Noninvasive diagnosis of internal traits in fruit crops is a high unmet need; however it generally requires time, costs, and special methods or facilities. Recent progress in deep neural network (or deep learning) techniques would allow easy, but highly accurate diagnosis with single RGB images, and the latest applications enable visualization of “the reasons for each diagnosis” by backpropagation of neural networks. Here, we propose an application of deep learning for image diagnosis on the classification of internal fruit traits, in this case seedlessness, in persimmon fruit (Diospyros kaki). We examined the classification of seedlessness in persimmon fruit by using four convolutional neural networks (CNN) models with various layer structures. With only 599 pictures of ‘Fuyu’ persimmon fruit from the fruit apex side, the neural networks successfully made a binary classification of seedless and seeded fruits with up to 85% accuracy. Among the four CNN models, the VGG16 model with the simplest layer structure showed the highest classification accuracy of 89%. Prediction values for the binary classification of seeded fruits were significantly increased in proportion to seed numbers in all four CNN models. Furthermore, explainable AI methods, such as Gradient-weighted Class Activation Mapping (Grad-CAM) and Guided Grad-CAM, allowed visualization of the parts and patterns contributing to the diagnosis. The results indicated that finer positions surrounding the apex, which correspond to hypothetical bulges derived from seeds, are an index for seeded fruits. These results suggest the novel potential of deep learning for noninvasive diagnosis of fruit internal traits using simple RGB images and also provide novel insights into previously unrecognized features of seeded/seedless fruits.

水果作物内在性状的非侵入性诊断是亟待解决的问题。但是，这通常需要时间，成本以及特殊的方法或设施。深度神经网络（或深度学习）技术的最新进展将允许使用单个RGB图像进行简单但高度准确的诊断，而最新的应用程序则通过神经网络的反向传播实现了“每次诊断的原因”的可视化。在这里，我们提出了深学习的内部果实性状的分类图像诊断的应用程序，在这种情况下，无核，在柿子（柿）。我们通过使用具有不同层结构的四个卷积神经网络（CNN）模型检查了柿子果实中无核的分类。神经网络仅从水果顶点一侧获得599张'富宇'柿子水果的图片，因此成功地对无籽和有籽水果进行了二值分类，准确度高达85％。在四个CNN模型中，具有最简单层结构的VGG16模型显示了89％的最高分类精度。在所有四个CNN模型中，种子水果的二进制分类的预测值与种子数量成比例地显着增加。此外，可解释的AI方法（例如，梯度加权类激活映射（Grad-CAM）和Guided Grad-CAM）允许可视化有助于诊断的零件和模式。结果表明，根尖周围较细的位置（对应于从种子衍生的假想的凸起）是种子果实的指标。这些结果表明，深度学习对于使用简单的RGB图像进行无创诊断水果内部性状具有潜在的新颖性，并且还提供了对种子/无籽水果先前无法识别的特征的新颖见解。