Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Aquaponic
systems, an integration of aquaculture and hydroponics, have untapped potential
due to challenges in water quality control, nutrient deficiency detection, feed
management, and the lack of comprehensive market feasibility analysis. This
study introduces an innovative approach that employs sensor technology,
internet of things and machine learning to effectively address these issues.
The Internet of things system is developed to monitor status of water and A
Neural Network training model is developed to predict water quality parameters
proactively before they reach critical levels, optimizing system efficiency,
yield, and sustainability. By applying a deep learning-based model, nutrient
deficiencies are detected early, using a convolutional neural network that
classifies crops based on nutrient content. This gap-filling measure provides
valuable insights for nutrient management in aquaponic systems. A novel
automated fish-feeding mechanism, leveraging machine learning, eliminates the
drawbacks of manual control and enhances system performance, product quality,
and profitability. Additionally, a market feasibility analysis model, absent in
prior systems, helps to forecast, and reduce the risk of overselling or unsold
products. These advancements contribute significantly to the commercial and
sustainable potential of aquaponic farming, providing a robust framework for
future research and development.
Country : Sri Lanka
IRJIET, Volume 7, Issue 11, November 2023 pp. 693-704
|
[1] |
F. L. Valiente,
"Internet of things (IOT)-based mobile application for monitoring of
automated aquaponics system.," 2018 IEEE 10th international conference
on humanoid, nanotechnology, information technology, communication and
control, environment and management (HNICEM), 2018. |
|
[2] |
A. R. P. M. a. R. A.
Yanes, "Towards automated aquaponics: A review on monitoring, IoT, and
smart systems," Journal of Cleaner Production 263, 2020. |
|
[3] |
A. Dutta, "IoT
based aquaponics monitoring system," 1st KEC Conference Proceedings.,
2018. |
|
[4] |
K. S. Aishwarya,
"Survey on IoT based automated aquaponics gardening approaches.,"
2018 Second International Conference on Inventive Communication and
Computational Technologies (ICICCT), 2018. |
|
[5] |
N. K. Jacob, "IoT
powered portable aquaponics system," Proceedings of the Second
International Conference on Internet of things, Data and Cloud Computing,
2017. |
|
[6] |
T. Rajaee, S.
Mirbagheri, M. Zounemat-Kermani and V. Nourani, "Daily suspended
sediment concentration simulation using ANN," Total Environ, 2009. |
|
[7] |
H. Shouliang, H.
Zhuoshi, S. Jing, X. Beidou and Z. Chaowei, "Using Artificial Neural
Network Models for Eutrophication Prediction.," Procedia Environ, 2013,
p. 310–316. |
|
[8] |
F. Chang, P. Chen, L.
Chang and Y. Tsai, " Estimating spatio-temporal dynamics of stream total
phosphate concentration by Estimating spatio-temporal dynamics of stream
total phosphate concentration by," Total Environ., 2016, pp. 562,
228–236.. |
|
[9] |
Y. Fan, W. Lu, T. Miao,
Y. An, J. Li and J. Luo, " Optimal design of groundwater pollution
monitoring network based on the SVR," 2020, p. 24090–24102. |
|
[10] |
W. Leong, A. Bahadori
and J. Zhang, "Prediction of water quality index (WQI) using support
vector machine (SVM) and least," River Basin Manag, 2019, p. 149–156. |
|
[11] |
J. e. a. Guo, "An
XGBoost-based physical fitness evaluation model using advanced feature
selection and Bayesian hyperparameter optimization for wearable running
monitoring.," Computer Networks, 2019, pp. 166-180. |
|
[12] |
J. e. a. Smith, "
Image analysis for detection of nitrogen deficiencies in corn plants,"
Agricultural Engineering Journal, 2018. |
|
[13] |
W. e. a. Li,
"Computer vision-based detection of iron deficiencies in lettuce
plants," Computers and Electronics in Agriculture, 2019. |
|
[14] |
H. e. a. Wang,
"Detection of multiple nutrient deficiencies in lettuce using
hyperspectral imaging," Remote Sensing, 2020. |
|
[15] |
S. e. a. Yu,
