Remotely Piloted Aircraft (RPA) in Agriculture: A Pursuit of Sustainability

Ali, Ahmad; Ordóñez, Javier; Cartujo, P.; Martos, Vanessa

doi:10.3390/agronomy11010007

Cited by 47 publications

(38 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another way to describe information treatment using RS technology is the preparation of vegetation indices (VIs). Most commonly, VIs calculated using RS, among others, include: normalized difference vegetation index (NDVI) for crop monitoring and empirical studies; soil-adjusted vegetation index (SAVI) for improving the sensitivity of NDVI to soil backgrounds; green normalized difference vegetation index (gNDVI) for estimating the photosynthetic activity; wide dynamic range vegetation index (WDRVI) for enhancing the dynamic range of NDVI; chlorophyll index-green (CI-G) for determining the leaf chlorophyll content; modified soil adjusted vegetation index (MSAVI) for reducing the influence of bare soil on SAVI; optimized soil-adjusted vegetation index (OSAVI) for calculating aboveground biomass, leaf nitrogen content, and chlorophyll content; chlorophyll vegetation index (CVI) for representing relative abundance of vegetation and soil; triangular vegetation index (TVI) for predicting leaf nitrogen status; normalized green red difference index (NGRDI) for estimating nutrient status; visible atmospherically resistant index (VARI) for mitigating the illuminating differences and atmospheric effects in the visible spectrum; crop water stress index (CWSI) for measuring canopy temperature changes and dynamics; and photochemical reflectance index (PRI) for detecting disease symptoms [7].…”

Section: Agricultural Remote Sensingmentioning

confidence: 99%

“…Normally sensors used in RS that are for crop monitoring detect the following electromagnetic wave bands, depending on specific objectives [7]: The amplitude of the information retrieved from RS is considerable to support sustainable agriculture capable of feeding a rapidly growing world population. Among the prominent advantages or applications of RS are the identification of phenotypically better varieties, optimization of crop management, evapotranspiration, agriculture phenology, crop production forecasting, ecosystem services (related to soil or water resources) provision, plant and animal biodiversity screening, crop and land monitoring, and precision farming [8,18,19,[40][41][42].…”

Section: Agricultural Remote Sensingmentioning

confidence: 99%

“…The first use of RPAs in agriculture was reported as back as 1986 for monitoring Montana's forest fires [7]. This 5th revolution is anticipated to make more frequent use of RPAs in farming.…”

Section: Remotely Piloted Aircrafts (Rpas)mentioning

confidence: 99%

“…Quite a number of review articles, with respect to RS, are also available nowdiscussing or comparing particular aspects of these novel technologies. For example, a few of the reported studies, just in past three years (2018-2020), have discussed topics like plant biodiversity, monitoring of a particular crop, highlighting various applications, discussing substantial improvements to different platforms or technologies, use of particular technology, weed management, data management, underlining limitations, and hinting towards opportunities in this field [7,11,. Such a huge number of publications emphasize the tenacious necessity of using RS technologies in agriculture.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

et al. 2021

Self Cite

View full text Add to dashboard Cite

Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.

show abstract

Section: Agricultural Remote Sensingmentioning

confidence: 99%

Section: Agricultural Remote Sensingmentioning

confidence: 99%

Section: Remotely Piloted Aircrafts (Rpas)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this context, remote sensing can play a fundamental role in changing the production model by developing and implementing new technologies for vegetation monitoring (e.g., advanced sensors and remote platforms, powerful algorithms, etc.) that will lead to higher yields, while also obtaining more sustainable and environmentally friendly food and plant products [1]. Among the recent innovations, the unmanned aerial vehicles (UAVs) or drones have demonstrated their suitability for timely tracking and assessment of vegetation status due to several advantages, as follows: (1) they can operate at low altitudes to provide aerial imagery with ultra-high spatial resolution allowing detection of fine details of vegetation, (2) the flights can be scheduled with great flexibility according to critical moments imposed by vegetation progress over time, (3) they can use diverse sensors and perception systems acquiring different ranges of vegetation spectrum (visible, infrared, thermal), (4) this technology can also generate digital surface models (DSMs) with three-dimensional (3D) measurements of vegetation by using highly overlapping images and applying photoreconstruction procedures with the structure-from-motion (SfM) technique [2].…”

Section: Introductionmentioning

confidence: 99%

UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions

et al. 2021

View full text Add to dashboard Cite

This paper reviewed a set of twenty-one original and innovative papers included in a special issue on UAVs for vegetation monitoring, which proposed new methods and techniques applied to diverse agricultural and forestry scenarios. Three general categories were considered: (1) sensors and vegetation indices used, (2) technological goals pursued, and (3) agroforestry applications. Some investigations focused on issues related to UAV flight operations, spatial resolution requirements, and computation and data analytics, while others studied the ability of UAVs for characterizing relevant vegetation features (mainly canopy cover and crop height) or for detecting different plant/crop stressors, such as nutrient content/deficiencies, water needs, weeds, and diseases. The general goal was proposing UAV-based technological solutions for a better use of agricultural and forestry resources and more efficient production with relevant economic and environmental benefits.

show abstract

Soybean rust detection and disease severity classification by remote sensing

Negrisoli

Silva

et al. 2022

Agronomy Journal

View full text Add to dashboard Cite

The detection and monitoring of soybean rust (SBR) through remote sensing is promising because of the importance of the crop and the aspects of the disease.We evaluated the effects of different levels of SBR severity on soybean [Glycine max (L.) Merr.] leaflets reflectance aiming for the construction of a disease classification model. Leaflet reflectance was evaluated on two cultivars (susceptible and partially resistant) at four disease severity levels: healthy, low, moderate, and high.Leaflets were collected in the field and taken to the laboratory for spectral evaluation through the spectrophotometer UV 2700 coupled with Integrating Sphere Attachment ISR-603, in the range of 270-1000 nm. The feasibility of using a collection of vegetation indices (VIs) and data dimensionality reduction through multiple factor analysis (MFA) was evaluated, and a classification model was constructed. Ten algorithms were assessed based on precision, sensibility, and accuracy parameters, using 80% of the dataset as training data and 20% as testing dataset. The visible range and red edge region contributed more significantly to the disease prediction and classification model. The MFA performed satisfactorily in the dimensionality reduction and unveiled the effect of specific wavelengths on the classification of each class. Most of the VIs studied had high correlation performance across the severity classes. Classification accuracy and precision were >70% for all models. Linear support vector machine with the collection of VIs achieved the best results. This study provides a practical path for developing a detection model to be integrated into SBR management programs.

show abstract

Remotely Piloted Aircraft (RPA) in Agriculture: A Pursuit of Sustainability

Cited by 47 publications

References 74 publications

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions

Soybean rust detection and disease severity classification by remote sensing

Contact Info

Product

Resources

About