Research on the application of Internet of Things (IoT) for water and fertilizer integration and smart irrigation system in cotton production

[1] Shukr, H. H., Pembleton, K. G., Zull, A. F., & Cockfield, G. J. (2021). Impacts of effects of deficit irrigation strategy on water use efficiency and yield in cotton under different irrigation systems. Agronomy, 11(2), 231. Search in Google Scholar

[2] Lonescu, L. M., Mazare, A. G., Serban, G., Visan, D., & Lita, A. (2018, October). Intelligent command of an underground irrigation and fertilization system. In 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging(SIITME) (pp. 306-309). IEEE. Search in Google Scholar

[3] Abioye, E. A., Hensel, O., Esau, T. J., Elijah, O., Abidin, M. S. Z., Ayobami, A. S., ... & Nasirahmadi, A. (2022). Precision irrigation management using machine learning and digital farming solutions. AgriEngineering, 4(1), 70-103. Search in Google Scholar

[4] Chen, X., Qi, Z., Gui, D., Gu, Z., Ma, L., Zeng, F., ... & Sima, M. W. (2019). A model-based real-time decision support system for irrigation scheduling to improve water productivity. Agronomy, 9(11), 686. Search in Google Scholar

[5] Zhu, F., Zhang, L., Hu, X., Zhao, J., Meng, Z., & Zheng, Y. (2023). Research and Design of Hybrid Optimized Backpropagation (BP) Neural Network PID Algorithm for Integrated Water and Fertilizer Precision Fertilization Control System for Field Crops. Agronomy, 13(5), 1423. Search in Google Scholar

[6] Jiang, Z., Yang, S., Dong, S., Pang, Q., Smith, P., Abdalla, M., ... & Xu, Y. (2023). Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning. Frontiers in plant science, 14, 1143462. Search in Google Scholar

[7] Touil, S., Richa, A., Fizir, M., Argente Garcia, J. E., & Skarmeta Gomez, A. F. (2022). A review on smart irrigation management strategies and their effect on water savings and crop yield. Irrigation and Drainage, 71(5), 1396-1416. Search in Google Scholar

[8] Feng, L., Wan, S., Zhang, Y., & Dong, H. (2024). Xinjiang cotton: Achieving super-high yield through efficient utilization of light, heat, water, and fertilizer by three generations of cultivation technology systems. Field Crops Research, 312, 109401. Search in Google Scholar

[9] Zhu, X., Chikangaise, P., Shi, W., Chen, W. H., & Yuan, S. (2018). Review of intelligent sprinkler irrigation technologies for remote autonomous system. International Journal of Agricultural & Biological Engineering, 11(1). Search in Google Scholar

[10] Yu, Y., Li, Z., & Gao, Z. (2020). Research and development of smart irrigation in China. Irrigation and Drainage, 69, 108-118. Search in Google Scholar

[11] Reddy, K. S., Ricart, S., Maruthi, V., Pankaj, P. K., Krishna, T. S., & Reddy, A. A. (2020). Economic assessment of water harvesting plus supplemental irrigation for improving water productivity of a pulse– cotton based integrated farming system in Telangana, India. Irrigation and Drainage, 69(1), 25-37. Search in Google Scholar

[12] Ambade, S. D., & Mishra, R. (2024). LEIFMCY: Deployment of an Efficient Low-Cost & Energy-Aware Multiparametric IoT-Based Fertilization and Irrigation Monitoring Model for Cotton Yield Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(6s), 804-818. Search in Google Scholar

[13] Hussain, S., Ahmad, A., Wajid, A., Khaliq, T., Hussain, N., Mubeen, M., ... & Nasim, W. (2020). Irrigation scheduling for cotton cultivation. Cotton Production and Uses: Agronomy, Crop Protection, and Postharvest Technologies, 59-80. Search in Google Scholar

[14] Chen, Y., Yu, Z., Han, Z., Sun, W., & He, L. (2023). A Decision-Making System for Cotton Irrigation Based on Reinforcement Learning Strategy. Agronomy, 14(1), 11. Search in Google Scholar

[15] Jiménez, A. F., Cárdenas, P. F., & Jiménez, F. (2022). Intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems at farm and district scales. Computers and Electronics in Agriculture, 192, 106635. Search in Google Scholar

[16] Sun, F., Ma, W., Li, H., & Wang, S. (2018, July). Research on water-fertilizer integrated technology based on neural network prediction and fuzzy control. In IOP Conference Series: Earth and Environmental Science (Vol. 170, p. 032168). IOP Publishing. Search in Google Scholar

[17] Zurweller, B. A., Rowland, D. L., Mulvaney, M. J., Tillman, B. L., Migliaccio, K., Wright, D., ... & Vellidis, G. (2019). Optimizing cotton irrigation and nitrogen management using a soil water balance model and in-season nitrogen applications. Agricultural water management, 216, 306-314. Search in Google Scholar

[18] Bin, L., Shahzad, M., Khan, H., Bashir, M. M., Ullah, A., & Siddique, M. (2023). Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology. Sustainability, 15(18), 13874. Search in Google Scholar

