This paper presents a convolutional neural network (CNN) to classify between the conventionally and organically cultivated Memecik varieties of green olives. The image forming method called the rising paper chromatography is utilized in preparing the images of Memecik varieties of green olives for CNN. In the rising chromatography method, 20, 30, and 40% sample concentrations were determined as the suitable concentrations for both organic and conventional olives. The concentrations of AgNO₃ and FeSO₄ were determined as 0.25, 0.5, 0.75 and 1% for both conventional and organic samples. The visual differences used for differentiation of different types of Memecik green olives are usually determined according to the regional color differences, the vivid color occurrence, the width and the frequency of bowl occurrence, the thin line, and the picks at drop zone by the expert assessors. The testing results in this study verified the effectiveness of the CNN methodology in differentiating between the organically and conventionally cultivated Memecik green olives. The newly designed neural network achieved 100% accuracy. Furthermore, this high accuracy achieved by CNN might suggest that it can be effectively used in place of the expert assessors.

本文提出了一种卷积神经网络 (CNN)，用于对传统和有机种植的 Memecik 绿橄榄品种进行分类。在为 CNN 准备 Memecik 绿橄榄品种的图像时，使用了称为上升纸色谱的图像形成方法。在上升色谱法中，20%、30% 和 40% 的样品浓度被确定为有机橄榄和常规橄榄的合适浓度。AgNO ₃和 FeSO ₄的浓度对于常规样品和有机样品，分别确定为 0.25、0.5、0.75 和 1%。用于区分不同类型的 Memecik 绿橄榄的视觉差异通常是根据区域颜色差异、鲜艳的颜色出现、碗出现的宽度和频率、细线和专家在滴区的采摘来确定的评估员。本研究中的测试结果验证了 CNN 方法在区分有机和常规种植的 Memecik 绿橄榄方面的有效性。新设计的神经网络达到了 100% 的准确率。此外，CNN 实现的这种高精度可能表明它可以有效地代替专家评估员。