PREDICTING GERM DEVELOPMENT CONDITIONS ON CROPS USING REAL-TIME IOT DATA AND ANN MODEL
DOI:
https://doi.org/10.29121/shodhkosh.v4.i2.2023.5720Keywords:
Internet of Things (IoT), MATLAB, Artificial Neural Network (ANN), Feedforward Neural Networks (FNN), Environmental Sensors, Modern Agriculture etc.Abstract [English]
The prediction of germ development conditions on crops is critical in optimizing crop yields and ensuring sustainable agriculture. This study presents a MATLAB-based artificial neural network (ANN) model for predicting germ development conditions in crops using IoT-based weather data. The research utilizes feedforward neural networks (FNN) algorithms for training, leveraging historical pattern-based weather data collected from IoT sensors for temperature, humidity, moisture, and light intensity (LDR).
The IoT system provides real-time data streams, enabling the ANN models to predict optimal conditions for germ development with high accuracy. The FNN model learns complex relationships between environmental factors and germ development. The proposed system achieves robust performance in scenarios with varying weather conditions, making it a reliable tool for farmers and agricultural planners.
The results demonstrate the feasibility of using IoT-enabled systems and ANN models for agricultural applications, showing significant potential for scalability and integration into smart farming systems.
References
Biswas Agni., & Prakash, S., Farming technology for India agriculture based sensorics and indicative systems, IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR) (pp. 72-78). July 2015. IEEE. DOI: https://doi.org/10.1109/TIAR.2015.7358534
Chetan Dwarkani M, Ganesh Ram, Jagannathan S, R. Priyadarshini, Smart Farming System Using Sensors for Agricultural Task Automation, 2015 IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural Development (TIAR 2015) DOI: https://doi.org/10.1109/TIAR.2015.7358530
An IoT Based System for Remote Monitoring of Soil Characteristics, Abdullah Na1, William Isaac2, Shashank Varshney3, Ekram Khan4, International conference on Information Technology, The next generation IT Summit, 2016.
P. Sukumar1, Dr. T. Kavitha2, A. Deepika3, V. Jashnavi4, Real Time soil fertility analyzer using IOT, National Conference on Emerging Trends In Information, Management and Engineering Sciences NC’e-TIMES 1.0- 2018.
P. Sindhu1 and G. Indirani2, “IoT Enabled Soil Testing”, Asian Journal of Computer Science and Technology, ISSN: 2249-0701 Vol.7 No. S1, 2018, pp. 54-57. DOI: https://doi.org/10.51983/ajcst-2018.7.S1.1805
Hridesh Shah, Dhruv Shah, Jeet Shah, Akshat Shah, Karan Shah and Krupal Vora, “International Journal of Advanced Research, 6(11), 974-979, ISSN: 2320-5407.
Anandkumar Vellaichamy, Balakrishnan S, “IoT based soil analysis and irrigation system”, International Journal of Pure and Applied Mathematics · Volume 119 No. 12 January 2018, 1127-1134.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Susmita A. Meshram, Dr. N.K Choudhari
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
