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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 2026 | Viewed by 8755

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Special Issue Editors

Dr. Fatwa Ramdani

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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

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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

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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

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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 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 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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Published Papers (11 papers)

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Research

19 pages, 42632 KiB

Open AccessArticle

Correlation Between the Growth Index and Vegetation Indices for Irrigated Soybeans Using Free Orbital Images

by Gildriano Soares de Oliveira, Jackson Paulo Silva Souza, Érica Pereira Cardozo, Dhiego Gonçalves Pacheco, Marinaldo Loures Ferreira, Marcelo Coutinho Picanço, João Rafael Silva Soares, Ana Maria Oliveira Souza Alves, André Medeiros de Andrade and Ricardo Siqueira da Silva

AgriEngineering 2025, 7(3), 67; https://doi.org/10.3390/agriengineering7030067 - 5 Mar 2025

Viewed by 162

Abstract

Soybeans are key in generating foreign currency for the world economy. Geotechnologies, through vegetation indices (VIs) generated by orbital images or remotely piloted aircraft, are essential tools for assessing the impact of climate on productivity and the ecoclimatic suitability of crops. This study aimed to correlate the growth indices from the CLIMEX model, previously validated, with VIs derived from orbital remote sensing and ecological niche modeling for soybean cultivation in six irrigated pivots located in the northwest of Minas Gerais, Brazil. The maximum normalized difference vegetation index (NDVImax) and the maximum soil-adjusted vegetation index (SAVImax) were extracted from Landsat-8 OLI/TIRS sensor images for the 2016 to 2019 harvests during the R1 to R3 phenological stages. The maximum NDVI values varied across the study regions and crops, ranging from 0.27 to 0.95. Similarly, SAVI values exhibited variability, with the maximum SAVI ranging from 0.13 to 0.85. The growth index (GI_w), derived from the CLIMEX model, ranged from 0.88 to 1. The statistical analysis confirmed a significant correlation (p < 0.05) between NDVImax and GI_w only for the 2018/19 harvest, with a Pearson correlation coefficient of r = 0.86, classified as very strong. Across all harvests, NDVI consistently outperformed SAVI in correlation strength with GI_w. Using geotechnologies through remote sensing shows promise for correlating spectral indices and climate suitability models. However, when using a valid model, all crops did not correlate. Still, our study has the potential to be improved by investigating new hypotheses, such as using drone images with better resolution (spatial, spectral, temporal, and radiometric) and adjusting the response of soybean vegetation indices and the phenological stage. Our results correlating the CLIMEX model of growth indices with vegetation indices have the potential for monitoring soybean cultivation and analyzing the performance of varieties but require a more in-depth view to adapt the methodology. Full article

(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)

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36 pages, 66814 KiB

Open AccessArticle

Characterization of Irrigated Rice Cultivation Cycles and Classification in Brazil Using Time Series Similarity and Machine Learning Models with Sentinel Imagery

by Andre Dalla Bernardina Garcia, Ieda Del’Arco Sanches, Victor Hugo Rohden Prudente and Kleber Trabaquini

AgriEngineering 2025, 7(3), 65; https://doi.org/10.3390/agriengineering7030065 - 4 Mar 2025

Viewed by 150

Abstract

The mapping and monitoring of rice fields on a large scale using medium and high spatial resolution data (<10 m) is essential for efficient agricultural management and food security. However, challenges such as managing large volumes of data, addressing data gaps, and optimizing available data are key focuses in remote sensing research using automated machine learning models. In this sense, the objective of this study was to propose a pipeline to characterize and classify three different irrigated rice-producing regions in the state of Santa Catarina, Brazil. To achieve this, we used Sentinel-1 Synthetic Aperture Radar (SAR) polarizations and Sentinel-2 optical multispectral spectral bands along with multiple time series indices. The processing of input data and exploratory analysis were performed using a clustering algorithm based on Dynamic Time Warping (DTW), with K-means applied to the time series. For the classification step in the proposed pipeline, we utilized five traditional machine learning models available on the Google Earth Engine platform to determine which had the best performance. We identified four distinct irrigated rice cropping patterns across Santa Catarina, where the northern region favors double cropping, the south predominantly adopts single cropping, and the central region shows both, a flattened single and double cropping. Among the tested classification models, the SVM with Sentinel-1 and Sentinel-2 data yielded the highest accuracy (IoU: 0.807; Dice: 0.885), while CART and GTBoost had the lowest performance. Omission errors were reduced below 10% in most models when using both sensors, but commission errors remained above 15%, especially for patches in which rice fields represent less than 10% of area. These findings highlight the effectiveness of our proposed feature selection and classification pipeline for improving the generalization of irrigated rice mapping in large and diverse regions. Full article

