Abstract: In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.
Abstract: PARIS (Personal Archiving and Retrieving Image
System) is an experiment personal photograph library, which includes
more than 80,000 of consumer photographs accumulated within a
duration of approximately five years, metadata based on our proposed
MPEG-7 annotation architecture, Dozen Dimensional Digital Content
(DDDC), and a relational database structure. The DDDC architecture
is specially designed for facilitating the managing, browsing and
retrieving of personal digital photograph collections. In annotating
process, we also utilize a proposed Spatial and Temporal Ontology
(STO) designed based on the general characteristic of personal
photograph collections. This paper explains PRAIS system.