Foreseeing changes in the way companies manufacture products and provide services, future trends are emerging in design and manufacture. Together with growing internet applications and technologies connected through the cloud, a new Industrial Revolution, named “Industry 4.0”, aims to integrate cyber-virtual and cyber-physical systems to aid smart manufacturing, as presented in this paper. Connecting information and physical machinery, this new paradigm relies on how effective and fast connectivity are achieved for Industry 4.0. A new generation of wireless connection, 5G, will help and accelerate this trend. Following analysis of the present cyber-physical integration for the 4 ^th Industrial Revolution, this paper also investigates future methodologies and trends in smart manufacturing, design and innovation.

Food security is one of the societal challenge topics. As one-third of all food consumed by humans relies on animal pollination currently, this research provides an emerging solution to food supply reduction caused by population shrinking of natural pollinators, so as to reduce its impact on ecological relationships, ecosystem conservation and stability, genetic variation in the crop plant community, floral diversity, specialisation and evolution. This paper develops a conceptual technical roadmap of autonomous pollination for future farming using robotic micro air vehicle pollinators (MPrs). The research provides new insights into autonomous design and manufacture and into possible ways to increase the production efficiency which shortens the time from lab to market. The autonomous MPrs are realized using artificial intelligence and human expertise in the loop for smart agricultural industry. Further, this work identifies scientific and technological advances that are expected to translate, within proposed regulatory frameworks, into the pervasive use of MPrs for agricultural applications and beyond.

Machine-vision-based surface defect inspection is one of the key technologies to realize intelligent manufacturing. This paper provides a systematic review on leather surface defect inspections based on machine vision. Leather products are regarded as the most traded products all over the world. Automatic detection, location, and recognition of leather surface defects are very important for the intelligent manufacturing of leather products, and are challenging but noteworthy tasks. This work investigates a large amount of literature related to leather surface defect inspection. In addition, we also investigate and evaluate the performance of some edge detectors and threshold detectors for leather defect detection, and the identification accuracy of the classical machine learning method SVM for leather surface defect identification. A detailed and methodical review of leather surface defect inspection with image analysis and machine learning is presented. Main challenges and future development trends are discussed for leather surface defect inspection, which can be used as a source of guidelines for designing and developing new solutions in this field.