E. Kaufmane, Edgars Edelmers, K. Sudars, Ivars Namatēvs, A. Nikulins, S. Strautiņa, I. Kalniņa, Astile Peter
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引用次数: 0
摘要
本研究介绍了一种利用三维成像测量果实的创新方法,重点是拉脱维亚种植的日本榅桲(Chaenomeles japonica)。研究分为两个阶段:使用卡尺对水果参数(长度和宽度)进行人工测量;使用基于 k 近邻(k-NN)算法、巧妙设计的 "虚方格 "方法和物体投影分析进行三维成像。我们的研究结果表明,人工测量和三维成像数据之间存在差异,凸显了三维成像技术在精度和准确性方面面临的挑战。研究发现了两个主要的限制因素:扫描平台上水果定位的可变性和近距离区分单个水果的困难。这些限制突出表明,需要改进算法能力,以处理不同的空间方向和距离。我们的发现强调了改进三维扫描技术以提高农业应用可靠性和准确性的重要性。加强图像处理、深度感知算法和机器学习模型对于在各种农业场景中有效实施至关重要。这项研究不仅有助于科学理解园艺中的三维成像技术,还强调了其在推进可持续和高产农业实践中的潜力和局限性。
Three-Dimensional Imaging in Agriculture: Challenges and Advancements in the Phenotyping of Japanese Quinces in Latvia
This study presents an innovative approach to fruit measurement using 3D imaging, focusing on Japanese quince (Chaenomeles japonica) cultivated in Latvia. The research consisted of two phases: manual measurements of fruit parameters (length and width) using a calliper and 3D imaging using an algorithm based on k-nearest neighbors (k-NN), the ingeniously designed “Imaginary Square” method, and object projection analysis. Our results revealed discrepancies between manual measurements and 3D imaging data, highlighting challenges in the precision and accuracy of 3D imaging techniques. The study identified two primary constraints: variability in fruit positioning on the scanning platform and difficulties in distinguishing individual fruits in close proximity. These limitations underscore the need for improved algorithmic capabilities to handle diverse spatial orientations and proximities. Our findings emphasize the importance of refining 3D scanning techniques for better reliability and accuracy in agricultural applications. Enhancements in image processing, depth perception algorithms, and machine learning models are crucial for effective implementation in diverse agricultural scenarios. This research not only contributes to the scientific understanding of 3D imaging in horticulture but also underscores its potential and limitations in advancing sustainable and productive farming practices.