Precision agriculture—a worldwide overview

By Nan Zhang, Maohua Wang, Ning Wang. 2002

1,152 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/3e2780df51017956e81f700ffb0225785c3ba90c

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Citations in the corpus

Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging (2018, background): “With the ever-increasing tendency for automation in agricultural practices [1] and the development of precision agriculture concepts [2,3], it is desirable to develop remote sensing technologies and applications.”
Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications (2020, background): “Many of the limitations in the application of remote sensing techniques for precision agriculture of previous years [8] have been overcome with the launches of Sentinel-2 A + B.”
A Review: The Role of Remote Sensing in Precision Agriculture (2010, background): “PA has emerged as a valuable component of the framework to achieve this goal (Kropff et al., 1997; Tellaeche et al., 2008; Zhang et al., 2002).”
Spatial variation in yield and quality in a small apple orchard (2010, background): “Most applications of precision agriculture to date have been in the USA, Canada, Australia and Northern Europe, where fields are large and soil conditions vary considerably (Zhang et al. 2002; Fountas et al. 2004a, b; Griffin et al. 2004).”
IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology (2012, methodology): “In general, farmers who have adopted PA technology are mostly focusing on map based approaches [73] using a combination of GPS for positional data, GIS for soil mapping, yield monitors for yield mapping and variable rate fertilizer applicators.”
A compilation of UAV applications for precision agriculture (2020, neutral): “Accordingly, in [26] N. Zhang et al. provide an overview of PA by presenting many common applications.”
From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management (2020, background): “[38], the numb r of management zones is a function of the natural variability with the field, the size of the f el and c rtain management f ctors.”
Sentinel 2-Based Nitrogen VRT Fertilization in Wheat: Comparison between Traditional and Simple Precision Practices (2019, background): “However, the majority of precision fertilization approaches are generally map-based, because on-the-go sensors are too expensive, not sufficiently accurate, or not available [26].”
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, background): “Hyperspectral data acquired by traditional platforms like satellites—whose limitations were identified in [29]—and aircrafts is usually influenced by climate—among others factors pointed out by Pölönen [30]—and this hampers its use, especially in precision agriculture, where information detail matte”
Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex (2016, neutral): “Zhang N., M. Wang and N. Wang, 2002.”