Special Issue "Digital Agriculture: Latest Advances and Prospects"

A special issue of AgriEngineering (ISSN 2624-7402).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 3990

Special Issue Editors

Dr. Alireza Pourreza
E-Mail Website
Guest Editor
Department of Biological and Agricultural Engineering, University of California, Davis, CA, USA
Interests: precision agriculture/horticulture; agricultural mechanization; remote sensing and GIS; machine vision; agricultural robotics and automation; big data; hyperspectral/multispectral imaging
Dr. Hao Gan
E-Mail Website
Guest Editor
Biosystems Engineering and Soil Sciences, The University of Tennessee, Knoxville, TN, USA
Interests: sensors; smart agriculture; agricultural robotics; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Future agricultural and food production systems must make better use of limited resources to ensure that farmers can economically produce more high quality food for the growing world population while minimizing the impact on the environment. The current food production practices are addressing challenges such as climate change; crop loss due to plant stress, drought, invasive pests and diseases; inefficient management; and postharvest waste. Data-driven agriculture enables farmers to increase productivity, adapt to the changing climate, and reduce the risk of environmental degradation. Digital agriculture integrates advanced technologies to enhance agricultural production systems. It includes cutting-edge techniques to acquire and convert data to practical knowledge using validated interpretation models, and exploit this knowledge as decision-support tools.

This Special Issue is aimed at gathering recent developments related to digital agriculture and artificial intelligence with respect to its potential capabilities when used in agricultural applications. Contributions could include, but are not limited to, the following areas:

  • Digital agriculture
  • Smart farming
  • Remote sensing
  • Proximity sensing
  • Artificial intelligence
  • Data analytics and interpretation
  • Spectral data analytics
  • 3D point cloud analysis
  • Thermography
  • Decision support tools
  • Data calibration
  • Internet of things
  • Prescription map
  • Precision agriculture
  • Big data
  • Management zone
  • Data management
  • In situ sensing

Dr. Alireza Pourreza
Dr. Hao Gan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • digital agriculture
  • smart farming
  • remote sensing
  • proximity sensing
  • artificial intelligence
  • spectral data analytics
  • 3D point cloud analysis
  • thermography
  • decision support tools
  • data calibration
  • internet of things
  • prescription map
  • precision agriculture
  • big data
  • management zone
  • data management
  • in situ sensing

Published Papers (1 paper)

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Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands
AgriEngineering 2020, 2(4), 523-543; https://doi.org/10.3390/agriengineering2040035 - 05 Nov 2020
Cited by 11 | Viewed by 3515
The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV)/drones to map both pasture quantity as biomass yield and pasture quality as the proportions of key pasture nutrients, across a [...] Read more.
The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV)/drones to map both pasture quantity as biomass yield and pasture quality as the proportions of key pasture nutrients, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed machine learning based predictive models of both pasture measures. UAV-based structure from motion photogrammetry provided a measure of yield from overlapping high resolution visible colour imagery. Pasture nutrient composition was estimated from the spectral signatures of visible near infrared hyperspectral UAV sensing. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of yield and on average indicated an error of 0.8 t ha−1 in grasslands and 1.3 t ha−1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale; however, further research is needed, in particular further field sampling of both yield and nutrient elements across such a diverse landscape, with the potential to scale up to a satellite platform for broader scale monitoring. Full article
(This article belongs to the Special Issue Digital Agriculture: Latest Advances and Prospects)
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