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Precision agriculture

About: Precision agriculture is a(n) research topic. Over the lifetime, 5528 publication(s) have been published within this topic receiving 87497 citation(s). The topic is also known as: SSCM & precision farming.

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Journal ArticleDOI
D. Haboudane1, John R. Miller2, John R. Miller1, Nicolas Tremblay3  +2 moreInstitutions (4)
Abstract: Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing in the assessment of vegetation biophysical variables both in forestry and agriculture. Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as Leaf Area Index (LAI), leaf chlorophyll content, canopy shadows, and background soil reflectance. Of particular significance to precision agriculture is chlorophyll content, an indicator of photosynthesis activity, which is related to the nitrogen concentration in green vegetation and serves as a measure of the crop response to nitrogen application. This paper presents a combined modeling and indices-based approach to predicting the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil (background) effects. This combined method has been developed first using simulated data and followed by evaluation in terms of quantitative predictive capability using real hyperspectral airborne data. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer models. In this modeling study, we developed an index that integrates advantages of indices minimizing soil background effects and indices that are sensitive to chlorophyll concentration. Simulated data have shown that the proposed index Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) is both very sensitive to chlorophyll content variations and very resistant to the variations of LAI and solar zenith angle. It was therefore possible to generate a predictive equation to estimate leaf chlorophyll content from the combined optical index derived from above-canopy reflectance. This relationship was evaluated by application to hyperspectral CASI imagery collected over corn crops in three experimental farms from Ontario and Quebec, Canada. The results presented here are from the L’Acadie, Quebec, Agriculture and AgriFood Canada research site. Images of predicted leaf chlorophyll content were generated. Evaluation showed chlorophyll variability over crop plots with various levels of nitrogen, and revealed an excellent agreement with ground truth, with a correlation of r 2 =.81 between estimated

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1,287 citations


Journal ArticleDOI
Chunhua Zhang1, John M. Kovacs2Institutions (2)
TL;DR: To provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required and the farmer should involve in the process of field design, image acquisition, image interpretation and analysis.

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Abstract: Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.

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1,152 citations


Journal ArticleDOI
David J. Mulla1Institutions (1)
Abstract: Precision agriculture dates back to the middle of the 1980's. Remote sensing applications in precision agriculture began with sensors for soil organic matter, and have quickly diversified to include satellite, aerial, and hand held or tractor mounted sensors. Wavelengths of electromagnetic radiation initially focused on a few key visible or near infrared bands. Today, electromagnetic wavelengths in use range from the ultraviolet to microwave portions of the spectrum, enabling advanced applications such as light detection and ranging (LiDAR), fluorescence spectroscopy, and thermal spectroscopy, along with more traditional applications in the visible and near infrared portions of the spectrum. Spectral bandwidth has decreased dramatically with the advent of hyperspectral remote sensing, allowing improved analysis of specific compounds, molecular interactions, crop stress, and crop biophysical or biochemical characteristics. A variety of spectral indices now exist for various precision agriculture applications, rather than a focus on only normalised difference vegetation indices. Spatial resolution of aerial and satellite remote sensing imagery has improved from 100's of m to sub-metre accuracy, allowing evaluation of soil and crop properties at fine spatial resolution at the expense of increased data storage and processing requirements. Temporal frequency of remote sensing imagery has also improved dramatically. At present there is considerable interest in collecting remote sensing data at multiple times in order to conduct near real time soil, crop and pest management.

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1,017 citations


Journal ArticleDOI
Dennis L. Corwin1, Scott M. Lesch1Institutions (1)
Abstract: The field-scale application of apparent soil electrical conductivity (EC"a) to agriculture has its origin in the measurement of soil salinity, which is an arid-zone problem associated with irrigated agricultural land and with areas having shallow water tables. Apparent soil electrical conductivity is influenced by a combination of physico-chemical properties including soluble salts, clay content and mineralogy, soil water content, bulk density, organic matter, and soil temperature; consequently, measurements of EC"a have been used at field scales to map the spatial variation of several edaphic properties: soil salinity, clay content or depth to clay-rich layers, soil water content, the depth of flood deposited sands, and organic matter. In addition, EC"a has been used at field scales to determine a variety of anthropogenic properties: leaching fraction, irrigation and drainage patterns, and compaction patterns due to farm machinery. Since its early agricultural use as a means of measuring soil salinity, the agricultural application of EC"a has evolved into a widely accepted means of establishing the spatial variability of several soil physico-chemical properties that influence the EC"a measurement. Apparent soil electrical conductivity is a quick, reliable, easy-to-take soil measurement that often, but not always, relates to crop yield. For these reasons, the measurement of EC"a is among the most frequently used tools in precision agriculture research for the spatio-temporal characterization of edaphic and anthropogenic properties that influence crop yield. It is the objective of this paper to provide a review of the development and use of EC"a measurements for agricultural purposes, particularly from a perspective of precision agriculture applications. Background information is presented to provide the reader with (i) an understanding of the basic theories and principles of the EC"a measurement, (ii) an overview of various EC"a measurement techniques, (iii) applications of EC"a measurements in agriculture, particularly site-specific crop management, (iv) guidelines for conducting an EC"a survey, and (v) current trends and future developments in the application of EC"a to precision agriculture. Unquestionably, EC"a is an invaluable agricultural tool that provides spatial information for soil quality assessment and precision agriculture applications including the delineation of site-specific management units. Technologies such as geo-referenced EC"a measurement techniques have brought precision agriculture from a 1980's concept to a promising tool for achieving sustainable agriculture.

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745 citations


Journal ArticleDOI
David Lamb1, Ralph B. Brown2Institutions (2)
TL;DR: This paper summarizes the work completed to date to investigate the use of airborne remote-sensing for weed mapping in crops, and discusses application of the technology in precision weed management practices.

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Abstract: Airborne remote-sensing has been identified worldwide as a promising technique for identifying and mapping weeds in crops, and potentially offers a solution to the current logjam in precision weed management: namely, the ability to generate timely and accurate weed maps. One of the main advantages of remote-sensing is that synoptic weed data can be acquired virtually instantaneously (within the field of view of the sensor), and a weed map generated within hours of data acquisition. However, because little information is available concerning the scale at which weeds should be managed within fields, the sensing and mapping technology has tended to dictate the resolution at which weeds must be mapped. This paper summarizes the work completed to date to investigate the use of airborne remote-sensing for weed mapping in crops, and discusses application of the technology in precision weed management practices.

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703 citations


Network Information
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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202222
2021453
2020516
2019487
2018413
2017356

Top Attributes

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Topic's top 5 most impactful authors

R. H. Rust

31 papers, 342 citations

Burton C. English

29 papers, 525 citations

W. E. Larson

27 papers, 318 citations

James A. Larson

25 papers, 460 citations

Roland K. Roberts

21 papers, 478 citations