The production and distribution planning of fresh produce is a complex optimization problem, which is affected by many factors including its perishable characteristics. Farmers cannot guarantee the efficiency and accuracy of production and distribution decisions. Given the close relationship between the production and distribution of annual fresh produce, the intention of our research is to solve the two-stage joint planning problem, and maximize the revenue of farmers ultimately. The internal relationship matrix between the two links of production and distribution is established. On this basis, we propose a mixed integer programming (MIP) model, which covers the constraints of labor and capital. The decisions obtained are not only based on price estimation and resource availability, but also on the impact of the agricultural IoT technology and the special requirements of each distribution channel. Numerical experiments demonstrate that when the planting area is 1 hectare, 4 hectares, and 6 hectares, the proposed joint planning model can improve the distribution revenue of farmers by 7.92%, 4.15%, and 4.94%, respectively, compared with the traditional separate decision-making approach of distribution. According to different decision scenarios, management insights have been obtained. For example, farmers should carefully sort and package products as well as choose a timely and safe third-party express delivery company. Additionally, the proposed strategy can evaluate the impact of distribution channels on farmers’ revenue.

The Internet of Things integrates lots of capacitated vehicles in smart logistics. The routing for capacitated vehicles is a combinatorial optimization problem which has been widely studied in recent years. This paper proposes an effective order-aware hybrid genetic algorithm for the capacitated vehicle routing problem in the Internet of Things. The method is characterized by an improved initialization strategy and a problem-specific crossover operator. The former combines the sweep algorithm with randomness to harmonize the contradiction between diversity and convergence, while the latter integrates neighborhood search heuristics to find the offspring with the best fitness and check constraints simultaneously. A large number of simulations have been carried out, and the results validated the effectiveness of our algorithm.

The platform-based agricultural service is receiving popularity in small-scale farming and shows significant advantages in gathering dispersed service requests and matching supply and demand. However, it also generates new challenges including service traceability, denial and fraud, information security and privacy issues. Blockchain is an emerging technology that provides a secure and trusted environment to track and manage the service process. In this study, we propose a blockchain-based service platform for efficient agricultural service operations with the support of Internet of Things technology. Following the smart platform, we use the drone plant protection service as an example and develop a new execution procedure for smart contract-based agricultural services. In the proposed procedure, we focus on integrating optimization methods to deal with multiple service requests as well as potential disruption events and establishing detailed interactions among service plans, smart contracts, and physical services. Moreover, we formulate the drone plant protection issue using a mixedinteger linear programming model to obtain the optimal service plan and develop a recovery model to deal with potential disruptions of new order arrival. Finally, we design detailed smart contract terms for drone plant protection services. Results of numerical experiments demonstrate the effectiveness of the developed optimization model in obtaining the optimal service plan before and after disruptions. Also, we verify the applicability and security of the smart contract on the Ethereum platform based on a three-phase functional test and a comprehensive security test.