[19] Lu, Y., Liu, M., Li, C., Liu, X., Cao, C., Li, X., & Kan, Z. (2022). Precision fertilization and irrigation: Progress and applications. AgriEngineering, 4(3), 626-655. Search in Google Scholar

[20] Kaur, T., Sharma, P. K., Brar, A. S., Vashisht, B. B., & Choudhary, A. K. (2024). Optimizing crop water productivity and delineating root architecture and water balance in cotton–wheat cropping system through sub-surface drip irrigation and foliar fertilization strategy in an alluvial soil. Field Crops Research, 309, 109337. Search in Google Scholar

[21] Bwambale, E., Abagale, F. K., & Anornu, G. K. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review. Agricultural Water Management, 260, 107324. Search in Google Scholar

[22] Brahmanand, P. S., & Singh, A. K. (2022). Precision irrigation water management-current status, scope and challenges. Indian J. Fertil, 18, 372-380. Search in Google Scholar

[23] Shi, X. J., Hao, X. Z., Li, N. N., Li, J. H., Shi, F., Han, H. Y., ... & Luo, H. H. (2022). Organic liquid fertilizer coupled with single application of chemical fertilization improves growth, biomass, and yield components of cotton under mulch drip irrigation. Frontiers in Plant Science, 12, 763525. Search in Google Scholar

[24] Feng, L., Dai, J., Tian, L., Zhang, H., Li, W., & Dong, H. (2017). Review of the technology for high-yielding and efficient cotton cultivation in the northwest inland cotton-growing region of China. Field Crops Research, 208, 18-26. Search in Google Scholar

[25] Karaşahin, M., Dündar, Ö., & Samancı, A. (2018). The way of yield increasing and cost reducing in agriculture: Smart irrigation and fertigation. Turkish Journal of Agriculture-Food Science and Technology, 6(10), 1370-1380. Search in Google Scholar

[26] Wang, H., Zhang, L., Hu, X., Wang, H., & Zhang, X. (2023). Research on water and fertilizer PH control strategy of automatic fertilizer application system in cotton field. Search in Google Scholar

[27] Guo, Z., Zhu, F., Zhao, P., & Chen, H. (2024). BA-Optimized Variable Domain Fuzzy PID Control Algorithm for Water and Fertilizer Ratio Control System in Cotton Field. Processes, 12(6), 1202. Search in Google Scholar

[28] Mohanraj, I., Gokul, V., Ezhilarasie, R., & Umamakeswari, A. (2017, April). Intelligent drip irrigation and fertigation using wireless sensor networks. In 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR) (pp. 36-41). IEEE. Search in Google Scholar

[29] Chen, X., Qi, Z., Gui, D., Sima, M. W., Zeng, F., Li, L., ... & Gu, Z. (2020). Evaluation of a new irrigation decision support system in improving cotton yield and water productivity in an arid climate. Agricultural Water Management, 234, 106139. Search in Google Scholar

[30] Du, C., Zhang, L., Ma, X., Lou, X., Shan, Y., Li, H., & Zhou, R. (2021). A cotton high-efficiency water-fertilizer control system using wireless sensor network for precision agriculture. Processes, 9(10), 1693. Search in Google Scholar

[31] Yuan, Y., Wang, C., Zai, X., Song, Y., & Zhang, X. (2023). Optimizing fertilizer use for sustainable food systems: an evaluation of integrated water-fertilizer system adoption among cotton farmers in China. Frontiers in Sustainable Food Systems, 7, 1310426. Search in Google Scholar

[32] Kumar G. Kranthi,Bangare Manoj L.,Bangare Pushpa M.,Kumar Chanda Raj,Raj Roop,Arias Gonzáles José Luis... & Mia Md. Solaiman.(2024).Internet of things sensors and support vector machine integrated intelligent irrigation system for agriculture industry.Discover Sustainability(1). Search in Google Scholar

[33] Yu Zhongxing, Sang Shuguang, Zhou Yihua, Dai Yingying, Zhang Lu & Ouyang Hanqing. (2024). Investigation of a cloud platform-based control system for an integrated water and fertilizer machine. (eds.) University of Jinan (China). Search in Google Scholar

[34] Weibin You,Xiaoxu Xuan,Songying Chen,Joon Yong Yoon & Xun Sun.(2024).Experimental and numerical studies on the partial cavitation in a Venturi.Journal of Physics: Conference Series(1). Search in Google Scholar

[35] Dorin Bordeasu,Florin Dragan,Ioan Filip,Iosif Szeidert & Gelu Ovidiu Tirian.(2024).Estimation of Centrifugal Pump Efficiency at Variable Frequency for Irrigation Systems.Sustainability(10). Search in Google Scholar

[36] Hu Zhuo,Guo Weihao,Zhou Kege,Wang Lei,Wang Fu & Yuan Jinliang.(2024).Optimization of PID control parameters for marine dual-fuel engine using improved particle swarm algorithm.Scientific Reports(1),12681-12681. Search in Google Scholar

[37] Singh Arunesh Kumar, Tariq Tabish, Ahmer Mohammad F., Sharma Gulshan, Bokoro Pitshou N. & Shongwe Thokozani.(2022).Intelligent Control of Irrigation Systems Using Fuzzy Logic Controller. Energies(19),7199-7199. Search in Google Scholar