Design of BQN-based decision support system and HSCNN-GPOR futuristic prediction for precision agriculture using IoT
References
- Manida. M., & Nedumaran. G., (2020). Agriculture in India: Information about Indian Agriculture & Its Importance. Aegaeum Journal, 8(3), 729–736.
- Nishant, Potnuru Sai, et al., (2020). Crop yield prediction based on Indian agriculture using machine learning. 2020 International Conference for Emerging Technology (INCET), IEEE, DOI: 10.1109/INCET49848.2020.9154036.
- Akhter, Ravesa, & Shabir Ahmad Sofi, (2021). Precision agriculture using IoT data analytics and machine learning. Journal of King Saud University-Computer and Information Sciences, DOI: 10.1016/j.jksuci.2021.05.013.
- Mir, Rayees Afzal, & Mohsina Ishrat, (2020). WIDE-AREA AGRICULTURAL ADVANCED MONITORING AND PREDICTION SYSTEM USING IOT AND MACHINE LEARNING.” International Journal of Management (IJM), 11(8),
http://dx.doi.org/10.22581/muet1982.2401.2806 . - Aryal, Jeetendra Prakash, et al., (2020). Climate change and agriculture in South Asia: Adaptation options in smallholder production systems. Environment, Development and Sustainability, 22(6), 5045–5075,
https://link.springer.com/article/10.1007/s10668-019-00414-4 . - Serrano, João, et al., (2020). Climate changes challenges to the management of Mediterranean montado ecosystem: Perspectives for use of precision agriculture technologies. Agronomy, 10(2), 218, DOI: 10.3390/agronomy10020218.
- Zhai, Zhaoyu, et al., (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 05256, DOI: 10.1016/j.compag.2020.105256.
- Gallardo, Marisa, Antonio Elia, Rodney B, & Thompson, (2020). Decision support systems and models for aiding irrigation and nutrient management of vegetable crops. Agricultural Water Management, 240, 106209, DOI: 10.1016/j.agwat.2020.106209.
- Ara, Iffat, et al., (2021). Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review. Agricultural Water Management, 257, 07161, DOI: 10.1016/j.agwat.2021.107161.
- Torres-Sanchez, Roque, et al., (2020). A decision support system for irrigation management: Analysis and implementation of different learning techniques. Water, 12(2), 548, DOI: 10.3390/w12020548.
- Aghaloo, Kamaleddin, & Yie-Ru Chiu, (2020). Identifying optimal sites for a rainwater-harvesting agricultural scheme in iran using the best-worst method and fuzzy logic in a GIS-based decision support system. Water, 12(7), 913, DOI: 10.3390/w12071913.
- Sujatha R, et al., (2021). Performance of deep learning vs machine learning in plant leaf disease detection. Microprocessors and Microsystems, 80, 03615, DOI: 10.1016/j.micpro.2020.103615.
- Jadhav, Sachin. B., Vishwanath, R., Udupi, Sanjay, B., & Patil, (2021). Identification of plant diseases using convolutional neural networks. International Journal of Information Technology, 13(6), 2461–2470, DOI: 10.1109/ACCESS.2022.3141371.
- Radovanović, Draško, & Slobodan Đukanovic, (2020). Image-based plant disease detection: a comparison of deep learning and classical machine learning algorithms. 2020 24th International conference on information technology (IT), IEEE, DOI: 10.1109/IT48810.2020.9070664.
- Abdu, Aliyu Muhammad, Musa Mohd Mokji, & Usman Ullah Sheikh, (2020). Automatic vegetable disease identification approach using individual lesion features. Computers and Electronics in Agriculture, 176, 05660, DOI: 10.1016/j.compag.2020.105660.
- Campoverde, Luis Miguel Samaniego, Mauro Tropea, & Floriano De Rango, (2021). An IoT based smart irrigation management system using reinforcement learning modeled through a Markov decision process. 2021 IEEE/ACM 25th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), IEEE, DOI: 10.1109/DS-RT52167.2021.9576130.
- Lizana, Fernando, et al., (2020). Building a Text Messaging-Based System to Support Low-Cost Automation in Household Agriculture. 2020 Congreso Estudiantil de Electrónica y Electricidad (INGELECTRA), IEEE, DOI: 10.1109/INGELECTRA50225.2020.246967.
- Franceschelli, Leonardo, et al., (2020). A non-invasive soil moisture sensing system electronic architecture: A real environment assessment. Sensors, 20(21), 6147, DOI: 10.3390/s20216147.
- Nie, Xuan, et al., (2019). Strawberry verticillium wilt detection network based on multi-task learning and attention. IEEE Access, 7, 170003–170011, DOI: 10.1109/ACCESS.2019.2954845.
- Zhang, Li, et al., (2019). Multi-task cascaded convolutional networks based intelligent fruit detection for designing automated robot. IEEE Access, 7, 56028–56038, DOI: 10.1109/ACCESS.2019.2899940.
- Li, W., Clark, B., Taylor, J.A., Kendall, H., Jones, G., Li, Z., Jin, S., Zhao, C., Yang, G., Shuai, C., & Cheng, X., (2020). A hybrid modelling approach to understanding adoption of precision agriculture technologies in Chinese cropping systems. Computers and Electronics in Agriculture, 172, 105305, DOI: 10.1016/j.compag.2020.105305.
- Torky, M., & Hassanein, A.E., (2020). Integrating blockchain and the internet of things in precision agriculture: Analysis, opportunities, and challenges. Computers and Electronics in Agriculture, 178, 105476, DOI: 10.1016/j.compag.2020.105476.
- Sahu, P., Chug, A., Singh, A.P., Singh, D., & Singh, R.P., (2021). Challenges and Issues in Plant Disease Detection Using Deep Learning. Handbook of Research on Machine Learning Techniques for Pattern Recognition and Information Security, 56–74, 10.4018/978-1-7998-3299-7.ch004.
- Akhter, R., & Sofi, S.A., (2022). Precision agriculture using IoT data analytics and machine learning. Journal of King Saud University-Computer and Information Sciences, 34(8), 5602–5618, DOI: 10.1016/j.jksuci.2021.05.013.
- Kouadri, S., Pande, C.B., Panneerselvam, B., Moharir, K.N., & Elbeltagi, A., (2022). Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models. Environmental Science and Pollution Research, 29(14), 21067–21091, DOI: 10.1007/s11356-021-17084-3.
- Panigrahi, K.P., Das, H., Sahoo, A.K., & Moharana, S.C., (2020). Maize leaf disease detection and classification using machine learning algorithms. In Progress in Computing, Analytics and Networking, 659–669. Springer, Singapore,
http://dx.doi.org/10.1007/978-981-15-2414-1_66 .
DOI: https://doi.org/10.2478/ijssis-2025-0050 | Journal eISSN: 1178-5608
Language: English
Submitted on: Oct 9, 2024
Published on: Oct 4, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Related subjects:
© 2025 Sneha M. Khupse, Prabhakar L. Ramteke, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.