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AgriEngineering
Special Issues
Research Progress and Challenges of Agricultural Information Technology
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Research Progress and Challenges of Agricultural Information Technology

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Remote Sensing in Agriculture".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 3052

Special Issue Editors

Dr. Fatwa Ramdani

E-Mail Website
Guest Editor
Program in Economic and Public Policy, Graduate School of Humanities and Social Sciences, University of Tsukuba, Tsukuba 305-8571, Japan
Interests: geoinformatics; geospatial; applied data science; remote sensing
Dr. Riswan S. Sianturi

E-Mail Website
Guest Editor
Department of Information Systems, Faculty of Computer Science, University of Brawijaya, Malang 65145, Indonesia
Interests: geoinformatics; remote sensing; geographic information systems
Dr. Adi Wibowo

E-Mail Website
Guest Editor
Department of Geography, Faculty of Mathematics and Natural Science, University of Indonesia, Depok, Indonesia
Interests: geography; urban heat; environment

Special Issue Information

Dear Colleagues,

The use of technology in the agricultural industry has seen significant progress in recent years, with new developments in precision farming, data management, and automation. However, the integration of technology into the agricultural sector also presents numerous challenges. This Special Issue focuses on the research progress and challenges of agricultural information technology, and explore the latest developments and innovations in this rapidly evolving field.

The Special Issue on the application of geographic information systems (GIS) and remote sensing technology in agricultural research aims to bring together the latest research and developments in the use of these technologies to advance the field of agriculture.

GIS and remote sensing technology have become increasingly important tools in the study of agriculture and related fields, providing valuable insights and data that can be used to improve crop yield, optimize land use, and monitor the effects of climate change on agricultural systems.

The Special Issue will feature original research articles, review articles, and case studies on the application of GIS and remote sensing technology in agriculture, covering but not limited to the following topics:

  • The use of GIS and remote sensing to develop precision agriculture systems
  • The integration of GIS and remote sensing data with other agricultural data sets
  • The application of GIS and remote sensing in crop monitoring and yield prediction
  • The role of GIS and remote sensing in natural resource management, including water, soil, and biodiversity conservation
  • The use of GIS and remote sensing to study the impacts of climate change on agriculture

The Special Issue will provide a comprehensive overview of the latest research and developments in the application of GIS and remote sensing technology in agricultural research and will be of interest to researchers, practitioners, and policymakers in the field of agriculture and related fields.

Dr. Fatwa Ramdani
Dr. Riswan S. Sianturi
Dr. Adi Wibowo
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 1600 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

  • geographic information system 
  • remote sensing technology 
  • precision agriculture 
  • LiDAR for agriculture 
  • soil mapping 
  • crop monitoring 
  • irrigation management 
  • yield prediction 
  • climate change 
  • satellite imagery 
  • drone technology 
  • geospatial analysis 
  • environmental conservation 
  • sustainable agriculture 
  • food security

