Aerial Harvest Yield Normalization Across Low-Connectivity Fields

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Solution Overview

Problem

Agricultural data transfer and processing are hindered by poor network connectivity in rural areas, leading to unreliable data collection and loss, which affects real-time decision-making and yield accuracy in precision agriculture, especially in regions with limited infrastructure and high data complexity.

Innovation Solution

A data-centric computing platform enabling edge and cloud computing for reliable data transfer and processing, standardizing IoT data ingestion, and simplifying AI/ML inferencing functions, using aerial drones to collect and process data in areas with little to no network connectivity, and enabling two-way communication between data delivery vehicles and compute nodes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If USB sticks are used to store data on farm machinery, then data can be collected offline in areas with poor network connectivity, but data loss occurs due to lost, erased, stolen, or misplaced USB sticks

Engineering Contradiction:

Improvedata collection reliabilityVSAvoiddata loss

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

A data delivery vehicle (drone) acts as an intermediary to collect data from multiple USB sticks in the field, transfer it to a centralized location with network connectivity, and upload it to the cloud. This eliminates the risk of individual USB stick loss while maintaining offline data collection capability.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system creates a backup copy of data by reading from USB sticks, storing it temporarily in the drone's memory, and then uploading to the cloud. This copying process ensures data preservation even if original USB sticks are lost or damaged.

Inventive Principle:

Principle #26Copying

2Reliability

If network infrastructure is expanded to rural areas, then real-time data transfer becomes possible, but infrastructure investment is required in sparsely populated regions

Engineering Contradiction:

Improvenetwork connectivityVSAvoidinfrastructure complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The drone serves as a mobile intermediary that bridges the gap between remote field locations without network coverage and centralized cloud infrastructure. It enables data transfer without requiring physical network expansion to every rural location.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

Instead of expanding horizontal network coverage across vast rural areas, the system uses vertical aerial deployment of drones to deliver data from field locations to centralized processing points, adding a dimensional solution to the connectivity problem.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Productivity

If multiple harvesters are deployed to harvest large land tracts, then harvesting productivity increases, but yield data accuracy decreases due to variations in harvester performance and calibration

Engineering Contradiction:

Improveharvesting productivityVSAvoidyield data accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system merges yield data from multiple harvesters by collecting USB sticks from all machines and processing them through a centralized normalization system. This combines data from multiple sources while applying consistent calibration to ensure accuracy.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The yield normalization system adjusts calibration parameters across data from different harvesters, compensating for variations in sensor performance, cutting widths, and operational conditions to produce standardized, comparable yield data.

Inventive Principle:

Principle #35Parameter changes

4Ease of operation

If yield data is collected without normalization, then data collection is simpler, but yield accuracy decreases due to uncorrected variations in harvester performance and environmental conditions

Engineering Contradiction:

Improvedata collection simplicityVSAvoidyield accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system performs preliminary data collection in simple fashion (just collecting USB sticks), then applies normalization processing afterward. This separates the simple collection phase from the correction phase, maintaining ease of operation while improving accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The normalization process continuously refines yield data by applying corrections for harvester variations, environmental conditions, and calibration differences, transforming raw data into accurate yield information without interrupting the overall data collection workflow.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS11354757B2Systems and methods for aggregating harvest yield data

Publication Date: 2022.06.07 CHIOCCOLI LLC

AI summary

The present invention is for an autonomous aerial vehicle that enables near real-time computation of harvest yield data. Generally, the autonomous aerial vehicle receives combine harvest data from a harvesting vehicle, generates high-resolution yield data based on sensor suite that is on-board the autonomous vehicle, obtains edge compute data from an edge computing device at the edge of the network, and segments the received combine harvest data, the generated high-resolution yield data, and the obtained edge compute data. The aerial vehicle applies data normalization models to the segmented data and computes a normalized harvest yield for at least a portion a land tract. In this manner, the data delivery vehicles computes normalized data that otherwise can by noisy and unreliable.