论文标题
从多个图像中估算葡萄的葡萄产量
Estimating Grape Yield on the Vine from Multiple Images
论文作者
论文摘要
在收获前估算葡萄产量对商业葡萄园的生产很重要，因为它可以为许多葡萄园和酿酒厂的决策提供信息。当前，收益估计的过程耗时，其准确性从75-90 \％因葡萄栽培家的经验而异。本文提出了多个任务学习（MTL）卷积神经网络（CNN）方法，该方法使用了通过简单的三脚架布置保护的廉价智能手机捕获的图像。 CNN模型使用MTL从自动编码器转移，从收获前6天捕获的图像数据中获得85 \％的准确性。
Estimating grape yield prior to harvest is important to commercial vineyard production as it informs many vineyard and winery decisions. Currently, the process of yield estimation is time consuming and varies in its accuracy from 75-90\% depending on the experience of the viticulturist. This paper proposes a multiple task learning (MTL) convolutional neural network (CNN) approach that uses images captured by inexpensive smart phones secured in a simple tripod arrangement. The CNN models use MTL transfer from autoencoders to achieve 85\% accuracy from image data captured 6 days prior to harvest.
