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A Review of Detection of Pest Problem in Rice Farming by using Blockchain and IoT Technologies

Authors

  • Taufik Hidayat Universitas Wiralodra
  • Rahutomo Mahardiko Platinumetrix Pte, Ltd

DOI:

10.47709/cnahpc.v3i1.935

Keywords:

Blockchain, Internet of Things, Agriculture, Pest, Systematic Literature Review

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Abstract

Pest can be a serious topic in agricultural areas especially rice plantations. The pest destroys the plants before harvesting time. Because of the presence of the pest, the yield of agricultural products is decreasing. From a technical point of view, an agricultural professional should identify not only the type of pest that destroys rice plants but also overcome the pest. This research proposes a paper review on pest detection systems by using Blockchain technology and the Internet of Things involving all parties involved. The use of the review is to have a broad overview regarding the functional combination between IoT and Blockchain technologies to reduce the pest problems. Smart contract technology Blockchain may be held to determine automatic alert in the system and know how to resolve the problem accurately and all information is verified by blockchain system without a human interception. IoT devices can be attached to rice plantations to monitor, determine and send the information of the pests. Our paper explains the combination of IoT and Blockchain technologies in order to improve any possibility of success rate by getting the pests. Thus, IoT replaces human manual checking in pest identification to reduce human error. So that, the harvesting time can be increased and the agriculture yields are good. The search comparison results show that ScienceDirect has the highest search value

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ARTICLE Published HISTORY

Submitted Date: 2021-02-11
Accepted Date: 2021-02-11
Published Date: 2021-03-05

How to Cite

Hidayat, T., & Mahardiko, R. (2021). A Review of Detection of Pest Problem in Rice Farming by using Blockchain and IoT Technologies. Journal of Computer Networks, Architecture and High Performance Computing, 3(1), 89-96. https://doi.org/10.47709/cnahpc.v3i1.935