Machine Vision Crop Detection System for Automated Data Collection
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Solution Overview
Problem
Current methods for collecting crop characteristics, such as corn ear information, are labor-intensive, time-consuming, and prone to human error, lacking efficiency and accuracy, which hinders seed breeding decisions and agricultural operations.
Innovation Solution
A machine vision system mounted on a mobile machine with sensors and cameras captures crop characteristics from multiple perspectives, processing data to predict plant parameters and adjust equipment for optimal data collection and agricultural operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data collection by individuals is used, then the system is simple to implement, but it is labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical data collection with an automated machine vision system using cameras, sensors, and computer processing to detect and record crop characteristics, thereby eliminating labor-intensive manual writing and input operations while significantly improving data collection efficiency
Solution Approach 2:
The system enables self-service data collection where the machine vision system automatically captures, processes, and records crop information without human intervention in the data gathering process, allowing the system to serve itself in collecting agricultural data
2Reliability
If manual data collection is used, then the system is easy to operate, but it is prone to human error
Solution Approach 1:
The patent replaces human manual data recording with automated optical sensing and computer processing systems that objectively capture crop characteristics, eliminating human error in data transcription and measurement while maintaining operational simplicity through automated processing
Solution Approach 2:
The system incorporates feedback mechanisms where the computer processes captured images and sensor data to verify detection accuracy, validate crop characteristic measurements, and ensure data quality standards are met before recording, thereby improving reliability through systematic verification
3Adaptability or versatility
If single-perspective camera is used, then the device complexity is low, but it cannot capture crop characteristics under multiple conditions
Solution Approach 1:
The patent divides the sensing function into multiple independent cameras and sensors positioned at different locations and angles, each capturing specific crop characteristics from its perspective, allowing the system to handle diverse conditions while keeping individual sensor units relatively simple
Solution Approach 2:
The system employs multiple cameras and sensors that serve universal functions of detecting various crop characteristics (presence, size, location, orientation) under different conditions (lighting, obscuration, orientation variations), making the system adaptable to diverse agricultural scenarios
4Productivity
If automated machine vision system is implemented, then data collection efficiency improves, but energy consumption increases
Solution Approach 1:
The system activates cameras and sensors selectively based on detection needs and environmental conditions, processing only necessary data rather than continuously operating all components, thereby achieving efficient data collection while managing energy consumption through targeted activation
Data Source
AI summary
A crop detection system and method of using the same includes a machine vision system mounted to a mobile vehicle. The machine vision system includes an information capturing device connected to a computer having a processor and memory. The memory includes stored crop and field information. Positioning members are mounted to an extend forward of the mobile structure. The information capturing device includes a camera, a sensor, a transceiver and/or a stereo sensor configuration and is positioned to sense the presence, size, location and orientation of characteristics of a crop.