Measuring growth rates of apple fruitlets is important because it allows apple growers to determine when to apply chemical thinners to their crops to optimize yield. The current practice of obtaining growth rates involves using calipers to record sizes of fruitlets across multiple days. Due to the number of fruitlets needed to be sized, this method is laborious, time-consuming, and prone to human error. In this paper, we present a computer vision approach to measure the sizes and growth rates of apple fruitlets. With images collected by a hand-held stereo camera, our system detects, segments, and fits ellipses to fruitlets to measure their diameters. To measure growth rates, we utilize an Attentional Graph Neural Network to associate fruitlets across different days. We provide quantitative results on data collected in an apple orchard, and demonstrate that our system is able to predict abscise rates within 3% of the current method with a 7 times improvement in speed, while requiring significantly less manual effort. Moreover, we provide results on images captured by a robotic system in the field, and discuss the next steps to make the process fully autonomous.
翻译:衡量苹果果子生长率很重要, 因为它让苹果种植者能够决定何时将化学稀薄剂施用其作物以优化收成。 获取增长率的当前做法是使用卡利珀斯来记录多天的水果大小。 由于水果数量需要大小, 这种方法很费力, 耗时, 容易发生人为错误。 在本文中, 我们提出了一个计算机愿景方法, 以测量苹果果子的大小和生长率。 由手持立体摄像机收集的图像, 我们的系统探测器、 区块和对水果的精液进行匹配, 以测量其直径。 为了测量增长率, 我们使用一个注意图神经网络来将水果连接到不同的日子里。 我们提供苹果园收集的数据的数量结果, 并表明我们的系统能够预测在目前方法的3 % 范围内的收缩率, 速度提高7倍, 而手动努力要少得多。 此外, 我们提供实地机器人系统摄取的图像的结果, 并讨论使过程完全自主的下一步。