Detection, segmentation and tracking of fruits and vegetables are three fundamental tasks for precision agriculture, enabling robotic harvesting and yield estimation applications. However, modern algorithms are data hungry and it is not always possible to gather enough data to apply the best performing supervised approaches. Since data collection is an expensive and cumbersome task, the enabling technologies for using computer vision in agriculture are often out of reach for small businesses. Following previous work in this context, where we proposed an initial weakly supervised solution to reduce the data needed to get state-of-the-art detection and segmentation in precision agriculture applications, here we improve that system and explore the problem of tracking fruits in orchards. We present the case of vineyards of table grapes in southern Lazio (Italy) since grapes are a difficult fruit to segment due to occlusion, color and general illumination conditions. We consider the case in which there is some initial labelled data that could work as source data (\eg wine grape data), but it is considerably different from the target data (e.g. table grape data). To improve detection and segmentation on the target data, we propose to train the segmentation algorithm with a weak bounding box label, while for tracking we leverage 3D Structure from Motion algorithms to generate new labels from already labelled samples. Finally, the two systems are combined in a full semi-supervised approach. Comparisons with state-of-the-art supervised solutions show how our methods are able to train new models that achieve high performances with few labelled images and with very simple labelling.
翻译:水果和蔬菜的检测、分解和跟踪是精密农业的三项基本任务,使机器人的收获和产量估计应用成为了三项基本任务。然而,现代算法缺乏数据,而且并不总是能够收集足够的数据以采用最佳的监管方法。由于数据收集是一项昂贵和繁琐的任务,在农业中使用计算机视觉的有利技术往往对小企业来说遥不可及。在这方面,我们曾提出过一个初步的、监督薄弱的解决方案,以减少在精准农业应用中获取最先进的检测和分解所需的数据。在这里,我们改进了这个系统,并探索了跟踪果园水果的问题。我们介绍了南拉齐奥(意大利)的葡萄葡萄园的例子,因为葡萄园由于隔绝性、颜色和一般的污染条件,对部分来说是很难获得的。我们考虑了这样一个案例,即有些初步的标签数据可以作为源数据(葡萄类数据),但与目标数据(例如,表葡萄数据数据)大不相同。为了改进目标数据的检测和分解,我们提议用较弱的标签葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄的葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄的葡萄葡萄葡萄葡萄葡萄葡萄葡萄葡萄的葡萄的葡萄葡萄葡萄葡萄葡萄葡萄的葡萄葡萄葡萄葡萄葡萄葡萄葡萄的葡萄葡萄生产方法,用新的方法,用来对目标数据进行联合分析。我们用来对目标数据进行分解方法,我们用一个固定的混合的分类的分类方法,我们用一种固定的分类方法,我们用一种固定的分类方法,我们建议用较弱的分类方法,在最后的分类法,在结构结构压压压压压式的标签的标签的标签结构中,我们用较弱的标签的标签的标签的标签的标签的标签的标签的标签方法中,在最后的标签中,我们用两种方法中,用一种总的标签制制制的标签制的标签制的标签制的标签制的标签方法中用的方法,最后的标签制的标签制的标签制的标签法是用来用的方法,最后制制制制制制制制制制制式的双重方法,我们用的方法,最后加压式的标签制式的标签制式的标签制式的标签制式的标签制式的标签制式的标签制式的标签制式的比较压式的