Abstract Grape yield forecasting is a valuable economic and quality issue for the grape and wine industry. The number of flowers at bloom could be used as an early indicator towards crop forecast in viticulture. The purpose of this work was to develop a non-invasive method for grapevine flower counting by on-the-go image acquisition, using a combination of deep learning and computer vision technology. A mobile sensing platform was used at 5 km/h to automatically capture Red Green Blue (RGB) images of vineyard canopy at night using artificial illumination under field conditions. For the image data set, 96 vines from six grapevine varieties were selected. For ground-truthing, the number of flowers per inflorescence was counted on a set of clusters before flowering, while yield per vine was weighted at harvest. The developed algorithm comprised two general steps: inflorescences’ segmentation, and individual flower detection. In both steps, the best results were obtained using the deep fully convolutional neural network SegNet architecture with a VGG19 network as the encoder, with F1 score values of 0.93 and 0.73 in the inflorescences segmentation and the individual flower detection steps, respectively. These values showed the high accuracy of the network. A determination coefficient (R2) of 0.91 between the detected number of flowers and the actual number of flowers per vine was obtained. In addition, a linear regression model was trained to estimate the actual number of flowers from the number of detected flowers. A root mean squared error (RMSE) of 590 flowers per vine and a normalized root mean squared error (NRMSE) of 23.7% was obtained. An R2 above 0.70 was achieved between the estimated number of actual flowers and the final yield, per vine. These results show that the number of flowers per vine can be estimated using machine vision and deep learning. The developed imaging platform can be used by the wine industry in commercial vineyards for a satisfactory early crop yield forecasting.

中文翻译：

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基于计算机视觉和深度学习的自动葡萄花检测和量化方法使用移动传感平台在野外条件下进行成像

摘要 葡萄产量预测是葡萄和葡萄酒行业的重要经济和质量问题。开花时的花数可用作葡萄栽培作物预测的早期指标。这项工作的目的是结合深度学习和计算机视觉技术，开发一种通过移动图像采集进行葡萄花计数的非侵入性方法。使用移动传感平台以 5 公里/小时的速度在野外条件下使用人工照明自动捕获夜间葡萄园树冠的红绿蓝 (RGB) 图像。对于图像数据集，选择了来自六个葡萄品种的 96 株葡萄藤。对于地面真实性，在开花前对一组簇计算每个花序的花数，而在收获时对每株葡萄藤的产量进行加权。开发的算法包括两个一般步骤：花序分割和单朵花检测。在这两个步骤中，最好的结果是使用深度全卷积神经网络 SegNet 架构和 VGG19 网络作为编码器获得的，在花序分割和单花检测步骤中，F1 得分值分别为 0.93 和 0.73。这些值显示了网络的高精度。获得了检测到的花数与每株葡萄树的实际花数之间的确定系数 (R2) 为 0.91。此外，训练线性回归模型以根据检测到的花朵数量估计实际花朵数量。获得了每株 590 朵花的均方根误差 (RMSE) 和 23.7% 的归一化均方根误差 (NRMSE)。每株葡萄藤的实际开花数和最终产量之间的 R2 高于 0.70。这些结果表明，可以使用机器视觉和深度学习来估计每株藤蔓的花朵数量。开发的成像平台可用于商业葡萄园的葡萄酒行业，以进行令人满意的早期作物产量预测。