Automated Plant Analysis Using Spectral Classification
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Solution Overview
Problem
Current methods for evaluating plant growth conditions, particularly in cannabis cultivation, are time-consuming, costly, and inaccurate, often failing to detect diseases and stressors early enough to prevent quality and quantity losses.
Innovation Solution
An automated optical method using an illumination unit, sensor unit, and evaluation unit with a data-based classifier that acquires and analyzes spectral information from plants to determine properties such as health, water content, and cannabinoid levels, enabling early detection of issues and predictive analysis for future growth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis and evaluation of plant properties is performed by human eye, then no complex equipment is needed, but the process is time-consuming, costly and inaccurate
Solution Approach 1:
The patent replaces the manual visual inspection system with an automated optical measurement system comprising illumination units, sensors, and a control unit that performs automated image analysis and spectral measurement. This substitution eliminates human eye limitations and provides objective, precise, and rapid plant property evaluation.
Solution Approach 2:
The system enables self-service automation where the plant analysis process is performed automatically by the device without human intervention. The control unit autonomously processes images and spectral data to determine plant properties, eliminating the need for manual analysis while maintaining high accuracy and reducing time consumption.
2Reliability
If manual analysis is used to assess plant health and disease infestation, then simple equipment is required, but disease detection is often visible only very late
Solution Approach 1:
The system performs preliminary detection of disease infestation and plant stress by analyzing spectral data and image features before visible symptoms appear to the human eye. The automated analysis identifies subtle changes in plant reflectance properties that indicate early-stage diseases, enabling timely intervention to prevent yield loss.
Solution Approach 2:
The patent introduces spectral data and automated image analysis as intermediaries between the plant and the evaluator. These intermediaries capture subtle physiological changes in the plant that are not visible to the human eye, providing an early warning system for disease detection and enabling preventive measures.
3Extent of automation
If automated optical analysis with classifier training is implemented, then measurement precision and automation are improved, but device complexity increases
Solution Approach 1:
The system performs classifier training in advance using a training set of plants with known properties. This preliminary action creates a ready-to-use classification model that can be applied automatically to new plants without requiring complex real-time training procedures, simplifying the operational complexity while maintaining high automation levels.
Solution Approach 2:
The patent implements a two-stage approach where a comprehensive classifier is trained offline using extensive training data, and then a simplified version is deployed for real-time operation. This partial action approach allows the system to achieve high automation and accuracy while keeping the operational device complexity manageable by pre-processing the complex training phase separately.
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
This method provides precise, automated analysis of plant properties, allowing for timely interventions to optimize growth conditions and improve cultivation efficiency by detecting diseases and stressors early, thus enhancing the quality and quantity of harvests.
Implementation Method 1
an illumination unit (12) for illuminating the plant (18) to be examined
Implementation Method 2
acquiring analysis input data by measuring the radiation reflected from the plant to be analyzed
Data Source
AI summary
The disclosure relates to a method for analyzing a plant, in particular for analyzing cannabis, using an illumination unit, a sensor unit, and an analysis unit, said analysis unit having a data-based classifier. The disclosure additionally relates to a device for analyzing a plant, said device comprising an illumination unit for lighting the plant to be analyzed and a sensor unit for receiving analysis input data, wherein the analysis input data contains at least spectral information, in particular an absorption spectrum or a reflection spectrum of the training plant. The device additionally comprises an analysis unit for analyzing the received analysis input data and for determining at least one property of the plant to be analyzed. The analysis unit is also designed to determine at least one property of the plant using a data-based classifier and the previously received analysis input data.