Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, ETSI Telecomunicación, Paseo de Belén 15, 47011 Valladolid, Spain.
Agricultural Engineering Department, Federal University of Lavras, P.O. Box 3037, Lavras 37200-000, Brazil.
Sensors (Basel). 2023 Feb 23;23(5):2471. doi: 10.3390/s23052471.
Sunflower seeds, one of the main oilseeds produced around the world, are widely used in the food industry. Mixtures of seed varieties can occur throughout the supply chain. Intermediaries and the food industry need to identify the varieties to produce high-quality products. Considering that high oleic oilseed varieties are similar, a computer-based system to classify varieties could be useful to the food industry. The objective of our study is to examine the capacity of deep learning (DL) algorithms to classify sunflower seeds. An image acquisition system, with controlled lighting and a Nikon camera in a fixed position, was constructed to take photos of 6000 seeds of six sunflower seed varieties. Images were used to create datasets for training, validation, and testing of the system. A CNN AlexNet model was implemented to perform variety classification, specifically classifying from two to six varieties. The classification model reached an accuracy value of 100% for two classes and 89.5% for the six classes. These values can be considered acceptable, because the varieties classified are very similar, and they can hardly be classified with the naked eye. This result proves that DL algorithms can be useful for classifying high oleic sunflower seeds.
葵花籽是世界上主要的油籽之一，广泛应用于食品工业。在整个供应链中都可能出现种子品种的混合。中间商和食品工业需要识别品种以生产高质量的产品。考虑到高油酸油籽品种相似，基于计算机的品种分类系统可能对食品工业有用。我们研究的目的是检验深度学习（DL）算法对葵花籽分类的能力。构建了一个图像采集系统，使用受控照明和固定位置的尼康相机拍摄 6000 颗六种葵花籽品种的种子照片。这些图像被用于创建系统的训练、验证和测试数据集。实施了卷积神经网络 AlexNet 模型来进行品种分类，具体是将两种至六种品种进行分类。分类模型对两类的准确率达到 100%，对六类的准确率达到 89.5%。这些值可以被认为是可以接受的，因为分类的品种非常相似，用肉眼几乎无法区分。这一结果证明，DL 算法可用于高油酸葵花籽的分类。
