Plant phenotyping involves the measurement, ideally objectively, of characteristics or traits. Traditionally, this is either limited to tedious and sparse manual measurements, often acquired destructively, or coarse image-based 2D measurements. 3D sensing technologies (3D laser scanning, structured light and digital photography) are increasingly incorporated into mass produced consumer goods and have the potential to automate the process, providing a cost-effective alternative to current commercial phenotyping platforms. We evaluate the performance, cost and practicability for plant phenotyping and present a 3D reconstruction method from multi-view images acquired with a domestic quality camera. This method consists of the following steps: (i) image acquisition using a digital camera and turntable; (ii) extraction of local invariant features and matching from overlapping image pairs; (iii) estimation of camera parameters and pose based on Structure from Motion(SFM); and (iv) employment of a patch based multi-view stereo technique to implement a dense 3D point cloud. We conclude that the proposed 3D reconstruction is a promising generalized technique for the non-destructive phenotyping of various plants during their whole growth cycles
PurposeMelting universality, quantification and relative computability into a meta‐synthesis, pansystems theory develops an investigation on W‐fuzziness and 0*‐fuzziness connected with generalized conceptions such as derivative, equation, variational principle and OR. The purpose of this paper is to unify various mathematical structures, fuzziness categories, definitions of systems are unified within a general framework.Design/methodology/approachThe paper includes topics: pansystems approach to fuzzy systems and relations, pansystems variational principle and Zadeh's extension principle, pansystems clustering and its fuzzy embodiment, pansystems topology and approximation to fuzziness, relative unification of fuzziness and roughness.FindingsZadeh extension principle about fuzziness transmission can be considered as a specific model of pansystems extremum principle, and so the more modes can be developed. Based on them a further investigation is present on pansystems clustering, which is a W‐fuzzy clustering, an extension or sublation of traditional one and fuzzy one.Originality/valuePansystems clustering embodies mutuality of many logoi of different subbraches with classification‐styled OR, including related interpromotions of the principles among knowledge rediscovery, data mining, mathematical reasoning and the investigations of fuzzy systems. W‐fuzziness and 0*‐fuzziness realize a relative unification for many logoi and principles.
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