesting efficiency, allowing for precise adjustments and better calibration of harvesting equipment, leading to optimized crop yield and reduced operational issues.
Implementation Method 1
using sensors like LIDAR, cameras, and strain gauges to derive detailed crop attributes
Implementation Method 2
using sensors like LIDAR, cameras, and strain gauges to derive detailed crop attributes
Implementation Method 3
using sensors like LIDAR, cameras, and strain gauges to derive detailed crop attributes such as yield and mass
Data Source
AI summary
Signals representing a sensed crop attribute are received for a least one forage plant in at least one row being harvested by one of a plurality of portions of a harvesting width engaged in harvesting. Control signals are generated causing the display to present information for plants harvested by the portion of the harvest width based on the sensed crop attribute.