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AgriEngineering
Special Issues
Novel Approaches for Unmanned Aerial Vehicle
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Novel Approaches for Unmanned Aerial Vehicle

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

Deadline for manuscript submissions: closed (1 February 2022) | Viewed by 16806

Special Issue Editors

Prof. Dr. Gabriel Araújo e Silva Ferraz

E-Mail Website
Guest Editor
Department of Agricultural Engineering, School of Engineering, Federal University of Lavras—UFLA, P.O. Box 3037, Lavras 37200-900, Brazil
Interests: remote sensing; UAV in agriculture and livestock; digital and precision farming and livestock
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Rossi

E-Mail Website
Guest Editor
Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura 13, 50145 Florence, Italy
Interests: topography; remote sensing; environmental monitoring and analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, precision agriculture and digital agriculture have been increasing exponentially. One of the distinguishing technologies in these fields is the unmanned aerial vehicle (UAV). The UAV promotes a new view of the field. The greatest power of this technology is to collect precision data that enable farmers, researchers, and rural professionals to understand the studied field better, while having this information in real time or in a relatively short time of processing data.
They could be used to identify crop diseases, pest attack, weed outbreaks, unhealth areas in the paddock, animals’ diseases, animals’ behaviors, wild animals’ traffic and behavior on the farm, and so on. Moreover, UAV can use LiDAR or photogrammetric systems that can be used to identify the topography of the farm, define better crop fields, define the mechanizable areas, define the crop rows, calculate areas, calculate volumes, and have also been used to create 3D images of the landscape and buildings to plan the management of the farms and evaluate the environmental impacts. Other UAV applications, such as spraying, seeding and GHG gas detection, are becoming more used in farms and must be studied more.
The use of UAV is now being related to the big data collection, data science, deep learning, machine learning, artificial intelligence, and other smart technologies. As can be seen, the UAV in a farm presents many applications, and everyday new uses and technologies are being developed in this field. This Special Issue is focused on the development in the field of novel UAV uses, applications, advances, data collection, data work, and in all of the themes related to this rapidly improving field.
We proudly invite the community of scholars to submit their research from across the spectrum of novel approaches for unmanned aerial vehicles in the area of agriculture, livestock, forestry, rural buildings, rural topography.
Contributions could include, but are not limited to, UAV applied in agriculture, UAV applied in livestock, UAV applied in rural buildings, UAV applied in forestry, UAV spraying applications, UAV seeding applications, UAV LiDAR and photogrammetric systems, remote sensing from UAV images, multispectral imagery, weeds, pests and diseases detection from UAV, spectral data analytics, image processing, vegetation indices, thermal imagery, GHG gas detection from UAV and UAV design for agricultural uses.

Dr. Gabriel A.e.S. Ferraz
Dr. Giuseppe Rossi
Guest Editors

 

Keywords

  • UAV applied in agriculture
  • UAV applied in livestock
  • UAV applied in rural buildings
  • UAV applied in forestry
  • UAV spraying applications
  • UAV seeding applications
  • UAV LiDAR and photogrammetric systems
  • Remote sensing from UAV images
  • Multispectral imagery
  • Weeds, pest and diseases detection from UAV
  • Spectral data analytics
  • Images processing
  • Vegetation indices
  • Thermal imagery
  • GHG gas detection from UAV
  • UAV design for agricultural uses

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Published Papers (5 papers)

