A Spectral Device for Rice Chlorophyll Content Detection with Background Classification Capability

The aim of this study is to enhance the detection accuracy of rice chlorophyll content under complex backgrounds, optimize rice production management models, and improve the efficiency and quality of grain production. To achieve this, a spectral device for rice chlorophyll content detection with integrated background classification capability was developed. The device employed a MobileNetV4-Conv-Small-based model for rice background classification, enabling the categorization of clear, muddy, and green algae-covered backgrounds. The classification results showed that the model performed best under the green algae-covered background, with all performance metrics exceeding 97%. The muddy background followed, with metrics surpassing 94%, while the clear background proved more challenging, though the metrics still exceeded 93%. By combining preprocessing techniques with convolutional neural networks (CNNs), distinct rice chlorophyll content detection models were developed for each background type. For clear backgrounds, the optimal model was FD + CNN, with R², RMSE, and RPD values of 0.975, 5.191, and 6.318, respectively. For muddy backgrounds, the optimal model was SS + CNN, with R², RMSE, and RPD values of 0.627, 18.249, and 1.638, respectively. For green algae-covered backgrounds, the SS + CNN model also achieved the best results, with R², RMSE, and RPD values of 0.719, 16.417, and 1.885, respectively. Field experiments confirmed that the device achieved a rice background classification accuracy of 94.67% and a relative error compliance rate of 84.00% for chlorophyll content detection. These results demonstrate the feasibility of integrating background classification and chlorophyll content detection for rice cultivation.