Nitrogen Detection via Spectral Imaging and Adaptive Model Selection
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Solution Overview
Problem
Current nitrogen content detection methods are either destructive, costly, and inefficient or nondestructive with low accuracy, making it difficult to quickly and accurately assess nitrogen levels in crops.
Innovation Solution
A method and system that acquire spectral images of crops, select an optimal nitrogen content prediction model based on crop variety and growth stage, and use hyperspectral vegetation indices and sensitive bands to predict nitrogen content using machine learning models, ensuring nondestructive and accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional destructive detection methods (soil testing, chemical analysis) are used, then nitrogen content can be detected, but the operation is complicated, cost is high, efficiency is low, and plant tissues are damaged
Solution Approach 1:
The patent replaces traditional mechanical/chemical destructive detection methods with a nondestructive optical detection system using spectral imaging and machine learning algorithms. The system captures spectral images of crop canopies and uses trained models to predict nitrogen content, eliminating the need for physical tissue sampling and chemical analysis while maintaining high detection efficiency and accuracy.
2Productivity
If nondestructive detection methods (image processing, machine vision, chlorophyll photometry) are used, then plant tissues are not damaged, but measurement accuracy is low
Solution Approach 1:
The patent transforms the detection approach by changing from simple visual parameters to comprehensive spectral parameters across multiple wavelengths. The system captures and analyzes spectral reflectance data across the visible and near-infrared ranges, extracting multiple spectral indices and features that correlate with nitrogen content, thereby significantly improving measurement accuracy while maintaining nondestructive detection efficiency.
Solution Approach 2:
The patent implements preliminary action by pre-training multiple nitrogen content prediction models using spectral images and corresponding actual nitrogen content data from different crop varieties and growth stages. These pre-trained models are stored in a model base and selected based on the specific crop type and growth stage, enabling accurate predictions without requiring real-time complex calculations, thus improving both accuracy and efficiency.
3Device complexity
If a single prediction model is used for all crops, then the system is simple, but accuracy decreases for specific crop varieties and growth stages
Solution Approach 1:
The patent applies local quality by creating specialized prediction models tailored to specific crop varieties and growth stages rather than using a universal model. The system trains separate models for different rice varieties (e.g., Zhua 38, Liang 88) and growth stages (e.g., tillering, jointing, heading), allowing each model to optimize for its specific target, thereby significantly improving prediction accuracy for each local condition.
Solution Approach 2:
The patent implements dynamics by making the model selection adaptive based on the detected crop variety and growth stage. The system dynamically selects the most appropriate pre-trained model from the model base according to the specific application scenario, enabling the system to adapt to different crops and growth conditions without requiring manual reconfiguration, thus balancing complexity and accuracy.
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
Enables quick and accurate detection of nitrogen content in crops, improving efficiency and reducing damage, with the system selecting the best performing model among hyperspectral, sensitive band, and machine learning models for precise predictions.
Implementation Method 1
acquiring a spectral image of a target crop
Data Source
AI summary
A method and a system for automatically detecting nitrogen content, an electronic device and a medium are provided and relate to the field of nitrogen content detection. The method includes: selecting an optimal nitrogen content prediction model from a prediction model base according to a variety and a growth stage of a target crop to obtain a target model; detecting, based on a spectral image of the target crop, a nitrogen content of the target crop by using the target model to obtain a predicted value of the nitrogen content. The optimal nitrogen content prediction model is the model with best performance among a first nitrogen content prediction model constructed based on a hyperspectral vegetation index, a second nitrogen content prediction model constructed based on a sensitive band, and a third nitrogen content prediction model constructed based on a machine learning method.