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Agriculture
Special Issues
Precision Remote Sensing and Information Detection in Agriculture
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Precision Remote Sensing and Information Detection in Agriculture

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 10 May 2024 | Viewed by 912

Special Issue Editors

Dr. Tongxi Hu

E-Mail Website
Guest Editor
Institute for Sustainability Energy and Environment, University of Illinois, Urbana, IL 61801, USA
Interests: agroecosystem modeling; remote sensing; machine learning; digital agriculture
Prof. Dr. Kebiao Mao

E-Mail Website
Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: artificial intelligence; deep learning; retrieval paradigm; soil moisture retrieval; land surface temperature retrieval; water vapor content retrieval; near surface temperature retrieval
Special Issues, Collections and Topics in MDPI journals
Dr. Kaiguang Zhao

E-Mail Website
Guest Editor
Ohio Agricultural Research and Development Center, School of Environment and Natural Resources, The Ohio State University, Wooster, OH 44691, USA
Interests: carbon monitoring; ecosystem structure and functioning; land dynamics; lidar; ecological modeling; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture is facing daunting challenges imposed by the increasing global population, natural resource scarcity, and climate change. Yet, there are unprecedented opportunities for the future, including the remarkable emergence of innovations in technological advances, such as precision remote sensing, which will help optimize agricultural management and thus improve agricultural sustainability.

Pivotal technologies for data collection, including airborne sensing, Unmanned Aerial Vehicles (UAV), real-time kinematics (RTK), and global positioning systems (GPS), are being used to monitor yields, weeds, chemical (herbicides, insecticides, and fertilizers) use etc. The collected data can influence farmer decisions with respect to seeding, fertilizer and chemical applications, irrigation scheduling, and other farm input use, which could lead to economic savings on farms and reduce the impact on the environment.

This Special Issue aims to cover a wide range of data collection approaches, such as UAVs (also known as drones), to monitor croplands and thus optimize management practices so that outcomes are robust and resource-efficient.

Dr. Tongxi Hu
Prof. Dr. Kebiao Mao
Dr. Kaiguang Zhao
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. Agriculture is an international peer-reviewed open access monthly 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 2600 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.

Keywords

  • precision farming
  • UAV
  • IoT
  • smart agriculture
  • digital agriculture
  • remote sensing
  • data fusion

Published Papers (1 paper)

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Research

20 pages, 5864 KiB  
Article
UAV-Based Vegetation Indices to Evaluate Coffee Crop Response after Transplanting Seedlings Grown in Different Containers
by Rafael Alexandre Pena Barata, Gabriel Araújo e Silva Ferraz, Nicole Lopes Bento, Lucas Santos Santana, Diego Bedin Marin, Drucylla Guerra Mattos, Felipe Schwerz, Giuseppe Rossi, Leonardo Conti and Gianluca Bambi
Agriculture 2024, 14(3), 356; https://doi.org/10.3390/agriculture14030356 - 23 Feb 2024
Viewed by 656
Abstract
Brazil stands out among coffee-growing countries worldwide. The use of precision agriculture to monitor coffee plants after transplantation has become an important step in the coffee production chain. The objective of this study was to assess how coffee plants respond after transplanting seedlings [...] Read more.
Brazil stands out among coffee-growing countries worldwide. The use of precision agriculture to monitor coffee plants after transplantation has become an important step in the coffee production chain. The objective of this study was to assess how coffee plants respond after transplanting seedlings grown in different containers, based on multispectral images acquired by Unmanned Aerial Vehicles (UAV). The study was conducted in Santo Antônio do Amparo, Minas Gerais, Brazil. The coffee plants were imaged by UAV, and their height, crown diameter, and chlorophyll content were measured in the field. The vegetation indices were compared to the field measurements through graphical and correlation analysis. According to the results, no significant differences were found between the studied variables. However, the area transplanted with seedlings grown in perforated bags showed a lower percentage of mortality than the treatment with root trainers (6.4% vs. 11.7%). Additionally, the vegetation indices, including normalized difference red-edge, normalized difference vegetation index, and canopy planar area calculated by vectorization (cm2), were strongly correlated with biophysical parameters. Linear models were successfully developed to predict biophysical parameters, such as the leaf area index. Moreover, UAV proved to be an effective tool for monitoring coffee using this approach. Full article
(This article belongs to the Special Issue Precision Remote Sensing and Information Detection in Agriculture)
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