Agricultural Image Processing for Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing agricultural working machines lack effective systems for reliably and quickly detecting and identifying anomalies, obstacles, and irregularities in the crop, which requires constant driver attention and does not allow for autonomous or semi-autonomous operation due to the inability of current GPS and topographic map-based systems to detect new obstacles or crop irregularities.
Innovation Solution
An agricultural working machine equipped with an image processing system that preprocesses, segments, and classifies surrounding images using a combination of optical, radar, and laser sensors to identify anomalies, allowing for autonomous or semi-autonomous control and reducing the need for manual driver intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If GPS or topographic map-based automatic route guidance is used, then the effort required to control the agricultural working machine is reduced, but unknown new obstacles or current irregularities in the crop cannot be detected
Solution Approach 1:
The patent combines GPS-based automatic route guidance with local image capture and evaluation systems. The control unit integrates data from both the global positioning system and local cameras to provide comprehensive guidance that includes both pre-planned routes and real-time obstacle detection, merging two previously separate functions into a unified system.
Solution Approach 2:
The control unit acts as an intermediary that processes information from both the GPS system and local image capture devices. It correlates the global position data with local image data to identify obstacles and irregularities, then provides integrated guidance recommendations to the driver, mediating between global navigation and local situation awareness.
2Reliability
If manual control with constant driver attention is used, then obstacles and irregularities can be detected, but the driver's constant attention is required which reduces productivity
Solution Approach 1:
The system enables the agricultural working machine to monitor its own environment automatically through integrated image capture devices and control units. The machine captures images, processes them through image evaluation methods, and generates guidance information without requiring continuous manual intervention, allowing the system to serve itself in terms of environmental monitoring.
Solution Approach 2:
The patent replaces the mechanical system of constant human visual monitoring with an automated image-based detection system. Cameras and image evaluation algorithms substitute for the driver's constant visual attention, automatically detecting obstacles and crop irregularities while allowing the driver to focus on operating the machine.
3Reliability
If image processing systems are added to detect anomalies, then obstacle detection capability is improved, but the device complexity increases
Solution Approach 1:
The control unit is designed to perform multiple functions: it processes GPS data, captures and evaluates images from multiple cameras, generates guidance information, and provides warnings to the driver. This multi-functional approach consolidates what could be separate complex systems into a single integrated unit, reducing overall system complexity while maintaining comprehensive anomaly detection capability.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Agricultural working machine (2) comprising an image processing system (1), a control and regulation unit (23), a data output unit (21) and at least one image generation system (3), wherein the image processing system processes a selection of the environmental images (7) in an image analysis unit (17), wherein the image generation system (3) is configured to generate environmental images (7) and to transfer the environmental images (7) to the image processing system (1), and the image processing system (1) is configured to preprocess the transferred environmental images (12) in a first step (S1), to segment the preprocessed environmental images (13) in a further step (S2), to classify the resulting segments (16) in a subsequent step (S3) so that anomalies (19) are identifiable, and to generate a further processable image data set (20) in a subsequent step (S4),and the control unit (23) is set up to control the agricultural machinery using the processable image data set (20).