Precision agriculture using IoT data analytics and machine learning
Autor: | Shabir Ahmad Sofi, Ravesa Akhter |
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Rok vydání: | 2022 |
Předmět: |
General Computer Science
Computer science business.industry media_common.quotation_subject 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Agrarian society Smart grid Agriculture Home automation Smart city 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Precision agriculture Artificial intelligence business Wireless sensor network computer media_common |
Zdroj: | Journal of King Saud University - Computer and Information Sciences. 34:5602-5618 |
ISSN: | 1319-1578 |
Popis: | In spite of the insight commonality may have concerning agrarian practice, fact is that nowadays agricultural science diligence is accurate, precise, data-driven, and vigorous than ever. The emanation of the technologies based on Internet of Things (IoT) has reformed nearly each industry like smart city, smart health, smart grid, smart home, including “smart agriculture or precision agriculture”. Applying machine learning using the IoT data analytics in agricultural sector will rise new benefits to increase the quantity and quality of production from the crop fields to meet the increasing food demand. Such world-shattering advancements are rocking the current agrarian approaches and generating novel and best chances besides a number of limitations. This paper climaxes the power and capability of computing techniques including internet of things, wireless sensor networks, data analytics and machine learning in agriculture. The paper proposed the prediction model of Apple disease in the apple orchards of Kashmir valley using data analytics and Machine learning in IoT system. Furthermore, a local survey was conducted to know from the farmers about the trending technologies and their effect in precision agriculture. Finally, the paper discusses the challenges faced when incorporating these technologies in the traditional farming approaches. |
Databáze: | OpenAIRE |
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