Figorilli Simone, Violino Simona, Moscovini Lavinia, Ortenzi Luciano, Salvucci Giorgia, Vasta Simone, Tocci Francesco, Costa Corrado, Toscano Pietro, Pallottino Federico
Consiglio per la Ricerca in Agricoltura e l'Analisi dell'Economia Agraria (CREA)-Centro di Ricerca Ingegneria e Trasformazioni Agroalimentari, Via della Pascolare 16, Monterotondo, 00015 Rome, Italy.
Dipartimento Di Ingegneria Civile e Ingegneria Informatica, Università Degli Studi di Roma "Tor Vergata", Via del Politecnico 1, 00133 Rome, Italy.
Foods. 2022 Oct 27;11(21):3391. doi: 10.3390/foods11213391.
(1) Background: Extra virgin olive oil production is strictly influenced by the quality of fruits. The optical selection allows for obtaining high quality oils starting from batches with different qualitative characteristics. This study aims to test a CNN algorithm in order to assess its potential for olive classification into several quality classes for industrial purposes, specifically its potential integration and sorting performance evaluation. (2) Methods: The acquired samples were all subjected to visual analysis by a trained operator for the distinction of the products in five classes related to the state of external veraison and the presence of visible defects. The olive samples were placed at a regular distance and in a fixed position on a conveyor belt that moved at a constant speed of 1 cm/s. The images of the olives were taken every 15 s with a compact industrial RGB camera mounted on the main frame in aluminum to allow overlapping of the images, and to avoid loss of information. (3) Results: The modelling approaches used, all based on AI techniques, showed excellent results for both RGB datasets. (4) Conclusions: The presented approach regarding the qualitative discrimination of olive fruits shows its potential for both sorting machine performance evaluation and for future implementation on machines used for industrial sorting processes.
(1) 背景：特级初榨橄榄油的生产受到果实品质的严格影响。光学分选能够从具有不同品质特征的批次中获取高质量的油。本研究旨在测试一种卷积神经网络（CNN）算法，以评估其在工业用途中将橄榄分类为多个品质等级的潜力，特别是其潜在的整合和分选性能评估。(2) 方法：所有采集的样本均由经过培训的操作人员进行视觉分析，以便将产品区分为与外部转色状态和可见缺陷存在情况相关的五个类别。橄榄样本以固定距离并固定在以1厘米/秒的恒定速度移动的传送带上。每隔15秒，使用安装在铝制主机架上的紧凑型工业RGB相机拍摄橄榄图像，以实现图像重叠并避免信息丢失。(3) 结果：所使用的建模方法均基于人工智能技术，对两个RGB数据集均显示出优异的结果。(4) 结论：所提出的关于橄榄果实品质鉴别的方法显示出其在分选机性能评估以及未来在工业分选过程所用机器上实施的潜力。