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

14 pages, 4118 KiB  
Article
Differentiation of Soybean Genotypes Concerning Seed Physiological Quality Using Hyperspectral Bands
by Izabela Cristina de Oliveira, Dthenifer Cordeiro Santana, Victoria Toledo Romancini, Ana Carina da Silva Cândido Seron, Charline Zaratin Alves, Paulo Carteri Coradi, Carlos Antônio da Silva Júnior, Regimar Garcia dos Santos, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro and Larissa Ribeiro Teodoro
AgriEngineering 2024, 6(4), 4752-4765; https://doi.org/10.3390/agriengineering6040272 - 9 Dec 2024
Viewed by 285
Abstract
The use of summarized spectral data in bands obtained by hyperspectral sensors can make it possible to obtain biochemical information about seeds and, thus, relate the results to seed viability and vigor. Thus, the hypothesis of this work is based on the possibility [...] Read more.
The use of summarized spectral data in bands obtained by hyperspectral sensors can make it possible to obtain biochemical information about seeds and, thus, relate the results to seed viability and vigor. Thus, the hypothesis of this work is based on the possibility of obtaining information about the physiological quality of seeds through hyperspectral bands and distinguishing seed lots regarding their quality through wavelengths. The objective was then to evaluate the possibility of differentiating soybean genotypes regarding the physiological quality of seeds using spectral data. The experiment was conducted during the 2021/2022 harvest at the Federal University of Mato Grosso do Sul in a randomized block design with four replicates and 10 F3 soybean populations (G1, G8, G12, G15, G19, G21, G24, G27, G31, and G36). After the maturation of each genotype, seeds were harvested from the central rows of each plot, which consisted of five one-meter rows. Seed samples from each experimental unit were placed in a Petri dish to collect spectral data. Readings were performed in the laboratory at a temperature of 26 °C and using two 60 W halogen lamps as the light source, positioned 15 cm between the sensor and the sample. The sensor used was the Ocean Optics (Florida, USA) model STS-VIS-L-50-400-SMA, which captured the reflectance of the seed sample at wavelengths between 450 and 824 nm. After readings from the hyperspectral sensor, the seeds were subjected to tests for water content, germination, first germination count, electrical conductivity, and tetrazolium. The data obtained were subjected to an analysis of variance and the means were compared by the Scott–Knott test at 5% probability, analyzed using R software version 4.2.3 (Auckland, New Zealand). The data on the physiological quality of the seeds of the soybean genotypes were subjected to principal component analysis (PCA) and associated with the K-means algorithm to form groups according to the similarity and distinction between the genetic materials. After the formation of these groups, spectral curve graphs were constructed for each soybean genotype and for the groups that were formed. The physiological quality of the soybean genotypes can be differentiated using hyperspectral bands. The spectral bands, therefore, provide important information about the physiological quality of soybean seeds. Through the use of hyperspectral sensors and the observation of specific bands, it is possible to differentiate genotypes in terms of seed quality, complementing and/or replacing traditional tests in a fast, accurate, and non-destructive way, reducing the time and investment spent on obtaining information on seed viability and vigor. The results found in this study are promising, and further research is needed in future studies with other species and genotypes. The interval between 450 and 649 nm was the main spectrum band that contributed to the differentiation between soybean genotypes of superior and inferior physiological quality. Full article
(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)
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24 pages, 5753 KiB  
Article
Phenological Monitoring of Irrigated Sugarcane Using Google Earth Engine, Time Series, and TIMESAT in the Brazilian Semi-Arid
by Diego Rosyur Castro Manrique, Pabrício Marcos Oliveira Lopes, Cristina Rodrigues Nascimento, Eberson Pessoa Ribeiro and Anderson Santos da Silva
AgriEngineering 2024, 6(4), 3799-3822; https://doi.org/10.3390/agriengineering6040217 - 18 Oct 2024
Viewed by 734
Abstract
Monitoring sugarcane phenology is essential since the globalized market requires reliable information on the quantity of raw materials for the industrial production of sugar and alcohol. In this context, the general objective of this study was to evaluate the phenological seasonality of the [...] Read more.
Monitoring sugarcane phenology is essential since the globalized market requires reliable information on the quantity of raw materials for the industrial production of sugar and alcohol. In this context, the general objective of this study was to evaluate the phenological seasonality of the sugarcane varieties SP 79-1011 and VAP 90-212 observed from the NDVI time series over 19 years (2001–2020) from global databases. In addition, this research had the following specific objectives: (i) to estimate phenological parameters (Start of Season (SOS), End of Season (EOS), Length of Season (LOS), and Peak of Season (POS)) using TIMESAT software in version 3.3 applied to the NDVI time series over 19 years; (ii) to characterize the land use and land cover obtained from the MapBiomas project; (iii) to analyze rainfall variability; and (iv) to validate the sugarcane harvest date (SP 79-1011). This study was carried out in sugarcane growing areas in Juazeiro, Bahia, Brazil. The results showed that the NDVI time series did not follow the rainfall in the region. The sugarcane areas advanced over the savanna formation (Caatinga), reducing them to remnants along the irrigation channels. The comparison of the observed harvest dates of the SP 79-1011 variety to the values estimated with the TIMESAT software showed an excellent fit of 0.99. The mean absolute error in estimating the sugarcane harvest date was approximately ten days, with a performance index of 0.99 and a correlation coefficient of 0.99, significant at a 5% confidence level. The TIMESAT software was able to estimate the phenological parameters of sugarcane using MODIS sensor images processed on the Google Earth Engine platform during the evaluated period (2001 to 2020). Full article
(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)
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13 pages, 1965 KiB  
Article
Geospatial Approach to Determine Nitrate Values in Banana Plantations
by Angélica Zamora-Espinoza, Juan Chin, Adolfo Quesada-Román and Veda Obando
AgriEngineering 2024, 6(3), 2513-2525; https://doi.org/10.3390/agriengineering6030147 - 1 Aug 2024
Viewed by 1059
Abstract
Banana (Musa sp.) is one of the world’s most planted and consumed crops. Analysis of plantations using a geospatial perspective is growing in Costa Rica, and it can be used to optimize environmental analysis. The aim of this study was to propose [...] Read more.
Banana (Musa sp.) is one of the world’s most planted and consumed crops. Analysis of plantations using a geospatial perspective is growing in Costa Rica, and it can be used to optimize environmental analysis. The aim of this study was to propose a methodology to identify areas prone to water accumulation to quantify nitrate concentrations using geospatial modeling techniques in a 40 ha section of a banana plantation located in Siquirres, Limón, Costa Rica. A total of five geomorphometric variables (Slope, Slope Length factor (LS factor), Terrain Ruggedness Index (TRI), Topographic Wetness Index (TWI), and Flow Accumulation) were selected in the geospatial model. A 9 cm resolution digital elevation model (DEM) derived from unmanned aerial vehicles (UAVs) was employed to calculate geomorphometric variables. ArcGIS 10.6 and SAGA GIS 7.8.2 software were used in the data integration and analysis. The results showed that Slope and Topographic Wetness Index (TWI) are the geomorphometric parameters that better explained the areas prone to water accumulation and indicated which drainage channels are proper areas to sample nitrate values. The average nitrate concentration in high-probability areas was 8.73 ± 1.53 mg/L, while in low-probability areas, it was 11.28 ± 2.49 mg/L. Despite these differences, statistical analysis revealed no significant difference in nitrate concentrations between high- and low-probability areas. The method proposed here allows us to obtain reliable results in banana fields worldwide. Full article
(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)
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