Assessment of Antioxidant, Antimicrobial, and Anticancer Activities of Sisymbrium Officinale Plant Extract

Khalid, Mahmoud; Amayreh, Mousa; Sanduka, Saadi; Salah, Zaidoun; Al‐Rimawi, Fuad; Al-Mazaideh, Ghassab M.; Alanezi, Abdulkareem A.; Alwedian, Fadel; Alasmari, Fawaz; Shalayel, Mohammed Helmy Faris

doi:10.2139/ssrn.3999160

Cited by 1 publication

(1 citation statement)

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“…The popular commercial UAV platforms are reasonably priced and easy to operate in field settings, and they serve as examples of standard high-throughput phenotyping workflows that include data analysis. Academics will be able to use new technologies and better utilize data for their breeding and research projects (Khalid et al 2020).…”

Section: Pest and Weed Identificationmentioning

confidence: 99%

Categorizing agricultural crops using unmanned aerial vehicle images: An in-depth review

Makhija,

Yadav,

Kopare

et al. 2024

Multidiscip. Rev.

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Unmanned Aerial Vehicle (UAV) technology has been employed widely in the past several years to increase agricultural output while decreasing labor-intensive tasks, inspection times and crop management expenses. They can quickly cover enormous regions in which being utilized more often to gather important data for a variety of precision agricultural applications, such as crop/plant categorization. In particular, the use of UAVs for managing pressures such as water, illnesses, malnourishment and pests has been rising in regard to tracking and evaluating the health of plants, agriculture and woods. With an emphasis on the processes employed to extract the data from images taken during the missions, this article provides a critical summary of the major developments in the field. A review of the main investigation gaps that need to be filled and the obstacles that have been addressed is presented, along with a few proposals for further study, based on data from over 55 published studies as well as our findings. Ultimately, this review offers a thorough assessment of the state of crop classification using UAV footage, providing insights into the field; accomplishments, difficulties and potential future paths.

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Section: Pest and Weed Identificationmentioning

confidence: 99%