通过两阶段训练和图像对齐提升稻米分类准确率。
An Improved Pure Fully Connected Neural Network for Rice Grain Classification
- 采用两阶段训练与图像方向校正改进全连接网络
- 模型准确率从97%提升至99%
- 适合农业图像分类与轻量级模型应用
稻米是全球众多人口的主食，提供必需营养并广泛用于各类烹饪。近年来，深度学习推动了稻米自动分类，提升了准确率与效率。然而，基于单阶段训练的经典模型在区分外观相似稻米品种时仍易出错。为此，本文选取并逐步优化纯全连接神经网络以实现稻米粒分类。数据集包含来自网站与实验室的国内外稻米图像。首先，将训练模式由单阶段改为两阶段，显著增强对相似稻米类型的区分能力；其次，将预处理方式由随机倾斜调整为水平或垂直方向校正。经过这两项改进，模型准确率从97%显著提升至99%。结果表明，两项细微优化可明显增强深度学习模型在稻米分类任务中的性能。
原文摘要 · Abstract (English)
Rice is a staple food for a significant portion of the world's population, providing essential nutrients and serving as a versatile in-gredient in a wide range of culinary traditions. Recently, the use of deep learning has enabled automated classification of rice, im-proving accuracy and efficiency. However, classical models based on first-stage training may face difficulties in distinguishing between rice varieties with similar external characteristics, thus leading to misclassifications. Considering the transparency and feasibility of model, we selected and gradually improved pure fully connected neural network to achieve classification of rice grain. The dataset we used contains both global and domestic rice images obtained from websites and laboratories respectively. First, the training mode was changed from one-stage training to two-stage training, which significantly contributes to distinguishing two similar types of rice. Secondly, the preprocessing method was changed from random tilting to horizontal or vertical position cor-rection. After those two enhancements, the accuracy of our model increased notably from 97% to 99%. In summary, two subtle methods proposed in this study can remarkably enhance the classification ability of deep learning models in terms of the classification of rice grain.
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