An Implementation of IoT and Data Analytics in Smart Agricultural System – A Systematic Literature Review
| Main Authors: | K. Vikranth, Krishna Prasad K. |
|---|---|
| Format: | Article Journal |
| Bahasa: | ang |
| Terbitan: |
, 2021
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/4496828 |
Daftar Isi:
- India is a country that depends on agriculture, where about half the population relies heavilyon agriculture for their livelihood. However, most of the practices undertaken in the agriculturalprocess are not for profit and yield favorable. It should upgrade with current technologies toboost seed quality, check soil infertility, check the water level, environmental changes, andmarket price prediction, and achieve in agriculture sensitivity of faults and backgroundunderstanding. The advancement in technology and developments is seen as a significantaspect in their financial development and agricultural production growth. The Internet ofThings (IoT), Wireless Sensor Networks (WSN), and data analytics accomplish these upgrades.These technologies help in providing solutions to agricultural issues such as resourceoptimization, agricultural land monitoring, and decision-making support, awareness of thecrop, land, weather, and market conditions for farmers. Smart agriculture is based on data fromsensors, data from cloud platform storage and data from databases, all three concepts need tobe implemented. The data are collected from different sensors and stored in a cloud-based backend support, which is then analyzed using proper analytics techniques, and then the relevantinformation is transferred to a user interface, which naturally supported the decision toconclude. The IoT applications mainly use sensors to monitor the situation, which collects alarge size of data every time, so in the case of the Internet of Things (IoT) application, sensorscontribute more. Data analytics requires data storage, data aggregation, data processing anddata extraction. To retrieve data and information from database, we must use data miningtechniques. It acts a significant position in the selection-making process on several agriculturalissues. The eventual objective of data mining is to acquire information form data transform itfor some advanced use into a unique human-comprehensible format. Big data's role inAgriculture affords prospect to increase the farmers' economic gain by undergoing a digitalrevolution in this aspect that we examine with precision. This paper includes reviewing asummary of some of the conference papers, journals, and books that have been going in favorof smart agriculture. The type of data required for smart farming system are analyzed and thearchitecture and schematic diagram of a proposed intelligent farming system are included. Italso involves implementing different components of the smart farming system and integratingIoT and data analytics in the smart farming system. Based on the review, research gap, researchagendas to carry out further research are identified.