Development Trends in Precision Agriculture and Its Management in China Based on Data Visualization

Abstract

Recent innovations are increasingly recognizing applications in precision agricultural systems that use data science techniques as well as so-called machine learning techniques. Big data analytics have created various data-intensive decision-making opportunities. This study reviews the big data analysis practices in the agriculture industry to resolve various problems to provide prospects and exciting fields of application in China. In the successful implementation of precise farming, the high-volume and complicated data generated present challenges for the economic growth of China. Emerging deep learning techniques seem promising and must be reinvented to meet current challenges. Thus, this paper suggests a big data analytics agriculture monitoring system (BDA-AMS) to ensure the highly accurate prediction of crop yield in precision agriculture and economic management using a deep learning algorithm. The convolution neural network gathers the raw images from UAVs and performs early predictions of crop yield. The simulation analysis using an open-source agricultural dataset resulted in a high parameter–precision ratio (98.8%), high accuracy (98.9%), a better performance ratio (95.5%), an improved data transmission rate (97.8%), a reduced power consumption ratio (18.8%), and an enhanced weather forecasting ratio (94.8%), production density ratio (98.8%), and reliability ratio (98.6%) compared to the baseline models.

Keywords

• big data analytics
• precision agriculture
• crop yield
• deep learning
• prediction