Automated Visual Yield Estimation in Vineyards
Automated Visual Yield Estimation in Vineyards
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DOI：
10.1002/rob.21541
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发表时间：
2014-09-01
影响因子：
8.3
通讯作者：
Singh, Sanjiv
中科院分区：
文献类型：
--
作者：
Nuske, Stephen;Wilshusen, Kyle;Singh, Sanjiv
我们提出了一个视觉系统，自动预测葡萄园的产量准确和高分辨率。传统的产量估计需要繁琐的手工测量，这是破坏性的，采样稀疏，不准确。我们的方法是有效的，高分辨率的，这是第一个这样的系统在现实的实验评估了几年，数百个葡萄树分布在几英亩不同的葡萄园。其他现有的研究仅限于10棵葡萄树或更少的小测试集，或者只是孤立的葡萄簇，严格控制图像采集和人工诱导产量分布。该系统结合了摄像机和照明安装在车辆驾驶通过葡萄园。我们通过利用纹理，颜色和形状这三个突出的视觉线索来处理图像，并将其转化为一个强大的分类器，即使浆果的颜色与葡萄叶相似，也可以检测到浆果。我们介绍的方法，以最大限度地提高空间和整体精度的产量估计，通过优化图像测量和产量之间的关系。我们的实验在几个葡萄酒和鲜食葡萄园的四个生长季节进行。这是第一个这样的结果，从实验，是足够的大小公平评估对真正的产量变化和现实世界的成像条件，从移动的车辆。结果分析表明，产量估计，捕获高达75%的空间产量方差和3%和11%之间的总产量的平均误差。(C)2014年威利期刊公司
We present a vision system that automatically predicts yield in vineyards accurately and with high resolution. Yield estimation traditionally requires tedious hand measurement, which is destructive, sparse in sampling, and inaccurate. Our method is efficient, high-resolution, and it is the first such system evaluated in realistic experimentation over several years and hundreds of vines spread over several acres of different vineyards. Other existing research is limited to small test sets of 10 vines or less, or just isolated grape clusters, with tightly controlled image acquisition and with artificially induced yield distributions. The system incorporates cameras and illumination mounted on a vehicle driving through the vineyard. We process images by exploiting the three prominent visual cues of texture, color, and shape into a strong classifier that detects berries even when they are of similar color to the vine leaves. We introduce methods to maximize the spatial and the overall accuracy of the yield estimates by optimizing the relationship between image measurements and yield. Our experimentation is conducted over four growing seasons in several wine and table-grape vineyards. These are the first such results from experimentation that is sufficiently sized for fair evaluation against true yield variation and real-world imaging conditions from a moving vehicle. Analysis of the results demonstrates yield estimates that capture up to 75% of spatial yield variance and with an average error between 3% and 11% of total yield. (C) 2014 Wiley Periodicals, Inc.