Machine Vision Crop Anomaly Detection via Two-Stage Imaging
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Solution Overview
Problem
Existing plant monitoring systems rely on manual observation, which is time-consuming and prone to human error, and struggle to efficiently and accurately identify anomalies in larger farm areas, especially in detecting early signs of developmental issues.
Innovation Solution
A method and system utilizing machine vision to analyze multimedia content elements of crops, comparing captured images against normal development data to identify deviations, with the option to verify anomalies using different capturing parameters for clearer views.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation is used to monitor crop development, then detailed inspection can be performed, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical observation with an automated machine vision system that captures images of crops and uses image processing algorithms to detect developmental anomalies. The system automatically analyzes crop characteristics such as leaf color, plant height, and fruit development without requiring human physical inspection, thereby eliminating time-consuming manual labor while maintaining detection accuracy.
2Area of stationary object
If wide-angle aerial imaging is used to cover large farm areas, then broad monitoring coverage is achieved, but early signs of anomalies are difficult to detect
Solution Approach 1:
The patent implements a two-stage imaging approach that segments the monitoring process into broad survey and detailed inspection phases. First, wide-angle aerial images provide overall coverage of large farm areas. When anomalies are suspected in specific zones, the system then captures targeted close-up images of those specific areas for detailed analysis. This segmentation allows the system to maintain both large coverage area and high detection precision for early anomaly signs.
Solution Approach 2:
The patent transitions between different spatial dimensions by switching from aerial wide-angle views (higher dimension, broader coverage) to ground-level or close-up detailed views (lower dimension, higher precision). This dimensional switching enables the system to efficiently cover large areas while still detecting subtle early signs of developmental anomalies that require close inspection.
3Reliability
If comprehensive image analysis is performed on all captured images, then thorough anomaly detection is achieved, but computing resources are excessively consumed
Solution Approach 1:
The patent applies partial action by performing comprehensive image analysis only on selected images that contain suspected anomalies rather than analyzing every captured image. The system uses preliminary processing to identify regions of interest or suspicious patterns, then applies full analytical algorithms only to those specific cases. This approach maintains high anomaly identification reliability while significantly reducing computing resource consumption compared to exhaustive analysis of all images.
Solution Approach 2:
The patent implements local quality by applying different levels of analysis intensity to different regions or images based on their suspiciousness. High-computation detailed analysis is applied locally only to images showing potential anomalies, while routine or minimal processing is applied to normal-appearing images. This localized approach ensures reliable anomaly detection where needed while conserving computing resources in areas where anomalies are unlikely.
Data Source
AI summary
A system and method for identifying developmental anomalies. The method includes obtaining a first set of at least one multimedia content element showing at least one crop and captured using a first set of at least one capturing parameter; obtaining normal development data for the at least one crop, wherein the normal development data represents at least one normal development characteristic of the at least one crop; analyzing, via machine vision, the first set of at least one multimedia content element to identify a first set of at least one characteristic of the at least one crop; determining, based on the first set of at least one characteristic and the normal development data, whether a suspected anomaly is identified; and verifying if the suspected anomaly is an anomaly using a second set of at least one multimedia content element captured using a second set of at least one capturing parameter.