Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality.

Quality monitoring is important for farmland protection. Here, high-resolution remote sensing data obtained by unmanned aerial vehicles (UAVs) and long-term ground sensing data, obtained by wireless sensor networks (WSNs), are uniquely suited for assessing spatial and temporal changes in farmland quality. However, existing UAV-WSN systems are unable to fully integrate the data obtained from these two monitoring systems. This work addresses this problem by designing an improved UAV-WSN monitoring system that can collect both high-resolution UAV images and long-term WSN data during a single-flight mission. This is facilitated by a newly proposed data transmission optimization routing protocol (DTORP) that selects the communication node within a cluster of the WSN to maximize the quantity of data that can be efficiently transmitted, additionally combining individual scheduling algorithms and routing algorithms appropriate for three different distance scales to reduce the energy consumption incurred during data transmission between the nodes in a cluster. The performance of the proposed system is evaluated based on Monte Carlo simulations by comparisons with that obtained by a conventional system using the low-energy adaptive clustering hierarchy (LEACH) protocol. The results demonstrate that the proposed system provides a greater total volume of transmitted data, greater energy utilization efficiency, and a larger maximum revisit period than the conventional system. This implies that the proposed UAV-WSN monitoring system offers better overall performance and enhanced potential for conducting long-term farmland quality data collection over large areas in comparison to existing systems.

[Planning of monitoring points for agricultural products security based on integrated weighted clustering method].

Integrated weighted clustering method was applied to plan the monitoring points for agricultural products security. Definite amounts of key monitoring sampling points were mined out from enormous monitoring objects to make the fewer monitoring sampling points cover the product categories, yields, and regions as more as possible. Among the 10172 agricultural products security enterprises all over the China, 2.46% of them were selected. The tested categories, yields, and regions of agricultural safety products covered 32.71%, 44.29%, and 75% of the total, and their coverage increased by 2.80%, 10.85%, 5.56%, respectively, compared with that by using conventional monitoring and management methods, which suggested that it could be more effective to apply integrated weighted clustering method in setting the monitoring points for agricultural products security.