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Normalized Difference Vegetation Index (NDVI) data used to estimate the health of green vegetation and post processed high definition images for precision agriculture. Drone provide high-resolution image taken of crops, it compares the reflected intensities of near infrared (NIR) and visible light. Autonomous aircrafts are improved and cost effecti...
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... Due to more time consuming and cost these techniques were not suggested for large scale estimation, the optical remote sensing (spaceborne/airborne) methods based on optical properties of leaves were evolved for providing non-destructive, reliable and frequent precise estimation of leaf chlorophyll at local to regional scales in past years. [4,[9][10][11][12][13][14][15]. Cardim et al. [16] also demonstrated remote sensing role in precise monitoring of crop health and growth status for various essential activities like irrigation schedule plan, fertilizer and yield prediction. ...
... Consumer-level drones equipped with red, green and blue (RGB) cameras offer many advantages as an affordable remote-sensing tool for small-scale research projects. These advantages include quicker data delivery to the users, the ability to fly at low altitudes, acquisition of high spatial resolution images at low operational cost and the opportunity for analysing the data in near real time (Chapman et al. 2014;Gago et al. 2015;Mahajan and Raj 2016). RGB camera-equipped drones are demonstrably cost-effective for studying the influence of abiotic and biotic stresses, analysis of plant growth and crop senescence (Casadesús et al. 2007). ...
Context Unmanned aerial vehicles (UAV) with red–green–blue (RGB) cameras are increasingly used as a monitoring tool in farming systems. This is the first field study in mungbean (Vigna radiata (L.) Wilzcek) using UAV and image analysis across multiple seasons. Aims This study aims to validate the use of UAV imagery to assess growth parameters (biomass, leaf area, fractional light interception and radiation use efficiency) in mungbean across multiple seasons. Methods Field experiments were conducted in summer 2018/19 and spring–summer 2019/20 for three sowing dates. Growth parameters were collected fortnightly to match UAV flights throughout crop development. Fractional vegetation cover (FVC) and computed vegetation indices: colour index of vegetation extraction (CIVE), green leaf index (GLI), excess green index (ExG), normalised green-red difference index (NGRDI) and visible atmospherically resistant index (VARI) were generated from UAV orthomosaic images. Key results (1) Mungbean biomass can be accurately estimated at the pre-flowering stage using RGB imagery acquired with UAVs; (2) a more accurate relationship between the UAV-based RGB imagery and ground data was observed during pre-flowering compared to post-flowering stages in mungbean; (3) FVC strongly correlated with biomass (R2 = 0.79) during the pre-flowering stage; NGRDI (R2 = 0.86) showed a better ability to directly predict biomass across the three experiments in the pre-flowering stages. Conclusion UAV-based RGB imagery is a promising technology to replace manual light interception measurements and predict biomass, particularly at earlier growth stages of mungbean. Implication These findings can assist researchers in evaluating agronomic strategies and considering the necessary management practices for different seasonal conditions.
... When plant growth conditions are below optimal, lower biomass or fruit production can be expected. NDVI is one of the most widely used parameters that can provide valuable information on plant greenness, biomass, and overall leaf health [24,26,49,50]. Sub-optimal soil chemical or physical properties of nutrient or soil water deficiency, or biological stress such as diseases caused by insects or fungi, could result in a decrease in NDVI values. ...
The main objective of this study was to investigate soil–plant–water interactions based on field measurements of plant reflectance and soil water content (SWC) in different inter-row managed sloping vineyards. The following three different soil management applications were studied: tilled (T), cover crops (CC), and permanent grass (NT) inter-rows. We measured SWCs within the row and between rows of vines. Each investigated row utilized 7 to 10 measurement points along the slope. Topsoil SWC and temperature, leaf NDVI and chlorophyll concentrations and leaf area index (LAI) were measured every two weeks over the vegetation period (May to November) using handheld instruments. We found that management method and slope position can significantly affect the soil’s physical and chemical properties, such as clay or soil organic carbon contents. Cover crops in the inter-row significantly reduced average SWC. The in-row average topsoil SWCs and temperatures were lower in all study sites compared to the values measured in between rows. Significantly higher SWCs were observed for the upper points compared to the lower ones for CC and T treatments (58.0 and 60.9%, respectively), while the opposite was noted for NT. Grassed inter-row grapevines had significantly lower leaf chlorophyll content than the other inter-row managed sites (p < 0.001). The highest average leaf chlorophyll contents were observed in the T vineyard (16.89 CCI). Based on slope positions, the most distinguishable difference was observed for the CC: 27.7% higher chlorophyll values were observed at the top of the slope compared to the grapevine leaves at the bottom of the slope (p < 0.01). The leaf NDVI values were not as profoundly influenced by slope position in the vineyard as the chlorophyll values were. For overall LAI values, the T treatment had significantly lower values compared to NT and CC (p < 0.001). Moderate correlations were observed between NDVI and LAI and soil nitrogen and carbon content. In general, we found that both inter-row management and slope position can significantly influence soil parameters and affect plant growth, and consequently can accelerate plant stress under sub-optimal environmental conditions such as prolonged drought.
