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Smart cities have become a trend with improved efficiency, resilience, and sustainability, providing citizens with high quality of life. With the increasing demand for a more participatory and bottom-up governance approach, citizens play an active role in the process of policy making, revolutionizing the management of smart cities. In the example of urban infrastructure maintenance, the public participation demand is more remarkable as the infrastructure condition is closely related to their daily life. Although blockchain has been widely explored to benefit data collection and processing in smart city governance, public engagement remains a challenge. In this article, we propose a novel public participation consortium blockchain system for infrastructure maintenance that is expected to encourage citizens to actively participate in the decision-making process and enable them to witness all administrative procedures in a real-time manner. To that aim, we introduced a hybrid blockchain architecture to involve a verifier group, which is randomly and dynamically selected from the public citizens, to verify the transaction. In particular, we devised a private-prior peer-prediction-based truthful verification mechanism to tackle the collusion attacks from public verifiers. Then, we specified a Stackelberg-game-based incentive mechanism for encouraging public participation. Finally, we conducted extensive simulations to reveal the properties and performances of our proposed blockchain system, which indicates its superiority over other variations.

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Crop row detection is one of the foundational and pivotal technologies of agricultural robots and autonomous vehicles for navigation, guidance, path planning, and automated farming in row crop fields. However, due to a complex and dynamic agricultural environment, crop row detection remains a challenging task. The surrounding background, such as weeds, trees, and stones, can interfere with crop appearance and increase the difficulty of detection. The detection accuracy of crop rows is also impacted by different growth stages, environmental conditions, curves, and occlusion. Therefore, appropriate sensors and multiple adaptable models are required to achieve high-precision crop row detection. This paper presents a comprehensive review of the methods and applications related to crop row detection for agricultural machinery navigation. Particular attention has been paid to the sensors and systems used for crop row detection to improve their perception and detection capabilities. The advantages and disadvantages of current mainstream crop row detection methods, including various traditional methods and deep learning frameworks, are also discussed and summarized. Additionally, the applications for different crop row detection tasks, including irrigation, harvesting, weeding, and spraying, in various agricultural scenarios, such as dryland, the paddy field, orchard, and greenhouse, are reported.

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