Cloud Framework for Imaging Data Processing in Precision Agriculture
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Solution Overview
Problem
Current precision agriculture technologies face challenges in efficiently processing and analyzing imaging data for tree crops, particularly due to labor-intensive manual methods and limited practical information from commercially available software, which hinders timely and accurate assessments of crop growth and orchard management.
Innovation Solution
A cloud-based framework that utilizes CPU and GPU-focused instances to stitch and process imaging data, applying object detection algorithms to identify objects such as trees, and generate actionable insights like tree counts, health, and nutrient estimations, facilitating efficient data processing and scalable analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sampling methods are used for field phenotypes, then measurement precision can be maintained, but productivity is significantly reduced due to labor-intensive and time-consuming processes
Solution Approach 1:
The patent replaces manual mechanical sampling with automated imaging systems (UAVs, satellites, ground devices) equipped with sensors (RGB, multispectral, hyperspectral, LiDAR) to capture field phenotype data, thereby maintaining measurement precision while dramatically improving productivity
Solution Approach 2:
The patent creates digital copies (images and data) of field phenotypes through remote sensing technologies, allowing multiple analyses and measurements to be performed on these copies without additional physical sampling effort, thus maintaining precision while enhancing productivity
2Productivity
If remote sensing techniques are deployed for large-area monitoring, then productivity is improved through automated data collection, but device complexity increases due to multiple sensing platforms and data processing requirements
Solution Approach 1:
The patent employs multiple sensing platforms (UAVs, satellites, ground devices) with various sensor types (RGB, multispectral, hyperspectral, LiDAR) that can be selectively deployed based on specific application needs, allowing a universal remote sensing framework to address diverse agricultural monitoring requirements while managing complexity through modular selection
Solution Approach 2:
The patent segments the complex remote sensing system into independent modular components (different platforms, sensor types, processing algorithms) that can be selected and combined based on specific monitoring needs, thereby managing device complexity while maintaining high productivity
3Measurement precision
If artificial intelligence is applied to process large amounts of image data, then measurement precision and object detection accuracy are improved, but data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary data processing steps (image stitching to create orthomosaics, preprocessing) before applying complex AI object detection algorithms, thereby reducing the complexity and processing time of subsequent AI analysis while maintaining detection accuracy
Solution Approach 2:
The patent segments the data processing workflow into distinct stages (image stitching, preprocessing, object detection, parameter extraction) that can be executed sequentially or in parallel, allowing optimization of computational resources and reducing overall processing time while maintaining measurement precision
4Ease of operation
If commercially available software is used for image analysis, then ease of operation is improved, but the extent of automation and analytical capability are limited due to restricted practical information provided
Solution Approach 1:
The patent provides a universal automated analysis framework that can perform multiple functions (object detection, parameter extraction, data visualization) beyond the capabilities of commercial software, while maintaining ease of operation through a user-friendly interface that returns actionable agricultural insights
Data Source
AI summary
Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for detecting objects located in an area of interest. In accordance with one embodiment, a method is provided comprising: receiving, via an interface provided through a general instance on a cloud environment, imaging data comprising raw images collected on the area of interest; upon receiving the images: activating a central processing unit (CPU) focused instance on the cloud environment and processing, via the image, the raw images to generate an image map of the area of interest; and after generating the image map: activating a graphical processing unit (GPU) focused instance on the cloud environment and performing object detection, via the image, on a region within the image map by applying one or more object detection algorithms to the region to identify locations of the objects in the region.