new method for rapid and non-destructive identification of rice varieties using near-infrared spectroscopy technology was proposed. A near-infrared spectrometer was used to obtain the spectral absorption characteristic curves of three kinds of rice, a genetic algorithm was used to extract 15 characteristic wavelengths, and the partial least squares method was used to conduct pattern feature analysis on the 15 characteristic wavelengths; the principal components were extracted through the interactive verification method to complete the features After extraction, the seven principal components were used as input variables of the neural network, and a three-layer error back propagation (BP) neural network was established. The results show that when 30 unknown samples are predicted, the relative prediction deviations are all <5%, and the accuracy of the prediction results reaches 100%; the genetic algorithm combined with the partial least squares method for clustering is better than the partial least squares method alone for a large amount of original spectral data. The effect of cluster analysis is good, and the accuracy of BP neural network prediction is also greatly improved. This method can quickly and non-destructively detect rice varieties, and also provides a new method for the identification of other organic varieties.

Introduction to Innovative Rice Variety Identification

In recent advancements in agricultural technology, a pioneering approach for the swift and non-destructive identification of rice varieties has been introduced, leveraging the capabilities of near-infrared (NIR) spectroscopy. This novel method represents a significant leap forward in enhancing the accuracy and efficiency of rice variety discrimination, a critical aspect in the agricultural sector for ensuring crop quality and authenticity.

Utilizing Near-Infrared Spectroscopy for Rice Analysis

The cornerstone of this innovative technique is the employment of a near-infrared spectrometer, a tool that facilitates the acquisition of spectral absorption characteristic curves for three distinct rice varieties. Through this process, a detailed spectral signature of each rice type is obtained, providing a unique dataset for further analysis.

Genetic Algorithm: Extracting Characteristic Wavelengths

To refine the dataset and highlight the most significant spectral features, a genetic algorithm was employed. This algorithm meticulously analyzed the spectral data to extract 15 characteristic wavelengths. These selected wavelengths were then subjected to a pattern feature analysis using the partial least squares (PLS) method. This analytical phase is crucial for distilling the complex spectral data into a more manageable form for classification purposes.

Pattern Feature Analysis and Principal Component Extraction

The analysis did not stop there. To further enhance the accuracy of the rice variety identification, the principal components were extracted from the distilled features using an interactive verification method. This step is instrumental in reducing the dimensionality of the dataset while retaining the most relevant information for classification.

Implementing a Neural Network for Rice Variety Prediction

Building upon this refined dataset, the seven principal components were utilized as input variables for a neural network. Specifically, a three-layer error back propagation (BP) neural network was established. This computational model is adept at recognizing patterns and making predictions based on the input variables derived from the spectral analysis.

Results: High Accuracy and Predictive Reliability

The efficacy of this method was rigorously tested, with the results showcasing its remarkable predictive capabilities. When applied to predict 30 unknown samples, the method demonstrated relative prediction deviations of less than 5%, achieving an astonishing 100% accuracy in the prediction results. This level of precision underscores the method’s robustness and reliability in identifying rice varieties.

Furthermore, the integration of the genetic algorithm with the partial least squares method for clustering analysis proved to be superior to using the PLS method alone. This synergy between the two analytical techniques enhances the effectiveness of clustering analysis on a large set of original spectral data, thereby improving the accuracy of the BP neural network’s predictions.

Comparative Advantages of Combined Analytical Methods

This breakthrough method not only offers a rapid and non-destructive means for detecting rice varieties but also opens new avenues for the identification of other organic varieties. Its application has the potential to revolutionize the agricultural sector by providing a reliable, efficient, and cost-effective tool for variety identification, thereby ensuring the quality and authenticity of agricultural products.

Conclusion: A New Horizon in Agricultural Technology

In conclusion, the introduction of near-infrared spectroscopy technology, combined with advanced analytical methods such as genetic algorithms, partial least squares method, and neural networks, offers a promising new direction for the agricultural industry. This approach not only enhances the accuracy and efficiency of crop variety identification but also paves the way for similar applications in other organic varieties, marking a significant advancement in agricultural technology.

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Original research was done by Lin Ping, Chen Yongming

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