Thermal Guidance for Agricultural Vehicle Wildlife Avoidance
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Solution Overview
Problem
Agricultural operations during the spring season pose a significant threat to ground-nesting animals like deer fawns and other wildlife, and also risk equipment damage from static obstacles, necessitating a systematic approach to ensure animal safety and operational efficiency.
Innovation Solution
An agricultural vehicle guidance system utilizing thermal cameras and machine learning models to detect heat signatures, generate geofences, and adjust vehicle operations to avoid animals and obstacles, incorporating GNSS and IMU data for precise navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mowing operations are conducted during spring season to maintain grasslands, then agricultural productivity is improved, but wildlife safety deteriorates due to fatal encounters with juvenile animals
Solution Approach 1:
The system performs preliminary detection of animals using thermal imaging cameras before mowing operations begin. The guidance system identifies heat signatures of juvenile animals in advance, generates geofence boundaries around detected animals, and pre-calculates alternative paths to avoid them, allowing mowing to proceed safely without fatal encounters
Solution Approach 2:
The patent introduces an intermediary guidance system that mediates between the mowing operation and wildlife. The system uses thermal cameras to detect animals, processes their locations through a guidance algorithm, and generates modified path instructions that allow continuous mowing while preventing direct contact between equipment and animals
2Object-affected harmful factors
If thermal imaging and machine learning analysis are implemented to detect animals, then wildlife safety is improved, but device complexity increases
Solution Approach 1:
The guidance system performs multiple functions using integrated components: thermal imaging cameras simultaneously detect animals and generate heat signature data, machine learning models classify detected objects as animals or obstacles, and the guidance algorithm generates both animal avoidance paths and obstacle avoidance paths. This multi-functionality reduces the need for separate specialized systems
Solution Approach 2:
The system replaces complex mechanical animal detection methods with thermal imaging technology. Instead of using multiple sensors and complex processing, the patent uses thermal cameras to directly detect heat signatures of animals, which are then processed by machine learning algorithms to identify animal presence and generate avoidance paths
3Object-affected harmful factors
If the agricultural vehicle stops and reverses to avoid animals, then wildlife safety is improved, but productivity decreases due to operational interruptions
Solution Approach 1:
The guidance system dynamically adjusts the vehicle path in real-time based on detected animal locations. Instead of fixed stop-and-reverse maneuvers, the system continuously calculates alternative paths that navigate around animals, allowing the mowing operation to proceed with minimal interruptions while maintaining wildlife safety
Solution Approach 2:
The system pre-calculates alternative paths around detected animals before the vehicle reaches their location. By generating avoidance routes in advance based on thermal detection data, the guidance system allows smooth path transitions without sudden stops or reverses, maintaining productivity while ensuring animal safety
4Object-affected harmful factors
If geofence generation and path modification are implemented to avoid animals, then wildlife safety is improved, but measurement precision requirements increase for accurate animal location determination
Solution Approach 1:
The system applies different processing levels to different detected objects. Thermal imaging data is processed to identify heat signatures, machine learning models classify them as animals or obstacles, and geofence boundaries are generated with appropriate precision based on the specific object type and location. This localized quality adjustment optimizes measurement precision requirements
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
Effectively prevents wildlife casualties and equipment damage by accurately detecting and navigating around animals and obstacles, ensuring safe and efficient agricultural processes.
Implementation Method 1
receive image data from an image sensor, analyze the received image data to identify heat signatures represented in the received image data
Data Source
Figure 1
Figure 2A~2B
Figure 3A~3B
AI summary
A guidance system for controlling operation of an agricultural vehicle includes at least one processor and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the guidance system, during an agricultural process, to receive thermal image data from a thermal camera, receive additional image data from at least one additional image sensor, generate a three-dimensional environmental model utilizing at least the received additional image data, based at least partially on the received thermal image data, identify heat signatures represented in thermal image data, analyze the identified heat signatures to determine a presence and a type of an object, mark an area around the object within a digital map, and adjust operation of the agricultural vehicle when the agricultural vehicle is proximate to the area around the object.