Usage of Artificial Intelligence and Remote Sensing as Efficient Devices to Increase Agricultural System Yields | Zendy

Karim Ennouri | Zendy; Slim Smaoui | Zendy; Yâakoub Gharbi | Zendy; Manel Cheffi | Zendy; Olfa Ben Braïek | Zendy; Monia Ennouri | Zendy; Mohamed Ali Triki | Zendy

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Usage of Artificial Intelligence and Remote Sensing as Efficient Devices to Increase Agricultural System Yields

Author(s) -

Karim Ennouri,

Slim Smaoui,

Yâakoub Gharbi,

Manel Cheffi,

Olfa Ben Braïek,

Monia Ennouri,

Mohamed Ali Triki

Publication year - 2021

Publication title -

journal of food quality

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.568

H-Index - 43

eISSN - 1745-4557

pISSN - 0146-9428

DOI - 10.1155/2021/6242288

Subject(s) - agriculture , productivity , computer science , drone , agricultural engineering , task (project management) , agricultural productivity , field (mathematics) , precision agriculture , domain (mathematical analysis) , quality (philosophy) , yield (engineering) , artificial intelligence , remote sensing , systems engineering , engineering , mathematics , geography , mathematical analysis , genetics , archaeology , biology , pure mathematics , economics , macroeconomics , philosophy , materials science , epistemology , metallurgy

Artificial Intelligence is an emerging technology in the field of agriculture. Artificial Intelligence-based tools and equipment have actually taken the agriculture sector to a different level. This new technology has improved crop production and enhanced instantaneous monitoring, processing, and collection. The most recent computerized structures using remote sensing and drones have made a significant contribution to the agro-based domain. Moreover, remote sensing has the capability to support the development of farming applications with the aim of facing this main defy, via giving cyclic records on yield status during studied periods at diverse degrees and for diverse parameters. Various hi-tech, computer-supported structures are created to determine different central factors such as plant detection, yield recognition, crop quality, and several other methods. This paper includes the techniques employed for the analysis of collected information in order to enhance the productivity, forecast eventual threats, and reduce the task load on cultivators.

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