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Time of flight (ToF) imaging is a promising emerging technology for the purposes of crop identification. This paper aim to presents localization system for identifying and localizing asparagus in the field based on point clouds from ToF imaging. Since the semantics are not included in the point cloud, it contains the geometric information of other objects such as stones and weeds other than asparagus spears. An approach is required for extracting the spear information so that a robotic system can be used for harvesting.

A real-time convolutional neural network (CNN)-based method is used for filtering the point cloud generated by a ToF camera, allowing subsequent processing methods to operate over smaller and more information-dense data sets, resulting in reduced processing time. The segmented point cloud can then be split into clusters of points representing each individual spear. Geometric filters are developed to eliminate the non-asparagus points in each cluster so that each spear can be modelled and localized. The spear information can then be used for harvesting decisions.

The localization system is integrated into a robotic harvesting prototype system. Several field trials have been conducted with satisfactory performance. The identification of a spear from the point cloud is the key to successful localization. Segmentation and clustering points into individual spears are two major failures for future improvements.

Most crop localizations in agricultural robotic applications using ToF imaging technology are implemented in a very controlled environment, such as a greenhouse. The target crop and the robotic system are stationary during the localization process. The novel proposed method for asparagus localization has been tested in outdoor farms and integrated with a robotic harvesting platform. Asparagus detection and localization are achieved in real time on a continuously moving robotic platform in a cluttered and unstructured environment.

This paper aims to investigate if a Cartesian robot system for kiwifruit harvesting works more effectively and efficiently than an articulated robot system. The robot is a key component in agricultural automation. For instance, multiple robot arm system has been developed for kiwifruit harvesting recently because of the significant labor shortage issue. The industrial robots for factory automation usually have articulated configuration which is suitable for the tasks in the manufacturing and production environment. However, this articulated configuration may not fit for agricultural application due to the large outdoor environment.

The kiwifruit harvesting tasks are completed step by step so that the robot workspace covers the canopy completely. A two-arm, Cartesian kiwifruit harvesting robot system and several field experiments are developed for the investigation. The harvest cycle time of the Cartesian robot system is compared to that of an articulated robot system. The difference is analyzed based on the workspace geometries of these two robot configurations.

It is found that the kiwifruit harvesting productivity is increased by using a multiple robot system with Cartesian configuration owing to its regular workspace geometry.

An articulated robot is a common configuration for manufacturing because of its simple structure and the relatively static factory environment. Most of the agricultural robotics research studies use single articulated robot for their implementation. This paper pinpoints how the workspace of a multiple robot system affects the harvest cycle time for kiwifruit harvesting in a pergola style kiwifruit orchard.

A harvesting robot is developed as part of kiwifruit industry automation in New Zealand. This kiwifruit harvester is currently not economically viable, as it drops and damages too many kiwifruit in the harvesting task due to the positional inaccuracy of the gripper. This is due to the difficulties in measuring the exact effective dimensions of the gripper from the manipulator. The purpose of this study is to obtain the effective gripper dimensions using kinematic calibration procedures.

A setup of a constraint plate with a dial gauge is proposed to acquire the calibration data. The constraint plate is positioned above the robot. The data is obtained by using a dial gauge and a permanent marker. The effective dimensions of the gripper are used as error parameters in the calibration process. Calibration is exercised by minimizing the difference between target positions and measured positions iteratively.

The robot with the obtained effective dimensions is tested in the field. It is found that the fruit drops due to positional inaccuracy of the gripper are greatly reduced after calibration.

The kiwifruit industry in New Zealand is growing rapidly and announced plans in 2017 to double global sales by 2025. This growth will put extra pressure on the labour supply for harvesting. Furthermore, the Covid pandemic and resulting border restrictions have dramatically reduced seasonal imported labour availability. A robotic system is a potential solution to address the labour shortages for harvesting kiwifruit.

For kiwifruit harvesting, the picking envelope is well above the robot; the experimental data points obtained by placing a constraint plate above the robot are at similar positions to the target positions of kiwifruit. Using this set of data points for calibration yields a good effect of obtaining the effective dimension of the gripper, which reduces the positional inaccuracy as shown in the field test results.

The COVID-19 pandemic has created a new normal for international business (IB) activities, leaving them pondering their next steps. The decreasing effectiveness of current vaccines to protect individuals against new variants have created uncertainty on how to respond to the new waves of the COVID-19 infection. This study aims to empirically assesses how IBs perceive the unfolding challenges in the supply chain due to the pandemic and the solutions.

The survey data is obtained from 166 logistics professionals in Hong Kong and India.

The results reveal that returns on investment, logistics, delays and imports are the most affected areas. The most often recommended solutions for supply chain management (SCM) include using local manufacturing capabilities, analytics and automation, offering better customer service, providing more effective transportation means, ensuring diligence around optimization and focusing on sustainability.

The findings of this study help to improve supply chain operations. This study also provides recommendations for changes to SCM in response to the new normal.

The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.

The main objective of this research is to investigate the factors that influence consumer purchase decisions for halal products before and during the COVID-19 pandemic, based on the Engel-Kollat-Blackwell (EKB) theory.

The research was conducted as a survey. The influencing factors were determined based on the grey relational analysis (GRA) approach.

The findings indicate before the COVID-19 pandemic, consumers mainly purchased halal products based on four key factors: purchasing experience, certification label, Internet searches and past consumption experience. However, during the pandemic, the ranking and factors have changed to six indicators, which are past consumption experience, purchasing experience, certification labels, standardized specifications, Internet searches and halal certification labels.

The study was limited by the sample size and geographical area. Nevertheless, the findings could be further explored by expanding related theories toward understand human decisions based on spiritual beliefs.

The findings of this study have important implications for research, practice and society. Understanding the factors influencing halal purchase decisions before and during the pandemic can help businesses, policymakers and halal certification bodies to better cater to consumers' needs and preferences and ensure the continued growth and development of the halal industry.

This study evaluates halal purchasing decisions between periods of certainty and uncertainty by using the GRA. Changes in halal consumption and purchase decisions in response to COVID-19 pandemic have become an emerging topic of discovery. The study addresses the gap in the literature regarding changes in consumer decision pattern.