Remote Sensing Group, Institute of Computer Science, Osnabrück University, 49074 Osnabrück, Germany.
Agronomy and Crop Science, Kiel University, 24118 Kiel, Germany.
Sensors (Basel). 2020 Dec 27;21(1):118. doi: 10.3390/s21010118.
Pumpkins are economically and nutritionally valuable vegetables with increasing popularity and acreage across Europe. Successful commercialization, however, require detailed pre-harvest information about number and weight of the fruits. To get a non-destructive and cost-effective yield estimation, we developed an image processing methodology for high-resolution RGB data from Unmanned aerial vehicle (UAV) and applied this on a Hokkaido pumpkin farmer's field in North-western Germany. The methodology was implemented in the programming language Python and comprised several steps, including image pre-processing, pixel-based image classification, classification post-processing for single fruit detection, and fruit size and weight quantification. To derive the weight from two-dimensional imagery, we calculated elliptical spheroids from lengths of diameters and heights. The performance of this processes was evaluated by comparison with manually harvested ground-truth samples and cross-checked for misclassification from randomly selected test objects. Errors in classification and fruit geometry could be successfully reduced based on the described processing steps. Additionally, different lighting conditions, as well as shadows, in the image data could be compensated by the proposed methodology. The results revealed a satisfactory detection of 95% (error rate of 5%) from the field sample, as well as a reliable volume and weight estimation with Pearson's correlation coefficients of 0.83 and 0.84, respectively, from the described ellipsoid approach. The yield was estimated with 1.51 kg m corresponding to an average individual fruit weight of 1100 g and an average number of 1.37 pumpkins per m. Moreover, spatial distribution of aggregated fruit densities and weights were calculated to assess in-field optimization potential for agronomic management as demonstrated between a shaded edge compared to the rest of the field. The proposed approach provides the Hokkaido producer useful information for more targeted pre-harvest marketing strategies, since most food retailers request homogeneous lots within prescribed size or weight classes.
南瓜是一种经济价值和营养价值都很高的蔬菜，在欧洲的种植面积和受欢迎程度都在不断增加。然而，要想成功商业化，就需要在收获前详细了解果实的数量和重量等信息。为了实现非破坏性且具有成本效益的产量估算，我们开发了一种基于无人机（UAV）高分辨率 RGB 数据的图像处理方法，并将其应用于德国西北部北海道南瓜种植者的农田。该方法在编程语言 Python 中实现，包括图像预处理、基于像素的图像分类、用于单个果实检测的分类后处理，以及果实大小和重量的量化。为了从二维图像中得出重量，我们根据直径和高度的长度计算出了椭圆形的球体。通过与手动收获的地面真实样本进行比较，并对随机选择的测试对象进行交叉检查以确定分类错误，评估了该过程的性能。通过描述的处理步骤，可以成功减少分类和果实几何形状的误差。此外，还可以通过所提出的方法来补偿图像数据中不同的光照条件和阴影。结果表明，从田间样本中得到了令人满意的 95%（误差率为 5%）的检测率，以及可靠的体积和重量估算，描述的椭球体方法的皮尔逊相关系数分别为 0.83 和 0.84。产量估计为 1.51kg/m，平均单个果实重量为 1100g，平均每平方米有 1.37 个南瓜。此外，还计算了聚集果实密度和重量的空间分布，以评估田间优化的潜力，从而进行农业管理，如与田间其余部分相比，在阴凉边缘的优化潜力。该方法为北海道种植者提供了有用的信息，以便制定更有针对性的收获前营销策略，因为大多数食品零售商都要求在规定的大小或重量范围内提供均匀的批次。
