Report from the conference, ‘identifying obstacles to applying big data in agriculture’

White, Emma L.; Thomasson, J. Alex; Auvermann, Brent W.; Kitchen, Newell R.; Pierson, Leland S.; Porter, Dana; Baillie, Craig; Hamann, Hendrik F.; Hoogenboom, Gerrit; Janzen, Todd; Khosla, Rajiv; Lowenberg‐DeBoer, James; McIntosh, Matt; Murray, Seth C.; Osborn, Dave; Shetty, Ashoo; Stevenson, Craig; Tevis, Joe W.; Werner, Fletcher

doi:10.1007/s11119-020-09738-y

Cited by 23 publications

(19 citation statements)

References 4 publications

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“…There is large demand for sensor techniques that provide consistent, reliable, and inexpensive information on soil properties (Lobsey et al., 2017; White et al., 2021). An application that relies on such soil data is precision agriculture (PA).…”

Section: Introductionmentioning

confidence: 99%

A LUCAS‐based mid‐infrared soil spectral library: Its usefulness for soil survey and precision agriculture#

Leenen

Pätzold

Tóth

et al. 2022

J. Plant Nutr. Soil Sci.

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Background Mid‐infrared spectroscopy (MIRS) is commonly recognized as a rapid and high throughput measurement technology for numerous soil properties, given that appropriate prediction models are calibrated. Soil spectral libraries (SSL) may reduce effort and costs for MIRS practical application. Aims To calibrate MIR‐SSL‐based prediction models for soil properties and to test their applicability to independent sample sets at regional scale (e.g., for soil survey) and at field scale (i.e., for precision agriculture, PA). Methods Spectra of 1013 arable topsoil samples of the European Land Use/Land Cover Area Frame Survey 2009 (LUCAS) from Belgium, the Netherlands, Luxembourg, and Germany formed the basis for the MIR‐SSL. Leave‐one‐out cross‐validation (LOOCV) via partial least squares regression served to calibrate (1) generic prediction models including all samples, and (2) stratified models for different parent materials. Test‐set validation (TSV) was conducted on samples from independent campaigns at (1) regional scale with a sample set from Schleswig‐Holstein (Germany; n = 385) and (2) field scale for four individual fields in Germany (n = 513). Results Generic LOOCV models successfully predicted soil organic carbon, total nitrogen, sand, silt, clay, carbonate, and pH. Calibration for available nutrients failed. The TSV was successful for the regional sample set for all variables (2.5 ≤ RPIQ ≤ 5.9), except for carbonate (RPIQ = 0). At field scale, the validation was highly variable for different sites and parameters. Stratified models using soil parent material as auxiliary variable improved only occasionally the applicability at field scale, that is, on single fields and only for clay and carbonate. Conclusions Although the MIR‐SSL in its present state cannot be recommended for nutrient management, it provides valuable support for soil survey and PA.

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Section: Introductionmentioning

confidence: 99%

A LUCAS‐based mid‐infrared soil spectral library: Its usefulness for soil survey and precision agriculture#

Leenen

Pätzold

Tóth

et al. 2022

J. Plant Nutr. Soil Sci.

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“…White et al ( 2021) conducted a survey with researchers participating in a conference on Precision Farming to identify the challenges in different scenarios where Agricultural Big Data is used: (1) mid-season yield prediction for real-time decision-making, (2) sow lameness, (3) irrigation in cotton management, (4) in-season decision making, (5) policymaker perspective, (6) cropping selection system, (7) business analytics for agriculture, (8) grower perspective, (9) consumer perspective, (10) benchmarking scenario-comparing individual grower yields to modeled outputs based on other people's data [2]. The challenges indicated in these scenarios are: error in the data, inaccessibility, unusability, incompatibility, and inconvenience.…”

Section: Challenges In Agricultural Big Data and MLmentioning

confidence: 99%

“…In addition, sensor data need calibration. Finally, they indicate that better representations of crop growth models are required as well as more specific weather forecasts for individual farms and fields [2].…”

Section: Challenges In Agricultural Big Data and MLmentioning

confidence: 99%

“…Because of this increase, food production per hectare needs to double by the time the world population stabilizes around 2100 (United Nations 2019). Food security is a fundamental global need, which is threatened by several factors such as population growth, shrinking arable land, climate change, food waste, and consumer preference for animal protein [2]. Increasing agriculture or food production rapidly to meet the growing demand for food supply is not an easy task.…”

Section: Introductionmentioning

confidence: 99%

“…The implementation of Agricultural Big Data involves a set of challenges to consider. From the technical point of view in the implementation, White et al (2021) explain the challenges in the data, inaccessibility, unusability, incompatibility, inconvenience, lack of data interoperability, lack of rural bandwidth, lack of data calibration, and lack of representation of crop growth models and weather forecasts [2]. Lassoued et al (2021) analyzed the impact and potential of Big Data in agriculture.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

Cravero¹,

Pardo²,

Sepúlveda³

et al. 2022

Preprint

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Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address different problems such as farmers' decision-making, crops, weeds, animal research, land, food availability and security, weather, and climate change. The purpose of this paper is to synthesize the evidence regarding the challenges involved in implementing machine learning in Agricultural Big Data. We conducted a Systematic Literature Review applying the PRISMA protocol. This review includes 30 papers, published from 2015 to 2020. We develop a framework that summarizes the main challenges encountered, the use of machine learning techniques, as well as the main technologies used. A major challenge is the design of Agricultural Big Data architectures, due to the need to modify the set of technologies adapting the machine learning techniques, as the volume of data increases.

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Protecting farm privacy while researching large‐scale unmanned aircraft systems platforms for agricultural applications

et al. 2022

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The objective of this paper is to explore data privacy and sharing challenges associated with conducting regional data collection using large-scale unmanned aircraft systems (UAS) technologies. During the 2016 growing season, the North Dakota State University Extension Service and Elbit Systems of America conducted a proofof-concept UAS Research and Development (R&D) project to use large-scale UAS platforms to address some of the issues limiting UAS technology adoption. While the researchers initially thought that the high-resolution spatial and temporal field imagery collected would be well received by area farmers, the team learned that some had concerns over farm operations privacy and about who would have access to the data. The team conducted multiple stakeholder meetings to address the issues raised.While the flights were successfully executed, problems related to farmer interest in the data collected and how to get that data to farmers were identified. Though the project demonstrated how third party operation of large-scale UAS can remove the operational burden from farmers, the project introduced additional privacy concerns and highlighted the need for better rural broadband to make grower access to data in the cloud practical.

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Report from the conference, ‘identifying obstacles to applying big data in agriculture’

Cited by 23 publications

References 4 publications

A LUCAS‐based mid‐infrared soil spectral library: Its usefulness for soil survey and precision agriculture#

A LUCAS‐based mid‐infrared soil spectral library: Its usefulness for soil survey and precision agriculture#

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

Protecting farm privacy while researching large‐scale unmanned aircraft systems platforms for agricultural applications

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