(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)

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37 pages, 80118 KiB

Open AccessArticle

Integrating Statistical and Earth AgriData in Small Farming Systems for Food Security

by Theodore Tsiligiridis and Katerina Ainali

AgriEngineering 2025, 7(3), 54; https://doi.org/10.3390/agriengineering7030054 - 21 Feb 2025

Viewed by 356

Abstract

The present work unveils the role of small farming plots (less than 5 ha) in the context of food security. It determines their contribution by estimating the spatial distribution (location), the crop types (diversity), the crop area extent (acreage), and the yield (production), factors that remain unclear, mainly because the official statistical offices rarely include them in surveys. The development introduces a novel RS-based approach that fulfills this gap. It provides stakeholders with the appropriate tools to accurately and timely acquire crop type map information and objectively quantify their crop production capabilities. Approaches based on the Land Parcel Identification System (LPIS) of the Integrated Administration and Control System (IACS) applied by many countries in Europe are proved useful in providing information on location, diversity, and acreage but not crop production per farm owner applying and eligible for receiving subsidies. The developed RS approach is implemented in twenty European NUTS-3 regions and one in Africa. Nevertheless, in this research, we focus on its development, testing, and evaluation in three pilot prefectures of Greece, producing the corresponding land cover maps. Notably, the unbiased crop area computation and the crop production estimates are performed only for the highly accurate key crop products (per crop type classification, FScore > 75%), considering that the key crop production estimations are obtained by combining the key crop areas with the field-level yields provided by the key informant surveys. The above choice ensures that the estimation of crop production will be derived only for the best-classified crops per reference region. The RS approach reduces the error propagation when estimating the area and production of the crop types that are classified with low or very low accuracy levels. These levels could reduce the strength of the overall conclusions about the main contributions of small farming plots. Potential changes occurring in the key crop cultivations of small farming plots are also estimated and mapped using the LPIS geodatabase. Under various environmental and territorial conditions, the results of the RS approach show good classification accuracies for several key crops per reference region. Their integration with the existing official statistical data and those derived from the LPIS geodatabase shows the consistency and significant contribution in estimating all the factors needed to determine the small farming plots. Finally, the applied innovative integrated approach can be expanded beyond the Greek case to cover other regions with various agricultural practices. Full article

(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)

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24 pages, 5440 KiB

Open AccessArticle

Environmental Covariates for Sampling Optimization and Pest Prediction in Soybean Crops

by Cenneya Lopes Martins, Maiara Pusch, Wesley Augusto Conde Godoy and Lucas Rios do Amaral

AgriEngineering 2025, 7(1), 21; https://doi.org/10.3390/agriengineering7010021 - 18 Jan 2025

Viewed by 784

Abstract

Insect pest infestations can vary due to spatial differences in microclimates and food availability within agroecosystems. Covariates can reflect these environmental conditions. This study tested whether using environmental covariates in two-phase sample optimization improved the spatial predictions for soybean insect pests. During the 2021–2022 crop season, insect pest samples were collected at 50 georeferenced points in a commercial soybean field in Brazil, alongside data on environmental covariates such as vegetation indices, soil properties, terrain topography, and distances from riparian areas. Three covariates were selected using correlation and principal component analysis (PCA). In the 2022–2023 crop season, sample designs were optimized using the iterative algorithm optimization of sample configurations using spatial simulated annealing (SPSANN) using the selected covariates, resulting in two optimized designs that were compared to a regular grid. Data from the three sampling designs comprising 50 points were evaluated using geostatistical methods, regression analysis (pest abundance), and classification (pest presence or absence) via the random forest algorithm. The data showed no spatial dependence, making using geostatistical interpolators inappropriate. However, a multi-objective optimized sampling design, tailored to refine configurations for identifying and estimating variograms and spatial trends essential for spatial interpolation, produced the most accurate predictions. Therefore, a two-phase sample optimization with prior in situ selection of environmental covariates improves pest predictions in agricultural systems, contributing to more efficient and sustainable agricultural management. Full article

