Design and implementation of the agricultural meteorological system based on machine vision and cloud platform

Abstract

There are many problems in the process of agricultural production, such as high labor costs, lack of processional management, delayed disaster warning, waste of agricultural information. In order to solve these problems, we developed the Argo-meteorological system. On the base of achieving real-time monitoring of farmland and disaster warning, it focused on the implementation of the comprehensive analysis and the storage of data on cloud platform which simplified the system structure and improved the efficiency of agricultural management. To help managers understand the exact situation like the growth of crops, pests and diseases, weather, and environment, sensors and binocular imaging array were used by the low-power sensing devices to obtain data. Then the data was converged to the data center on the cloud platform to be classified and processed. Meanwhile, warning feedback was given after analyzing the collected data and the standard indicators of agricultural production. The results of processing were pushed to monitoring system on PC and then showed in real-time. The test results showed that the system could achieve stable data transmission, efficient data processing and provide massive data for data mining. The cost of system maintenance and upgrade was reduced.