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Design of deep learning system for agricultural purpose

Kadiri Kamoru OluwatoyinIromini Nurudeen AjibolaAkanbi Ibrahim

Abstract

Agriculture and its requirements are, at the time, quite challenging to handle. The bulk of the country's residents are dependent on agriculture for their income. Food production should also be increased to keep up with the World's population growth. Agriculture has benefited significantly from recent technological advancements. Agricultural experts are becoming excited by current technology advances such as the Internet of Things (IoT), Machine Learning (ML), and Deep Learning (DL). IoT agriculture and farming are a whole new area of IoT application. We all know how to use IoT-based analytics like sensing soil temperature, nutrients, and humidity and regulating and monitoring water consumption for plant growth. The Internet of Things collects and produces vast volumes of data across several sectors and applications. Many challenges facing the agriculture business may be dealt with using deep learning and IoT technologies.

Smart Agriculture and AIAgricultureInternet of ThingsAnalyticsThe InternetPopulationPopulation growthComputer sciencePrecision agricultureWorld populationBusiness
Citations
3
FWCI
0.22
field-weighted impact
References
20
Percentile
72%
vs. same field & year
Citations per year
References
Citation Network

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