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Research

9 pages, 2657 KiB  
Article
Vegetation Indices Applied to Suborbital Multispectral Images of Healthy Coffee and Coffee Infested with Coffee Leaf Miner
by Luana Mendes dos Santos, Gabriel Araújo e Silva Ferraz, Diego Bedin Marin, Milene Alves de Figueiredo Carvalho, Jessica Ellen Lima Dias, Ademilson de Oliveira Alecrim and Mirian de Lourdes Oliveira e Silva
AgriEngineering 2022, 4(1), 311-319; https://doi.org/10.3390/agriengineering4010021 - 17 Mar 2022
Cited by 10 | Viewed by 3011
Abstract
The coffee leaf miner (Leucoptera coffeella) is a primary pest for coffee plants. The attack of this pest reduces the photosynthetic area of the leaves due to necrosis, causing premature leaf falling, decreasing the yield and the lifespan of the plant. [...] Read more.
The coffee leaf miner (Leucoptera coffeella) is a primary pest for coffee plants. The attack of this pest reduces the photosynthetic area of the leaves due to necrosis, causing premature leaf falling, decreasing the yield and the lifespan of the plant. Therefore, this study aims to analyze vegetation indices (VI) from images of healthy coffee leaves and those infested by coffee leaf miner, obtained using a multispectral camera, mainly to differentiate and detect infested areas. The study was conducted in two distinct locations: At a farm, where the camera was coupled to a remotely piloted aircraft (RPA) flying at a 3 m altitude from the soil surface; and the second location, in a greenhouse, where the images were obtained manually at a 0.5 m altitude from the support of the plant vessels, in which only healthy plants were located. For the image processing, arithmetic operations with the spectral bands were calculated using the “Raster Calculator” obtaining the indices NormNIR, Normalized Difference Vegetation Index (NDVI), Green-Red NDVI (GRNDVI), and Green NDVI (GNDVI), the values of which on average for healthy leaves were: 0.66; 0.64; 0.32, and 0.55 and for infested leaves: 0.53; 0.41; 0.06, and 0.37 respectively. The analysis concluded that healthy leaves presented higher values of VIs when compared to infested leaves. The index GRNDVI was the one that better differentiated infested leaves from the healthy ones. Full article
(This article belongs to the Special Issue Novel Approaches for Unmanned Aerial Vehicle)
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9 pages, 14993 KiB  
Article
Estimate and Temporal Monitoring of Height and Diameter of the Canopy of Recently Transplanted Coffee by a Remotely Piloted Aircraft System
by Nicole Lopes Bento, Gabriel Araújo e Silva Ferraz, Rafael Alexandre Pena Barata, Daniel Veiga Soares, Lucas Santos Santana and Brenon Diennevan Souza Barbosa
AgriEngineering 2022, 4(1), 207-215; https://doi.org/10.3390/agriengineering4010015 - 24 Feb 2022
Cited by 4 | Viewed by 2573
Abstract
Digital agriculture is fundamental to potential improvements in the field by optimizing processes and providing intelligent decision making. This study aims to calculate the height and canopy diameter of recently transplanted coffee plants over three periods of crop development using aerial images, verify [...] Read more.
Digital agriculture is fundamental to potential improvements in the field by optimizing processes and providing intelligent decision making. This study aims to calculate the height and canopy diameter of recently transplanted coffee plants over three periods of crop development using aerial images, verify statistical differences between field measurements and aerial images, estimate linear equations between field data and aerial images, and monitor the temporal profile of the growth and development of the cultivar understudy in the field based on information extracted from aerial images through a Remotely Piloted Aircraft System (RPAS). The study area comprises a recently transplanted five-month-old Coffea arabica L. cultivar IAC J10 with information of height and crown diameter collected in the field and aerial images obtained by RPAS. As a result, it was possible to calculate the height and diameter of the canopy of coffee plants by aerial images obtained by RPAS. The linear estimation equation for height and crown diameter was determined with satisfactory results by coefficients R and R2 and performance metrics MAE, RMSE, and regression residuals, and it was possible to monitor the temporal profile of the height of the coffee cultivar in the field based on aerial images. Full article
(This article belongs to the Special Issue Novel Approaches for Unmanned Aerial Vehicle)
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12 pages, 3067 KiB  
Article
Predicting Soil Water Content on Rainfed Maize through Aerial Thermal Imaging
by Matheus Gabriel Acorsi and Leandro Maria Gimenez
AgriEngineering 2021, 3(4), 942-953; https://doi.org/10.3390/agriengineering3040059 - 28 Nov 2021
Cited by 2 | Viewed by 3218
Abstract
Restrictions on soil water supply can dramatically reduce crop yields by affecting the growth and development of plants. For this reason, screening tools that can detect crop water stress early have been long investigated, with canopy temperature (CT) being widely used for this [...] Read more.