... To be able to document these spaces, it is necessary to carry out a multi-scalar survey, which returns distances, angles, and elevations (calibrated to ensure agricultural production) in a single information database. Topographical and hydrographic surveying and the resulting three-dimensional representation over large expanses are dealt with by numerous authors in the literature (Tarolli, 2011;Mahajan and Bundel, 2016;Liang and Delahaye, 2019 (Flener, 2015). The use of all these instruments requires substantial initial funding. ...
This research aims to define a low-cost replicable methodology for obtaining fast multiscale information models. The experiments carried out were conducted by researchers from Dada LAB and PLAY experimental Laboratories of the University of Pavia, Department of Civil Engineering and Architecture, on the case study of the irrigated landscape of the Pavia plain. The entire work process was developed according to a low-cost purpose, starting from fast acquisition activities with UAV instruments, to the processing of photogrammetric data, urban and detailed scale modelling with open-source software, to the census, filing, and computerisation of the model. The resulting product is configured as a multiscale reality-based information system. A census card is associated with each constituent element of the model (crops, canals, valuable hydraulic artefacts). Connection to the GIS platform allows the user to query the model. The result is a digital system oriented to facilitate the management of the agricultural and irrigation landscape, and to digitally document and preserve the heritage of historical hydraulic existing artefacts. Two different GIS platforms for structuring the information system were tested. The first involved a high-budget solution using ESRI ArcGIS Pro/ArcSCENE software, and the second involved using QGIS software, an Open-Source Geographic Information System, to develop an accessible information system without license fees, to evaluate the advantages and disadvantages of low-cost processes.
... The overview of equations and references is given in Table 2. NDVI is an indicator for photosynthetic activities in plants and it helps in detecting the health of a plant [83][84][85]. It is widely used for vegetation studies due to its responsiveness to green vegetation [26]. ...
The Niger Delta belongs to the largest swamp and mangrove forests in the world hosting many endemic and endangered species. Therefore, its conservation should be of highest priority. However, the Niger Delta is confronted with overexploitation, deforestation and pollution to a large extent. In particular, oil spills threaten the biodiversity, ecosystem services and local people. Remote sensing can support the detection of spills and their potential impact when accessibility on site is difficult. We tested different vegetation indices to assess the impact of oil spills on the land cover as well as to detect accumulations (hotspots) of oil spills. We further identified which species, land cover types and protected areas could be threatened in the Niger Delta due to oil spills. The results showed that the Enhanced Vegetation Index, the Normalized Difference Vegetation Index and the Soil Adjusted Vegetation Index were more sensitive to the effects of oil spills on different vegetation cover than other tested vegetation indices. Forest cover was the most affected land-cover type and oil spills also occurred in protected areas. Threatened species are inhabiting the Niger Delta Swamp Forest and the Central African Mangroves that were mainly affected by oil spills and, therefore, strong conservation measures are needed even though security issues hamper the monitoring and control.
... Xu et al.[100] developed a new algorithm for cattle counting in different situations such as pastures and feedlots. Mahajan and Bundel[101] proposed a cheaper method than spatial satellite sensors for estimating the growing crop health, using images taken by a drone having modified airborne cameras and sensors. Patel et al.[102] designed and developed a quadcopter for crop surveillance that can distinguish the infected, deceased, and matured crops from each other. ...
During the past two decades, with the advancement of avionics, control systems, design, product methods, and communication systems, UAVs have gained more interest from both civil and military customers. Hence, more researchers have dedicated their time to developing new applications for drones and improving their performance. In this study, a literature review on drone applications is conducted. It is explained that they can be more effective and beneficial when merged with preexisting systems, and thus making a system of systems. Further, we have classified UAV applications more comprehensively. This classification can be very useful for providing insights when designing new UAVs. Finally, previous works are discussed thoroughly and diagrams are developed to show the research focus in this field. These statistical diagrams can also be used as a gap analysis tool.
... En las siguientes referencias se encuentra información detallada sobre teledetección y drones: Cambell y Wynne, 2011;Chuvieco, 2010;Lillesand y Kiefer, 2007;Mahajan y Bundel, 2016;Moshoua et al., 2005;Quarnby et al.,1993; y Veroustraete, 2015. ...
