Agricultural Harvesting Loss Detection with Rearward Field Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Agricultural machines experience harvesting losses due to inefficiencies in detecting and categorizing grain loss during operations, leading to reduced yield and economic impact.
Innovation Solution
Equipping agricultural machines with image sensors and controllers to capture and analyze external fields of view, adjusting operational characteristics based on grain presence to minimize loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional harvesting methods are used without image sensors, then the agricultural machine operates with simpler equipment, but harvesting loss detection precision deteriorates
Solution Approach 1:
The patent replaces traditional mechanical loss detection methods with an optical imaging system. Image sensors capture images of the harvesting area, and a processor analyzes these images to detect grain loss, substituting mechanical detection with optical and computational methods to achieve higher precision without significant mechanical complexity increase
Solution Approach 2:
The patent introduces image sensors as an intermediary between the harvesting operation and the detection system. The sensors capture visual data of the field and grain loss, serving as a mediator that translates physical grain loss into detectable image signals for analysis
2Measurement precision
If image sensors are added to detect grain loss, then harvesting loss detection precision improves, but device complexity increases
Solution Approach 1:
The image sensor system is designed to perform multiple functions: detecting grain loss, monitoring harvesting operation effectiveness, and providing data for real-time operational adjustments. This multi-functionality justifies the added complexity by delivering comprehensive monitoring capabilities beyond simple loss detection
Solution Approach 2:
The system establishes a feedback loop where image sensors continuously monitor grain loss, the processor analyzes the data, and the controller adjusts harvesting operations in real-time based on the analysis. This closed-loop feedback system optimizes harvesting efficiency and minimizes loss through dynamic adjustments
3Productivity
If real-time image analysis is implemented, then harvesting loss reduction improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by continuously capturing and pre-processing images during harvesting operations. The processor prepares and analyzes image data in advance, identifying grain loss patterns before they significantly impact overall harvesting efficiency, enabling proactive adjustments
Solution Approach 2:
The image capture and analysis process operates continuously throughout the harvesting operation, with no interruption to the harvesting workflow. The system maintains continuous monitoring and real-time adjustment capabilities, ensuring that grain loss detection and operational optimization occur without stopping the harvesting process
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
Enhances grain detection and reduces harvesting losses by optimizing machine operations based on real-time imaging data, improving yield and efficiency.
Implementation Method 1
at least one image sensor configured to capture one or more images of a field of view
Implementation Method 2
at least one light emitting device configured to emit light into the field of view
Data Source
AI summary
An agricultural machine for reducing harvesting loss during a harvesting operation includes: front ground engaging mechanisms coupled to a front axle, a chassis supported above the ground by the front ground engaging mechanisms, a cutting head located forward of the front ground engaging mechanisms and configured to harvest crop in a worksite, an image sensor, and a controller operatively coupled to the image sensor. The image sensor captures images of a field of view, which includes an area rearward of the front axle. The controller receives data corresponding to the images from the image sensor, determines the amount of grain shown in the images, and adjusts an operational characteristic of the agricultural machine based on the amount of grain determined to be shown in the images.