Crop Scouting Image Annotation for Accurate Plant Threat Detection

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Solution Overview

Problem

Agricultural farming relies heavily on manual labor and human observation, which is time-consuming and prone to errors, particularly in managing crop growth cycles, environmental impacts, and disease prevention.

Innovation Solution

Implementing automated systems with machine learning to collect, process, and analyze data on plant and environmental conditions, generating contextual plant images, and training machine learning models to identify plant abnormalities, such as diseases and pests, using a mobile sensing unit and computer-implemented methods.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If automated systems with machine learning are implemented to collect and analyze plant data, then measurement precision and reliability are improved, but device complexity increases

Engineering Contradiction:

Improveplant condition detection accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system divides plant monitoring into multiple specialized sensing units, each capturing specific data types (spectral images, thermal images, environmental data). Machine learning models are segmented into specialized components for different analysis tasks (disease detection, pest identification, growth prediction), allowing each component to be optimized independently while maintaining overall system precision.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A centralized data processing platform acts as an intermediary between diverse sensing units and user interfaces. This platform consolidates complex machine learning operations, data fusion, and analysis tasks, shielding end users from system complexity while delivering high-precision plant condition assessments through simplified interfaces.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of information

If comprehensive data collection and analysis systems are deployed, then information completeness is improved, but loss of time in data processing increases

Engineering Contradiction:

Improveplant data completenessVSAvoiddata processing time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system performs preliminary data processing, filtering, and feature extraction at the edge computing level within sensing units themselves. Data is pre-processed and annotated with key features before transmission to central systems, reducing the volume of raw data requiring comprehensive analysis and enabling faster processing of complete plant information.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous monitoring and incremental data collection rather than periodic comprehensive surveys. Machine learning models continuously process incoming data streams, maintaining up-to-date plant condition assessments without requiring complete re-analysis of all historical data, thus preserving information completeness while minimizing processing time delays.

Inventive Principle:

Principle #20Continuity of useful action

3Ease of operation

If manual observation methods are used for crop management, then ease of operation is maintained, but productivity and reliability deteriorate

Engineering Contradiction:

Improvesystem usabilityVSAvoidcrop management efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system enables growers to easily query plant condition data and receive automated recommendations through simple interfaces. Machine learning models automatically analyze complex plant data and generate actionable insights without requiring users to manually process information, maintaining ease of operation while dramatically improving productivity through automated decision support.

Inventive Principle:

Principle #25Self-service

4Reliability

If experienced growers perform manual observation, then reliability of plant assessment is improved, but loss of time and productivity worsen

Engineering Contradiction:

Improveplant condition assessment accuracyVSAvoidtime for crop monitoring

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system replaces manual observation by experienced growers with automated sensing units and machine learning algorithms. Multiple spectral sensors, thermal cameras, and environmental monitors objectively measure plant conditions without human intervention, maintaining or exceeding the reliability of expert assessment while eliminating the time required for manual field inspections.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12406357B2Crop scouting information systems and resource management

Publication Date: 2025.09.02 ADAVIV

AI summary

Described herein are techniques for generating contextually rich plant images. A number of data captures of raw plant data are generated via a sensing unit configured to navigate a growing facility. Metadata is generated and assigned to the raw plant data including at least one of: plant location, timestamp, plant identification, plant strain, facility identification, facility location, facility type, health risk factors, plant conditions, and human observations. Images generated by the sensing unit are analyzed and pixel annotations are generated in the images based on their relation to one or more plant well-being features. Data tags are generated and assigned the data captures based on an analysis of the data captures. The data tags are text phrases linking a particular data capture to a specific threat to plant well-being.