Publication Type:

Conference Paper

Source:

2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017, Institute of Electrical and Electronics Engineers Inc. (2018)

ISBN:

9781509066209

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057990787&doi=10.1109%2fICCIC.2017.8524140&partnerID=40&md5=a99fe46c190d45567d23757000fe321f

Keywords:

Agricultural system, agriculture, Crop rotation, Crops, Deep learning, Internet of things, Irrigation, Irrigation systems, Learning systems, Long short-term memory, Monitoring process, Optimal values, Smart agricultures, SMS services, Soil parameters, Wireless sensor networks

Abstract:

Main objective of the smart agricultural system is to improve the yield of the field. In this paper, two main streams are adopted: (i) predicting the suitable crop for the next crop rotation (ii) improvising the irrigation system of the field by selective irrigation. The above goal is achieved by periodically monitoring the field. The monitoring process involves collecting information about the soil parameters of the field. A wireless sensor network (WSN) is established to collect these data and have a hindsight of it by sporadically uploading it to cloud. This uploaded data forms the basis for analytics. Through experimentation, Long Short Term Memory (LSTM) networks is found to be the suitable algorithm. The inferred results are compared with the optimal values and the best-suited crop is intimated to the user through SMS service.

Notes:

cited By 0; Conference of 8th IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017 ; Conference Date: 14 December 2017 Through 16 December 2017; Conference Code:142125

Cite this Research Publication

A. Mozhi S. Varman, Baskaran, A. R., Aravindh, S., Prabhu E., and M., K., “Deep Learning and IoT for Smart Agriculture Using WSN”, in 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017, 2018.