Application and Research of Key Technologies of Big Data for Agriculture

Literature Review

In recent years, with the rapid development of science and technology, advanced technical means such as information technology have been widely used in various fields of society, realizing reform and innovation in these fields. Mage data technology is an inevitable product in the context of the information age. Mage data technology, Internet, cloud computing technology have changed people's production and lifestyle, and society has entered a new era of image data. Due to limited accessibility outside of the scientific community, hyperspectral images have not been widely used in precision agriculture. Compared with multispectral imaging, hyperspectral imaging is a more advanced technology, which can obtain the detailed spectral response of target features (De Alwis et al., 2022). It is a useful tool to monitor the temporal and spatial changes of crop morphology and physiological status and support precision agriculture practice (Lu et al., 2020). As a basic resource, data plays a key role in the development of various industries. China is a large agricultural country with diverse agricultural data and huge data systems, which brings great challenges to data analysis, management, storage, and so on. Agricultural remote sensing is one of the core technologies of precision agriculture. It takes into account the variability of the field to manage specific locations, rather than unified management in traditional agriculture. As a general remote sensing data, agricultural remote sensing data has all the characteristics of big data. The collection, processing, storage, analysis, and visualization of agricultural remote sensing big data are the keys to the success of precision agriculture. In the past decades, the use of remote sensing technology for precision agriculture (PA) has increased rapidly. The unprecedented high-resolution (spatial, spectral, and temporal) satellite images have promoted the application of remote sensing in many PA applications, including crop monitoring, irrigation management, nutrient application (Huang et al., 2018), pest management, and yield prediction (Sishodia et al., 2020). The combination of big data technology and modern agriculture can add rich experience to agricultural projects and greatly improve project execution ability and data analysis ability, which shows that modern agriculture can fully exert its technical application advantages to meet the requirements of centralized data processing and analysis. In the field of farming, with the continuous in-depth development of information construction and Agricultural Internet of Things (IoT) technology, as well as the continuous development of electronic information technology in recent years, the diversification and cheapness of electronic collection equipment have led to the rapid progress and development of Agricultural IoT. In addition, the continuous investment of the state in the construction of agricultural science and technology informatization, as well as the vigorous development of smart farming, fine farming, Agricultural IoT technology, and other projects, have made the level of agricultural science and technology achieve rapid development.