(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)

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16 pages, 2567 KiB

Open AccessArticle

Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes

by Michel E. D. Chaves, Lívia G. D. Soares, Gustavo H. V. Barros, Ana Letícia F. Pessoa, Ronaldo O. Elias, Ana Claudia Golzio, Katyanne V. Conceição and Flávio J. O. Morais

AgriEngineering 2025, 7(1), 19; https://doi.org/10.3390/agriengineering7010019 - 17 Jan 2025

Viewed by 708

Abstract

The conflict between environmental conservation and agricultural production highlights the need for precise land use and land cover (LULC) mapping to support agro-environmental-related policies. Satellite image time series from the Moderate Resolution Image Spectroradiometer (MODIS) sensor are essential for current LULC mapping efforts. However, most approaches focus on pixel data, and studies exploring object-based spatiotemporal heterogeneity and correlation features in its time series are limited. The objective of this study is to mix the data cube architecture (analysis-ready data—ARD) and the geo-object-oriented time series segmentation via Geographic Object-Based Image Analysis (GEOBIA) to assess its performance in identifying natural vegetation and double-cropping practices over a crop season. The study area was the state of Mato Grosso, Brazil. Results indicate that, by combining GEOBIA and time series analysis (materialized by the multiresolution segmentation algorithm to derive spatiotemporal geo-objects of the MODIS data cube), representative training data collected after a quality control process, and the Support Vector Machine to classify the ARD, the overall accuracy was 0.95 and all users’ and producers’ accuracies were higher than 0.88. By considering the heterogeneity of Mato Grosso’s landscape, the results indicate the potential of the approach to provide accurate mapping. Full article

(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)

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15 pages, 4935 KiB

Open AccessArticle

RGB Composition Obtained by a UAV in the Monitoring of Sugarcane Row Gaps Using the Biophysical Index

by Camila G. B. de Melo, Mário M. Rolim, Roberta Q. Cavalcanti, Marcos V. da Silva, Ana Lúcia B. Candeias, Pabrício M. O. Lopes, Pedro F. S. Ortiz and Renato P. de Lima

AgriEngineering 2025, 7(1), 17; https://doi.org/10.3390/agriengineering7010017 - 15 Jan 2025

Viewed by 612

Abstract

Sugarcane crops have a long cycle with successive harvests before re-planting, and row gaps are one of the main problems associated with the yield. The objective of this study was to establish an alternative methodology for measuring the planting and regrowth of sugarcane rows using UAV (Unmanned Aerial Vehicle) images and to compare it with manual measurements. This study was conducted in a 1 ha experimental area under mechanized harvesting. The reference methodology consists of measuring the continuous distances without regrowth between two plants along a planting row, considering distances greater than 0.50 m as gaps and the following gaps classes: >0.5–1.0 m, >1.0–1.5 m, >1.5–2.0 m, >2.0–3.5 m, and >3.5 m. Images were collected from a UAV equipped with a 12-megapixel RGB camera. The number of regrowth gaps measured through imaging for the class of gaps with a length between 0.5 and 1.0 m was eight times higher than field measurement. In the class of gaps with a length between 1.0 and 1.5 m, the result is the opposite, as the field measurement was approximately three times higher than the UAV measurement, with a significant difference in both classes. In the other length classes analyzed, the number of gaps did not show significant differences. Our results suggest that regrowth gaps can be quickly estimated with the proposed methodology for gaps greater than 1.5 m. For gaps smaller than <1 m, the methodology using a UAV is not accurate. Full article

