DEEP CONVOLUTIONAL NEURAL NETWORK BASED HIGH-THROUGHPUT METHOD FOR DETERMINING ALKALI SPREADING VALUE OF RICE GRAIN

摘要

The present invention relates to a deep convolutional neural network based high-throughput method for determining an alkali spreading value of a rice grain, including performing an alkaline reaction of rice grains through a single-grain-single-grid, multi-split reaction plate, performing high-throughput collection; after image processing, performing feature extraction and classification using a CNN-based convolutional neural network image classifier, and carrying out training under specific conditions; based on model parameters obtained by deep learning of the data training set, performing machine recognition on the images of the test samples to obtain a level of alkali spreading value. Through the determination method of the present invention, detection error caused by manual measurement is reduced, and the specific reaction plate is used for testing, which can ensure that the rice grains may not drift during the test process, thereby improving the clarity of later observations, and increasing the accuracy of detection. Moreover, the assessment result is no longer directly related to the operator's personal understanding, work experience, personal status and the like, which reduces the difficulty of detection, and the test results are more accurate and representative.