Autonomous UAV Crop Monitoring with Multi-Spectral Imaging

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Solution Overview

Problem

Agricultural farms, particularly those growing vines, face challenges in managing crops due to thin profit margins, which hinder the adoption of new technologies and efficient management practices, exacerbated by issues like over/under irrigation, diseases, and pest infestations across various varietals.

Innovation Solution

The implementation of a system utilizing autonomous vehicles and terrestrial robots equipped with cameras to capture aerial and terrestrial images in multiple spectrums, analyzing these images for anomalies using machine learning and AI, and generating visual reports to assist in managing crop growth, considering topological and phenological information.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If autonomous vehicles and terrestrial robots with cameras are deployed to monitor crops, then crop health monitoring efficiency is improved, but device complexity and operational costs increase

Engineering Contradiction:

Improvecrop health monitoring efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The autonomous vehicles and terrestrial robots are designed to perform multiple functions: capturing images in multiple spectrums, analyzing crop health data, navigating autonomously, and communicating with the server. This multi-functionality consolidates what could be multiple separate systems into a unified platform, improving monitoring efficiency while managing complexity through integration rather than proliferation of separate devices

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system replaces manual mechanical inspection methods with autonomous vehicles equipped with AI-powered image analysis. The autonomous navigation and automated image processing substitute for human-operated mechanical systems, significantly improving monitoring efficiency while the automation actually reduces the operational complexity of daily tasks

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If multiple spectrum images are captured and analyzed using AI, then detection precision of crop anomalies is improved, but use of energy and computational resources increases

Engineering Contradiction:

Improveanomaly detection precisionVSAvoidenergy consumption for image processing

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by capturing images in multiple spectrums simultaneously during the autonomous vehicle's flight path, rather than sequentially. The images from different spectrums are captured in one pass, and the AI analysis is initiated during transit or upon landing, allowing processing to begin before the vehicle needs to return or recharge, thus managing energy consumption more efficiently

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses multiple spectrums (excessive action) to ensure comprehensive detection of all types of crop anomalies, accepting the higher energy and computational cost as necessary to achieve the required detection precision for different plant stresses that manifest differently across various spectral ranges

Inventive Principle:

Principle #16Partial or excessive action

3Area of stationary object

If autonomous vehicles are used to traverse large vineyard areas, then coverage area is improved, but operation time and loss of time increase

Engineering Contradiction:

Improvevineyard coverage areaVSAvoidtime for complete vineyard inspection

Core Design Contradiction:

Area of stationary objectVSLoss of time

Solution Approach 1:

The autonomous vehicles employ dynamic flight path optimization, adjusting their routing in real-time based on detected anomalies, weather conditions, and battery status. The system dynamically prioritizes areas requiring immediate attention, allowing comprehensive coverage of large areas while minimizing total operation time through adaptive, intelligent routing rather than fixed predetermined paths

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS10779476B2Crop management method and apparatus with autonomous vehicles

Publication Date: 2020.09.22 POLLEN SYSTEMS CORP

AI summary

In some embodiments, a method for managing growing crops in a crop growing farm includes operating one or more unmanned aerial vehicles (UAV) to fly over a plurality of sections of a crop growing farm. The UAVs are fitted with a plurality of cameras equipped to generate images in a plurality of spectrums. The plurality of sections of the crop growing farm grow crops of one or more types or varietals. The method further includes taking a plurality of aerial images of the sections of the vineyard in the plurality of spectrums, using the plurality of cameras, while the UAVs are flying over the plurality of sections of the crop growing farm, and executing an analyzer on a computing system to machine analyze the plurality of aerial images for anomalies associated with growing the crops of the one or more types or varietals. The machine analysis takes into consideration topological information of the crop growing farm, as well as current planting information of the crop growing farm.