"Integrated nutrient status assessment in tomato plants using image
processing and machine learning," Computers and Electronics in
Agriculture, 2021. |
|
[16] |
Liu, X., Cao, J., Jiang,
W., Cai, J., Wu, J., & Gong, Y., "Development of a computer vision
system for fish feeding behavior analysis in aquaculture," Computers and
Electronics in Agriculture, pp. 154, 9-16, 2018. |
|
[17] |
Ren, J., Wu, X., Li, Y.,
& Huang, Y, "A computer vision-based fish feeding automation system
in aquaculture," Aquacultural Engineering, p. 90, 2020. |
|
[18] |
Saadeh, M., Al-Quraan,
S., & Gharaibeh, B., "Design and development of an automated system
for controlling fish feeding in aquaculture based on wireless sensor
networks," Sensors, no. 1198, 2018. |
|
[19] |
Li, H., Zhang, Wang, L.,
"Demand Prediction for agricultural products using support vector
machine," Agricultural Economics, pp. 145-156, 2019. |
|
[20] |
Gupta, S., Sharma, R.,
& Singh, M. , "Sales forecasting in the retail industry using random
forest and artificial neural network models," International Journal of
Retail Management, pp. 320-333, 2020. |
|
[21] |
Chen, Q., Liu, Y., &
Wang, J., "Sales forecasting in the food industry using deep
learning-based models," Journal of Food Science, pp. 567-580., 2018. |
|
[22] |
Zhang, S., Zhou, Y.,
& Li, M., "Time series analysis and LSTM-based sales prediction in
the agricultural sector," Agricultural Economics Review, pp. 120-135,
2020. |
|
[23] |
Song, L., Yang, J.,
& Chen, T. , "Seasonal patterns in consumer demand for organic
foods: A case study," Consumer Behavior, pp. 210-225, 2019. |
|
[24] |
Smith, J., Johnson, A. ,
"Aquaponic food demand and sales dataset," Agriculture and Food
Sciences, pp. 123-135, 2019. |
|
[25] |
Brown, R. ,
"Handling missing values in aquaponic food market data," in
International Conference on Data Science Proceedings, 2018. |
|
[26] |
Johnson, A., et al.,
"Demand and sales prediction in aquaponic food market using LSTM,"
Machine Learning Research, pp. 567-580, 2021. |
|
[27] |
M. Jones,
"Hyperparameter optimization for LSTM-based demand and sales
prediction," in the International Conference on Artificial Intelligence,
2022. |
|
[28] |
Smith, J. ,
"Evaluation metrics for demand and sales prediction in aquaponic food
market," Business Analytics, pp. 210-225, 2020. |
|
[29] |
Brown, R., et al.,
"Capturing seasonal variations in aquaponic food demand and sales,"
International Journal of Forecasting, pp. 315-328, 2019. |
|
[30] |
Johnson, A., Smith, J. ,
"Seasonal patterns in aquaponic food market: A case study.," in
Proceedings of the Annual Conference on Agricultural Economics, 78-84., 2020.
|
|
[31] |
A. Reyes Yanes,
"Towards Aquaponics 4.0: A Framework for Automation, IoT, and Smart
Systems Implementations in Indoor Farming," University of Alberta, 2020. |
|
[32] |
S. Mahanta, M. Habib and
J. Moore, "Effect of High-Voltage Atmospheric Cold Plasma Treatment on
Germination and Heavy," 2022. [Online]. Available:
https://doi.org/10.3390/ijms23031611. |
|
[33] |
1. K. C. Proceedings.,
""Development of a cloud-based IoT monitoring system for Fish
metabolism and activity in aquaponics.," Aquacultural Engineering, 2020. |
|
[34] |
G. Del, R. Muenich and
M. Kalcic, "On the practical usefulness of least squares for assessing
uncertainty in hydrologic and," 2018, p. 286–295. |
|
[35] |
L. a. L. I. U. Z.
Zhanshan, "Feature selection algorithm based on XGBoost.," Journal
on Communications, 2019. |
|
[36] |
Ogunleye and Q. -G.
Wang, "XGBoost Model for Chronic Kidney Disease Diagnosis," XGBoost
Model for Chronic Kidney Disease Diagnosistional Biology and Bioinformatics,
2020, pp. 2131-2140. |
|
[37] |
S. S. R. &. S. M. (.
". f. i. t. r. i. u. r. f. a. a. n. n. m. I. J. o. R. M. 2. 3.-3. Gupta,
"Gupta, S., Sharma, R., & Singh, M. (2020). "Sales forecasting
in the retail industry using random forest and artificial neural network
models." International Journal of Retail Management, 22(4),
320-333.". |
|
[38] |
Song, L., Yang, J.,
& Chen, T. , "Seasonal patterns in consumer demand for organic
foods: A case study," Consumer Behavior, pp. 210-225, 2019. |
