Abstract:
Through automated agricultural inspection, farmers can potentially achieve better productivity and accurately predict yields and crop quality. The cheapest device for collecting rich
information about a crop is the video camera. This thesis focuses on the design, implementation, and evaluation of algorithms for extracting information about a pineapple crop from a monocular webcam fixed to a mobile inspection platform. The process begins with fruit
detection and tracking in video sequence. I propose a 3D modeling algorithm that works with the tracking history to generate a 3D point cloud for each fruit using structure-from motion techniques in computer vision. The 3D modeling algorithm consists of two main
procedures: 3D point cloud estimation and 3D fruit shape reconstruction. Once the 3D point cloud for a fruit is obtained, the fruit shape reconstruction method estimates the shape and size of the fruit using a robust ellipsoid estimation technique. A series of experiments shows
that the method produces a reasonably accurate 3D field map despite only partial and occluded views of the fruit. The prototype is thus a promising step towards the realization of automated mobile in-field agricultural inspection systems.
