Authors : S.Harini; M.Hariprakash

Volume/Issue : Volume 9 - 2024, Issue 6 - June

---

Google Scholar : https://tinyurl.com/42u439su

Scribd : https://tinyurl.com/3p77uksd

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN068

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : In the current scenario the major that is played in every life of human is food which could only be brought through agriculture. In this economic world the development of agriculture has been reduced in day by day. The growth of every vegetables, rice etc are depends upon the soil and also the weather changes. If the soil condition is appropriate to grow the crop then there must be correct condition of weather to the better growth of crop. In this paper we are recommending the growth of crop which will be best suitable for the appropriate soil. For this we are using the technology that is known to be IoT for the analysis of soil. By using this the major issue might be resolved in yielding the crop with better production.

Keywords : Agriculture, Crop Yielding, Prediction, IOT.

References :

1. Gupta, A., Nagda, D., Nikhare, P., Sandbhor, A. (2021). Smart Crop Prediction using IoT and Machine Learning. International Journal of Engineering Research and Technology, 9(3).
2. B Thiyaneswaran, K Anguraj, M Sindhu, N. S Yoganathan, J Jayanthi. (2020). Development of Iris Biological Features Extraction for Biometric Based Authentication to Prevent Covid Spread. International Journal of Advanced Science and Technology, 29(3), 8266–8275.
3. Thiyaneswaran, B., Anguraj, K., Kumarganesh, S., Thangaraj, K. (2020). Early detection of melanoma images using gray level co‐occurrence matrix features and machine learning techniques for effective clinical diagnosis. International Journal of Imaging Systems and Technology, (ima.22514). doi:10.1002/ima.22514
4. Biradar, H. B., Shabadi, L. (2021). Review on IOT based multidisciplinary models for smart farming.
5. Mehta, P., Shah, H., Kori, V., Vikani, V., Shukla, S., Shenoy, M. (2019). Survey of unsupervised machine learning algorithms on precision agricultural data. IEEE Xplore.
6. Sandhiya, D., Thiyaneswaran, B. (2020). Extraction of dorsal palm basilic and cephalic hand vein features for human authentication system. 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE.
7. Radhika, Y., Shashi, M. (2021). Atmospheric Temperature Prediction using Support Vector Machines. International Journal of Computer Theory and Engineering, 55–58.
8. Ahmad, S., Kalra, A., & Stephen, H. (2019). Estimating soil moisture using remote sensing data: A machine learning approach. Advances in Water Resources, 33(1), 69–80.
9. Kumar, R., Singh, M. P., Kumar, P., & Singh, J. P. (2022). Crop Selection Method to maximize crop yield rate using machine learning technique. IEEE Xplore.
10. Shah, N. P., Bhatt, P. (2023). Greenhouse Automation And Monitoring System Design And Implementation. International Journal of Advanced Research in Computer Science, 8(9), 468–471
11. Jayanthi, J., and J. Selvakumar. A novel framework to facilitate personalized web search in a dual mode Cluster Computing 20.4 (2022): 3527-3535.
12. Parker, C. (2022). Unexpected challenges in large scale machine learning. Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining Algorithms, Systems, Programming Models and Applications - BigMine ’12.
13. Pudumalar, S., Ramanujam, E., Rajashree, R. H., Kavya, C., Kiruthika, T., & Nisha, J. (2020). Crop recommendation system for precision agriculture. IEEE Xplore.
14. Anitha, P., Chakravarthy, T. (2021). Agricultural Crop Yield Prediction using Artificial Neural Network with Feed Forward Algorithm. International Journal of Computer Sciences and Engineering, 6(11),178–181.
15. B Thiyaneswaran, V Bhuvaneshwaran, M.S Dharun, K Gopu, T Gowsikan (Ed.). (2020). Breathing level monitoring and alerting by using embedded IOT (Vol. 10). Journal of Green Engineering.
16. Rekha, P., Rangan, V. P., Ramesh, M. V., Nibi, K. V. (2021). High yield groundnut agronomy: An IoT based precision farming framework. IEEE Xplore.
17. Biradar, H. B.,Shabadi, L. (2021). Review on IOT based multidisciplinary models for smart farming. IEEE Xplore.
18. Jayanthi, J., and J. Selvakumar. A novel framework to facilitate personalized web search in a dual mode. Cluster Computing 20.4 (2022): 3527-3535.
19. Priya, P. K., Yuvaraj, N. (2019). An IoT Based Gradient Descent Approach for Precision Crop Suggestion using MLP. Journal of Physics: Conference Series, 1362, 012038.
20. Thiyaneswaran, B., Saravanakumar, A.,& Kandiban, R. (2020). Extraction of mole from  sclera using object area detection algorithm. 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE.
21. Patil, A., Beldar, M., Naik, A., & Deshpande, S. (2022). Smart farming using Arduino and data mining. IEEE Xplore.
22. Athani, S., Tejeshwar, C. H., Patil, M. M., Patil, P., Kulkarni, R. (2021, February 1). Soil moisture monitoring using IoT enabled arduino sensors with neural networks for improving soil management for farmers and predict seasonal rainfall for planning future harvest in North Karnataka — India