Mass flow estimation is of great importance to several industries, and it can be quite challenging to obtain accurate estimates due to limitation in expense or general infeasibility. In the context of agricultural applications, yield monitoring is a key component to precision agriculture and mass flow is the critical factor to measure. Measuring mass flow allows for field productivity analysis, cost minimization, and adjustments to machine efficiency. Methods such as volume or force-impact have been used to measure mass flow; however, these methods are limited in application and accuracy. In this work, we use deep learning to develop and test a vision system that can accurately estimate the mass of sugarcane while running in real-time on a sugarcane harvester during operation. The deep learning algorithm that is used to estimate mass flow is trained using very sparsely annotated images (semi-supervised) using only final load weights (aggregated weights over a certain period of time). The deep neural network (DNN) succeeds in capturing the mass of sugarcane accurately and surpasses older volumetric-based methods, despite highly varying lighting and material colors in the images. The deep neural network is initially trained to predict mass on laboratory data (bamboo) and then transfer learning is utilized to apply the same methods to estimate mass of sugarcane. Using a vision system with a relatively lightweight deep neural network we are able to estimate mass of bamboo with an average error of 4.5% and 5.9% for a select season of sugarcane.

中文翻译：

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从稀疏注释图像中估计质量的可推广半监督学习方法

质量流量估算对多个行业都非常重要，由于费用限制或普遍不可行，要获得准确的估算值可能非常具有挑战性。在农业应用的背景下，产量监测是精准农业的关键组成部分，质量流量是衡量的关键因素。测量质量流量可以进行现场生产力分析、成本最小化和机器效率调整。体积或力-冲击等方法已被用于测量质量流量；然而，这些方法在应用和准确性方面受到限制。在这项工作中，我们使用深度学习来开发和测试一个视觉系统，该系统可以在甘蔗收割机上实时运行时准确估计甘蔗的质量。用于估计质量流量的深度学习算法使用非常稀疏注释的图像（半监督）仅使用最终负载权重（特定时间段内的聚合权重）进行训练。尽管图像中的光照和材料颜色变化很大，但深度神经网络 (DNN) 成功地准确捕获了甘蔗的质量并超越了旧的基于体积的方法。深度神经网络最初经过训练以预测实验室数据（竹子）的质量，然后利用迁移学习应用相同的方法来估计甘蔗的质量。使用具有相对轻量级深度神经网络的视觉系统，我们能够以 4.5% 和 5.9% 的平均误差估计竹子的质量，对于选定的甘蔗季节。