Detecting the long-term spatiotemporal crop phenology changes in a highly fragmented agricultural landscape

Sisheber, Biniam; Marshall, Michael; Mengistu, Dirriba; Nelson, Andrew

doi:10.1016/j.agrformet.2023.109601

Cited by 10 publications

(1 citation statement)

References 40 publications

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“…Remote sensing satellites provide us with the ability to quickly obtain rich band information in a large range of images, while the development of UAV remote sensing technology and the ground Internet of Things technology can provide more flexible observation methods to obtain rich spatial details and high-accuracy ground information with a spatial resolution of up to the centimeter level [15][16][17]. Among them, sensors such as RGB, hyperspectral, and thermal imaging cameras can be used for all applications of drone remote sensing in precision agriculture research.…”

Section: Introductionmentioning

confidence: 99%

A New Remote Sensing Service Mode for Agricultural Production and Management Based on Satellite–Air–Ground Spatiotemporal Monitoring

Li,

Dong,

Zhang

et al. 2023

Agriculture

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Remote sensing, the Internet, the Internet of Things (IoT), artificial intelligence, and other technologies have become the core elements of modern agriculture and smart farming. Agricultural production and management modes guided by data and services have become a cutting-edge carrier of agricultural information monitoring, which promotes the transformation of the intelligent computing of remote sensing big data and agricultural intensive management from theory to practical applications. In this paper, the main research objective is to construct a new high-frequency agricultural production monitoring and intensive sharing service and management mode, based on the three dimensions of space, time, and attributes, that includes crop recognition, growth monitoring, yield estimation, crop disease or pest monitoring, variable-rate prescription, agricultural machinery operation, and other automatic agricultural intelligent computing applications. The platforms supported by this mode include a data management and agricultural information production subsystem, an agricultural monitoring and macro-management subsystem (province and county scales), and two mobile terminal applications (APPs). Taking Shandong as the study area of the application case, the technical framework of the system and its mobile terminals were systematically elaborated at the province and county levels, which represented macro-management and precise control of agricultural production, respectively. The automatic intelligent computing mode of satellite–air–ground spatiotemporal collaboration that we proposed fully couples data obtained from satellites, unmanned aerial vehicles (UAVs), and IoT technologies, which can provide the accurate and timely monitoring of agricultural conditions and real-time guidance for agricultural machinery scheduling throughout the entire process of agricultural cultivation, planting, management, and harvest; the area accuracy of all obtained agricultural information products is above 90%. This paper demonstrates the necessity of customizable product and service research in agricultural intelligent computing, and the proposed practical mode can provide support for governments to participate in agricultural macro-management and decision making, which is of great significance for smart farming development and food security.

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Section: Introductionmentioning

confidence: 99%