Drone and sensor technology for sustainable weed management: a review

Esposito, Marco; Crimaldi, Mariano; Cirillo, Valerio; Sarghini, Fabrizio; Maggio, Aurelio

doi:10.1186/s40538-021-00217-8

Cited by 152 publications

(91 citation statements)

References 120 publications

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“…Identification of weed patches using uncrewed aerial vehicles (UAVs) can aid in integrated weed management (IWM), decreasing both selection pressure on herbicide-resistant weeds and chemicals spread in the environment [98]. Thus, AI can help farmers control weeds by early detection and avoid any weed resistance in the future [99]. The AI can detect or differentiate weeds in the crop at an early stage, and it can prevent weed infestation in the area.…”

Section: Herbicide Resistance In Weedsmentioning

confidence: 99%

Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

et al. 2021

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Weeds are unwanted plants that can reduce crop yields by competing for water, nutrients, light, space, and carbon dioxide, which need to be controlled to meet future food production requirements. The integration of drones, artificial intelligence, and various sensors, which include hyperspectral, multi-spectral, and RGB (red-green-blue), ensure the possibility of a better outcome in managing weed problems. Most of the major or minor challenges caused by weed infestation can be faced by implementing remote sensing systems in various agricultural tasks. It is a multi-disciplinary science that includes spectroscopy, optics, computer, photography, satellite launching, electronics, communication, and several other fields. Future challenges, including food security, sustainability, supply and demand, climate change, and herbicide resistance, can also be overcome by those technologies based on machine learning approaches. This review provides an overview of the potential and practical use of unmanned aerial vehicle and remote sensing techniques in weed management practices and discusses how they overcome future challenges.

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Section: Herbicide Resistance In Weedsmentioning

confidence: 99%

Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

et al. 2021

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“…The strong capability of UAV-acquired multispectral imagery in estimating the bulk drag coefficients C D of the examined vegetated drainage channel fully covered by 9-10 m high Arundo donax stands was then demonstrated by a comparative analysis performed between C D prediction assessed by considering the average observed and UAV-based LAI measurements at the same 30 channel's cross-sections, respectively. Also, it was demonstrated here that NDVI data recordings based on UAV-acquired multispectral images can be exploited to develop further methods for predicting actual LAI* or other riparian and aquatic vegetation indices, such as those based on deep learning/machine learning algorithms, already widely validated in many precision agriculture and rainfall prediction studies [53][54][55][56]. Further studies are certainly undergoing to develop these algorithms [57,58] and making even faster the assessment of the most relevant changes in water flow dynamic features of vegetated open channels associated with different riparian vegetation species, under many distinct ecohydraulic conditions, by also analyzing the key bio-mechanical and morphometric properties of riparian stands at micro-scale [59][60][61].…”

Section: Discussionmentioning

confidence: 99%

Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images

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Crimaldi

Pasquino

et al. 2021

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Estimating the main hydrodynamic features of real vegetated water bodies is crucial to assure a balance between their hydraulic conveyance and environmental quality. Riparian vegetation stands have a high impact on vegetated channels. The present work has the aim to integrate riparian vegetation’s reflectance indices and hydrodynamics of real vegetated water flows to assess the impact of riparian vegetation morphometry on bulk drag coefficients distribution along an abandoned vegetated drainage channel fully covered by 9–10 m high Arundo donax (commonly known as giant reed) stands, starting from flow average velocities measurements at 30 cross-sections identified along the channel. A map of riparian vegetation cover was obtained through digital processing of Unnamed Aerial Vehicle (UAV)-acquired multispectral images, which represent a fast way to observe riparian plants’ traits in hardly accessible areas such as vegetated water bodies in natural conditions. In this study, the portion of riparian plants effectively interacting with flow was expressed in terms of ground-based Leaf Area Index measurements (LAI), which easily related to UAV-based Normalized Difference Vegetation Index (NDVI). The comparative analysis between Arundo donax stands NDVI and LAI map enabled the analysis of the impact of UAV-acquired multispectral imagery on bulk drag predictions along the vegetated drainage channel.

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“…This approach of weeding generates herbicide waste and pollutes the agricultural ecological environment. The site-specific weed management (SSWM) approach was suggested to tackle these problems [12]. SSWM is a strategy that consists of varying management of weed within a crop field to suit the variation in density, location, and composition of the weed population [13].…”

Section: Introductionmentioning

confidence: 99%

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

et al. 2021

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Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.

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Drone and sensor technology for sustainable weed management: a review

Cited by 152 publications

References 120 publications

Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

Bulk Drag Predictions of Riparian Arundo donax Stands through UAV-Acquired Multispectral Images

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

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