... In (Sankaran et al., 2019), multispectral and thermal images of dry bean (Phaseolus vulgaris) were used to study the correlation of NDVI with biomass and seed yield. Over the last decade, the use of NDVI for assessment of crop health was reported, among others, in the following papers: (Mahajan and Raj, 2016;Zaman-Allah et al., 2015;Kim et al., 2011). ...
Computer vision and machine learning have recently been applied to a number of sensing platforms, boosting their performance to a new level. These advances have shown the vast possibilities for enhancing remote plant health assessment and disease detection. Until now, however, the scanning time and spatial resolution of such automated tools have been limited, as well as the area of application. We developed a state-of-the-art sensing system equipped with artificial intelligence and multispectral imaging with a special focus on near real-time and universality of application in agriculture. For this purpose, we collected a dataset of over 360,000 images of healthy and infected apple trees to develop and test our system, which includes a Convolutional Neural Network (CNN) algorithm for leaves segmentation. The proposed solution automatically computed vegetation indices (VIs) accurate to a single pixel. Further, we developed a desktop application for data post-processing and visualization, which allows the user to rapidly assess the health status of a vast agricultural area and thoroughly examine each tree individually. The developed system was successfully tested under field conditions in a large apple orchard, confirming viability of a reliable, end-to-end solution based on a computer vision platform for remote assessment of plant health and identification of stressed plants with high precision and spatial resolution.
... size aircraft and satellite images for a variety of reasons, including a combination of high spatial resolution and quick turnaround capabilities, as well as low operation costs and convenience of use [8]. ...
... Aerial vehicles can be used to carry out these activities. With the miniaturization of compact cameras and other sensors connected with aerial vehicles, such as infrared and sonar, the operation becomes more sophisticated [8]. ...
... Healthy plants, in general, reflect both green and infrared light wavebands. Sensors can monitor and identify the behavior of the reflectance pattern when plants are stressed by pests, nutrients, water or soil [8]. ...
Precision Agriculture (PA) is being revolutionized by unmanned aerial vehicles (UAVs), which enable for more effective resource utilization. PA will reach every corner of Sri Lanka in the next years. As a result, the purpose of this research is to (a) categorize UAVs in agriculture based on their performance characteristics, and (b) examine future challenges and research opportunities that will lead to the long-term deployment of UAVs in PA. The types, applications, advantages, disadvantages, opportunities, and potential dangers of using UAVs in PA were critically reviewed, examined, and evaluated using scholarly research articles, conference proceeding papers, and previously published literature from the past fifteen years, and information gaps were identified. UAVs have been classified based on their major performance parameters such as weight, endurance
and range, altitude, wing loading, engine type, and power/thrust loading. UAVs are more appropriate in agricultural practices due to their high spatial resolution and fast turnaround capabilities, as well as their low operating costs and ease of use. Livestock and wildlife management, crop monitoring, chemical and fertilizer application, weed detection, field mapping, and soil condition assessment are the major applications of
UAVs. Some of the primary constraints observed are the capture and availability of images on time, a lack of high spatial resolution images, challenges with image interpretation, and data extraction. Overall, UAVs are one of the most important technologies that transform traditional cultivation practices into a new perspective of intelligence in PA.
... NDVI values range from 11.0 to 21.0. Areas of barren rock, sand, or snow usually show very low NDVI values (e.g., 0.1 or less) (Mahajan & Raj, 2016). Sparse vegetation such as shrubs and grasslands or senescing crops may result in moderate NDVI values (approximately 0.2À0.5). ...
Modern farming has progressed by espousing technological developments, for instance, machines intended for tillage and harvesting, controlled irrigation, fertilizers, pesticides, crop breeding, genetics research, and biotechnological tools for trait enhancement. These innovations facilitated farmers to yield a large quantity of superior crops yield. Conversely, to triumph the unsurpassed possible yield from various types of soil is still in progress, and there are chief losses related to food wastage—especially during and postharvest—where the production is not scrutinized and touched well. The industry prerequisites a shrewd and precise solution that is possible through new technologies. Smart farming targets custom modern technological gears to rally crop yield and product quality, for case in point, accurate agricultural information, a site-specific crop management concept that monitors, measures, and measures the variability in crops and field variability. The kin use of a decision support system [artificial intelligence (AI)] countenances farmers to use fertilizer pesticides given to crops as per the requisite. Thus such monitoring could be accomplished by integrating employing suitable electronic sensing devices that record data in soil, environment, or crops. The data can run useful information related to what the crop required. To sort the best possible use of soil in a particular area, control crop care and yield postharvest. Informed decisions have to be made approximately allocating with has been intricated in the development and use of sensors to help establish the quality of a wide range of horticultural products, including fruits. Therefore AI is indicated as a data-driven elucidation with many recompenses. The technique could help to diminish the loss of fruits and vegetables laterally with the supply chain from the farm.