Restrictions on soil water supply can dramatically reduce crop yields by affecting the growth and development of plants. For this reason, screening tools that can detect crop water stress early have been long investigated, with canopy temperature (CT) being widely used for this purpose. In this study, we investigated the relationship between canopy temperature retrieved from unmanned aerial vehicles (UAV) based thermal imagery with soil and plant attributes, using a rainfed maize field as the area of study. The flight mission was conducted during the late vegetative stage and at solar noon, when a considerable soil water deficit was detected according to the soil water balance model used. While the images were being taken, soil sampling was conducted to determine the soil water content across the field. The sampling results demonstrated the spatial variability of soil water status, with soil volumetric water content (SVWC) presenting 10.4% of variation and values close to the permanent wilting point (PWP), reflecting CT readings that ranged from 32.8 to 40.6 °C among the sampling locations. Although CT correlated well with many of the physical attributes of soil that are related to water dynamics, the simple linear regression between CT and soil water content variables yielded coefficients of determination (R2) = 0.42, indicating that CT alone might not be sufficient to predict soil water status. Nonetheless, when CT was combined with some soil physical attributes in a multiple linear regression, the prediction capacity was significantly increased, achieving an R2 value = 0.88. This result indicates the potential use of CT along with certain soil physical variables to predict crop water status, making it a useful tool for studies exploring the spatial variability of in-season drought stress. Full article
(This article belongs to the Special Issue Novel Approaches for Unmanned Aerial Vehicle)
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22 pages, 28975 KiB  
Article
Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield
by Jason Barnetson, Stuart Phinn and Peter Scarth
AgriEngineering 2021, 3(3), 681-702; https://doi.org/10.3390/agriengineering3030044 - 10 Sep 2021
Cited by 3 | Viewed by 3571
Abstract
The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. To date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted [...] Read more.
The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. To date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM (tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry provided measures of pasture biomass from many overlapping high-resolution images. These measurements were taken throughout a growing season and were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM (tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base. Full article
(This article belongs to the Special Issue Novel Approaches for Unmanned Aerial Vehicle)
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11 pages, 1549 KiB  
Article
Dicamba Injury on Soybean Assessed Visually and with Spectral Vegetation Index
by Matheus Gregorio Marques, João Paulo Arantes Rodrigues da Cunha and Ernane Miranda Lemes
AgriEngineering 2021, 3(2), 240-250; https://doi.org/10.3390/agriengineering3020016 - 3 May 2021
Cited by 9 | Viewed by 3312
Abstract
The recent availability of soybean cultivars with resistance to dicamba herbicide has increased the risk of injury in susceptible cultivars, mainly as a result of particle drift. To predict and identify the damage caused by this herbicide requires great accuracy. The objective of [...] Read more.
The recent availability of soybean cultivars with resistance to dicamba herbicide has increased the risk of injury in susceptible cultivars, mainly as a result of particle drift. To predict and identify the damage caused by this herbicide requires great accuracy. The objective of this work was to evaluate the injury caused by the simulated drift of dicamba on soybean (nonresistant to dicamba) plants assessed visually and using the Triangular Greenness Index (TGI) from images obtained from Remotely Piloted Aircraft (RPA). The study was conducted in a randomized complete block design with four replications during the 2019/2020 growing season, and the treatments consisted of the application of six doses of dicamba (0, 0.28, 0.56, 5.6, 28, and 112 g acid equivalent dicamba ha−1) on soybean plants at the third node growth stage. For the evaluation of treatments using the TGI technique, spectral data acquired through a Red Green Blue (RGB) sensor attached to an RPA was used. The variables studied were the visual estimation of injury, TGI response at 7 and 21 days after application, plant height, and crop yield. The exposure to the herbicide caused a reduction in plant height and crop yield. Vegetation indices, such as TGI, have the potential to be used in the evaluation of injury caused by dicamba, and may be used to cover large areas in a less subjective way than visual assessments. Full article
(This article belongs to the Special Issue Novel Approaches for Unmanned Aerial Vehicle)
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