IOT based Automated Greenhouse Using Machine Learning Approach
Keywords:
Artificial Intelligence, Computer Vision, Data-analytics, Machine Learning, Neural NetworkAbstract
Focusing on the effect of universal food insecurity, over 60% of sub-Saharan countries are predicted to be in a state of malnourishment and yet several farming places are under drought state. The climatic condition is believed to be biannual dry seasons which is very difficult for farmers to cultivate crops due to shortage of water and poor soil fertility. Yet heavy rainfall is still a great threat for the farmers since it devastates cash crops. The use of a smart greenhouse with Artificial Intelligence to grow and protect plants in both dry and wet seasons and reduce labor-intensive human tasks and automate pervasive data analytics of daily plant status can surprisingly boost food security. Here we present a fully automated greenhouse system with artificial intelligence embedded in it that uses around 10,000 plant images in it that initially nurture plants under optimum atmospheric conditions by taking real-time decisions, detecting any kind of illness, and interestingly notifying the stage of fruit ripeness. By implementing a neural network-based computer vision approach we were able to keep track of the health status of the plants caused by several microorganisms. The obtained predictions and results accurately verify how machine learning can be used to increase gross national food security by implementing such systems in multiple farming areas without prior human involvement.
Downloads
References
Neda Fatima, Salman Ahmad Siddiqui, and Anwar Ahmad, “IoT-based Smart Greenhouse with Disease Prediction using Deep Learning” International Journal of Advanced Computer Science and Applications (IJACSA), 12(7), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120713
H. Jaiswal, K. R. P, R. Singuluri and S. A. Sampson, "IoT and Machine Learning-based approach for Fully Automated Greenhouse," 2019 IEEE Bombay Section Signature Conference (IBSSC), 2019, pp. 1-6, doi: 10.1109/IBSSC47189.2019.8973086.
Rupali Satpute, Hemant Gaikwad, Shoaib Khan, Aaditya Inamdar, Deep Dave,” IOT Based Greenhouse Monitoring System”, IJRASET ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 6.887 Volume 6 Issue IV, April 2018.
D. Shinde and N. Siddiqui, "IOT Based Environment change Monitoring & Controlling in Greenhouse using WSN," 2018 International Conference on Information, Communication, Engineering and Technology (ICICET), 2018, pp. 1-5, doi: 10.1109/ICICET.2018.8533808.
Deepa, "A Pre-Processing Approach for Accurate Identification of Plant Diseases in leaves," 2018 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), 2018, pp. 249-252, doi: 10.1109/ICEECCOT43722.2018.9001616.
M. I. Alipio, A. E. M. Dela Cruz, J. D. A. Doria and R. M. S. Fruto, "A smart hydroponics farming system using exact inference in Bayesian network," 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE), 2017, pp. 1-5, doi: 10.1109/GCCE.2017.8229470.
S. Ruengittinun, S. Phongsamsuan and P. Sureeratanakorn, "Applied internet of thing for smart hydroponic farming ecosystem (HFE)," 2017 10th International Conference on Ubi-media Computing and Workshops (Ubi-Media), 2017, pp. 1-4, doi: 10.1109/UMEDIA.2017.8074148
Zhang, Z.; Gates, R.S.; Zou, Z.R.; Hu, X.H. Evaluation of ventilation performance and energy efficiency of greenhouse fans. Int. J. Agric. Biol. Eng. 2015, 8, 103–110.
Sachin D. Khirade and A. B. Patil. 2015. Plant Disease Detection Using Image Processing. In Proceedings of the 2015 International Conference on Computing Communication Control and Automation (ICCUBEA '15). IEEE Computer Society, USA, 768–771. https://doi.org/10.1109/ICCUBEA.2015.153.
Brahmbhatt S. (2013) Image Segmentation and Histograms. In: Practical OpenCV. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4302-6080-6_7.
M. Pack and K. Mehta, "Design of Affordable Greenhouses for East Africa," 2012 IEEE Global Humanitarian Technology Conference, 2012, pp. 104-110, doi: 10.1109/GHTC.2012.66.
C. Heidenreich, M. Pritts, M. Kelly, "High Tunnel Raspberries and Blackberries," Department of Horticulture Publication, no. 47, 2009.
M. Mati, Overview of Water and Soil Nutrient Management Under Smallholder Rainfed Agriculture in East Africa, Colombo: International Water Management Institute, 2006, p. 13
Klose, F.; Tantau, H.J. Test of insect screens—Measurement and evaluation of the air permeability and light transmission. Eur. J. Hort. Sci. 2004, 69, 235–243.
Christopher B. Barrett, Food security and food assistance programs, Handbook of Agricultural Economics, Elsevier, Volume 2, 2002, Pages 2103-2190, ISSN 1574-0072, ISBN 9780444510792, https://doi.org/10.1016/S1574-0072(02)10027-2.
Teitel M (2001) The effect of insect-proof screens in roof openings on greenhouse microclimate. Agricultural and Forest Meteorology, 110:13–25.
Von Elsner, B.; Briassoulis, D.; Waaijenberg, D.; Mistriotis, A.; von Zabeltitz, C.; Gratraud, J.; Russo, G.; Suay-Cortes, R. Review of Structural and Functional Characteristics of Greenhouses in European Union Countries, Part II: Typical Designs. J. Agric. Eng. Res. 2000, 75, 111–126.
Bethke, J.A.; Redak, R.A.; Paine, T.D. Screens deny specific pests’ entry to greenhouses. Calif. Agric. 1994, 48, 37–40.
Farooqui, N.A., Ritika (2020). A Machine Learning Approach to Simulating Farmers’ Crop Choices for Drought Prone Areas. In Proceedings of ICETIT 2019. Lecture Notes in Electrical Engineering, vol 605. Springer, Cham. https://doi.org/10.1007/978-3-030-30577-2_41.
Farooqui, N.A., Ritika, Tyagi, A. (2020). Data Mining and Fusion Techniques for Wireless Intelligent Sensor Networks. In Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's. Advances in Intelligent Systems and Computing, vol 1132. Springer, Cham. https://doi.org/10.1007/978-3-030-40305-8_28.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Nafees Akhter Farooqui, Amit Kumar Mishra, Ritika Mehra
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