(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)

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16 pages, 7829 KiB

Open AccessArticle

Fusion of Remotely Sensed Data with Monitoring Well Measurements for Groundwater Level Management

by César de Oliveira Ferreira Silva, Rodrigo Lilla Manzione, Epitácio Pedro da Silva Neto, Ulisses Alencar Bezerra and John Elton Cunha

AgriEngineering 2025, 7(1), 14; https://doi.org/10.3390/agriengineering7010014 - 9 Jan 2025

Viewed by 674

Abstract

In the realm of hydrological engineering, integrating extensive geospatial raster data from remote sensing (Big Data) with sparse field measurements offers a promising approach to improve prediction accuracy in groundwater studies. In this study, we integrated multisource data by applying the LMC to model the spatial relationships of variables and then utilized block support regularization with collocated block cokriging (CBCK) to enhance our predictions. A critical engineering challenge addressed in this study is support homogenization, where we adjusted punctual variances to block variances and ensure consistency in spatial predictions. Our case study focused on mapping groundwater table depth to improve water management and planning in a mixed land use area in Southeast Brazil that is occupied by sugarcane crops, silviculture (Eucalyptus), regenerating fields, and natural vegetation. We utilized the 90 m resolution TanDEM-X digital surface model and STEEP (Seasonal Tropical Ecosystem Energy Partitioning) data with a 500 m resolution to support the spatial interpolation of groundwater table depth measurements collected from 56 locations during the hydrological year 2015–16. Ordinary block kriging (OBK) and CBCK methods were employed. The CBCK method provided more reliable and accurate spatial predictions of groundwater depth levels (RMSE = 0.49 m), outperforming the OBK method (RMSE = 2.89 m). An OBK-based map concentrated deeper measurements near their wells and gave shallow depths for most of the points during estimation. The CBCK-based map shows more deeper predicted points due to its relationship with the covariates. Using covariates improved the groundwater table depth mapping by detecting the interconnection of varied land uses, supporting the water management for agronomic planning connected with ecosystem sustainability. Full article

(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)

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19 pages, 3692 KiB

Open AccessArticle

Hierarchical Stratification for Spatial Sampling and Digital Mapping of Soil Attributes

by Derlei D. Melo, Isabella A. Cunha and Lucas R. Amaral

AgriEngineering 2025, 7(1), 10; https://doi.org/10.3390/agriengineering7010010 - 2 Jan 2025

Viewed by 851

Abstract

This study assessed whether stratifying agricultural areas into macro- and micro-variability regions allows targeted sampling to better capture soil attribute variability, thus improving digital soil maps compared to regular grid sampling. Allocating more samples where soil variability is expected offers a promising alternative. We evaluated two sampling densities in two agricultural fields in Southeast Brazil: a sparse density (one sample per 2.5 hectares), typical in Precision Agriculture, and a denser grid (one sample per hectare), which usually provides reasonable mapping accuracy. For each density, we applied three designs: a regular grid and grids with 25% and 50% guided points. Apparent soil magnetic susceptibility (MSa) delimited macro-homogeneity zones, while Sentinel-2’s Enhanced Vegetation Index (EVI) identified micro-homogeneity, guiding sampling to pixels with higher Fuzzy membership. The attributes assessed included phosphorus (P), potassium (K), and clay content. Results showed that the 50% guided sample configuration improved ordinary kriging interpolation accuracy, particularly with sparse grids. In the six sparse grid scenarios, in four of them, the grid with 50% of the points in regular design and the other 50% directed by the proposed method presented better performance than the full regular grid; the higher improvement was obtained for clay content (RMSE of 54.93 g kg⁻¹ to 45.63 g kg⁻¹, a 16.93% improvement). However, prior knowledge of soil attributes and covariates is needed for this approach. We therefore recommend two-stage sampling to understand soil properties’ relationships with covariates before applying the proposed method. Full article

(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)

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14 pages, 4118 KiB

Open AccessArticle

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 685

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 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

Open AccessArticle

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 1093

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 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

Open AccessArticle

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 1245

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 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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