The introduction of a consortium blockchain-based agricultural machinery scheduling system will help improve the transparency and efficiency of the data flow within the sector. Currently, the traditional agricultural machinery centralized scheduling systems suffer when there is a failure of the single point control system, and it also comes with high cost managing with little transparency, not leaving out the wastage of resources. This paper proposes a consortium blockchain-based agricultural machinery scheduling system for solving the problems of single point of failure, high-cost, low transparency, and waste of resources. The consortium blockchain-based system eliminates the central server in the traditional way, optimizes the matching function and scheduling algorithm in the smart contract, and improves the scheduling efficiency. The data in the system can be traced, which increases transparency and improves the efficiency of decision-making in the process of scheduling. In addition, this system adopts a crowdsourcing scheduling mode, making full use of idle agricultural machinery in the society, which can effectively solve the problem of resource waste. Then, the proposed system implements authentication access mechanisms, and allows only authorized users into the system. It includes transactions based on digital currency and eliminates third-party platform to charge service fees. Moreover, participating organizations have the opportunity to obtain benefits and reduce transaction costs. Finally, the upper layers supervision improves the efficiency and security of consensus algorithm, allows supervisors to block users with malicious motives, and always ensures system security.

The advent of sensor-cloud technology alleviates the limitations of traditional wireless sensor networks (WSNs) in terms of energy, storage, and computing, which has tremendous potential in various agricultural internet of things (IoT) applications. In the sensor-cloud environment, virtual sensor provisioning is an essential task. It chooses physical sensors to create virtual sensors in response to the users’ requests. Considering the capricious meteorological environment of the outdoors, this paper presents an measurements similarity-based virtual-sensor provisioning scheme by taking advantage of machine learning in data analysis. First, to distinguish the changing trends, we classified all the physical sensors into several categories using historical data. Then, the k-means clustering algorithm was exploited for each class to cluster the physical sensors with high similarity. Finally, one representative physical sensor from each cluster was selected to create the corresponding virtual sensors. The experimental results show the reformation of our scheme with respect to energy efficiency, network lifetime, and data accuracy compared with the benchmark schemes.