Green Growth: AI-Driven Intelligent Farming for Effective Resource Management

Javeria Rusool Chaudary; Muhammed Afzal; Kishwar Rasool; Muskan Maryam

Authors

Javeria Rusool Chaudary Department of Computer Science and IT, Ghazi University, D. G. Khan
Muhammed Afzal Department of Computer Science and IT, Ghazi University, D. G. Khan
Kishwar Rasool Department of Computer Science and IT, Ghazi University, D. G. Khan
Muskan Maryam Department of Computer Science and IT, Ghazi University, D. G. Khan

Keywords:

IoT Sensors, Microcontroller, Real-Time Monitoring, Cat Boost Regressor, Random Forest, Crop Optimization

Abstract

Effective fertilizer management plays a critical role in maximizing crop yield while reducing environmental harm and minimizing resource waste. This study presents an IoT-based intelligent fertilizer recommendation system designed to deliver accurate, real-time application guidance. The system integrates NPK sensors for soil nutrient detection, environmental sensors for humidity and temperature monitoring, and rain gauges to collect precipitation data. Data from the field is transmitted through an Arduino microcontroller to a cloud platform. A Random Forest classifier is used to determine the need for fertilization, while a CatBoost regressor estimates the required fertilizer quantity. The system was tested using real-time field data across 22 crop types, achieving 100% accuracy in classification and strong performance in regression tasks. Recommendations are automated and delivered via SMS to streamline field operations. The objective of this study is to develop an automated, sensor-driven fertilizer recommendation system using machine learning for precision agriculture. The novelty lies in the integration of real-time IoT sensing with hybrid AI models to optimize fertilizer use. This approach enhances productivity, reduces input waste, and supports environmentally sustainable farming.

References

P. R. Harshani, T. Umamaheswari, R. Tharani, S. Rajalakshmi, and J. Dharani, “Effective Crop Productivity and Nutrient Level Monitoring in Agriculture Soil Using IOT,” ICSNS 2018 - Proc. IEEE Int. Conf. Soft-Computing Netw. Secur., Dec. 2018, doi: 10.1109/ICSNS.2018.8573674.

S.-K. K. Divya Shanmugavel, Iryna Rusyn, Omar Solorza-Feria, “Sustainable SMART fertilizers in agriculture systems: A review on fundamentals to in-field applications,” Sci. Total Environ., vol. 904, p. 166729, 2023, doi: https://doi.org/10.1016/j.scitotenv.2023.166729.

K. N. Bhanu, H. J. Jasmine, and H. S. Mahadevaswamy, “Machine learning Implementation in IoT based Intelligent System for Agriculture,” 2020 Int. Conf. Emerg. Technol. INCET 2020, Jun. 2020, doi: 10.1109/INCET49848.2020.9153978.

N. S. R. Tamoghna Ojha, Sudip Misra, “Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges,” Comput. Electron. Agric., vol. 118, pp. 66–84, 2015, doi: https://doi.org/10.1016/j.compag.2015.08.011.

H. M. S. K. Herath, D. Kumaragamage, and S. P. Indraratne, “Performance of an Optimized Nutrient Management Approach for Tomato in Central Sri Lanka,” Commun. Soil Sci. Plant Anal., vol. 44, no. 20, pp. 3049–3060, Nov. 2013, doi: 10.1080/00103624.2013.830122;REQUESTEDJOURNAL:JOURNAL:LCSS20;WGROUP:STRING:PUBLICATION.

K. Kaack and H. L. Pedersen, “Effects Of Potassium, Phosphorus And Nitrogen Fertilization On Endogenous Ethylene And Quality Characteristics Of Apples (Malus Domestica L.),” J. Plant Nutr., vol. 37, no. 7, pp. 1148–1155, 2014, doi: 10.1080/01904167.2013.868484.

R. A. Shah and S. Kumar, “Direct and Residual Effect of Integrated Nutrient Management and Economics in Hybrid Rice Wheat Cropping System,” Environ. Sci, 2014.

M. M, P. N., B. Madhavi, T. Ramashri, and G. Haritha, “An Efficient Low Cost Iot Based Fertilizer Intimation System for Mulberry Plants,” SSRN Electron. J., Feb. 2023, doi: 10.2139/SSRN.4346092.

Y. K. Raj Kishan Agrahari, “Smart fertilizer management: the progress of imaging technologies and possible implementation of plant biomarkers in agriculture,” Soil Sci. Plant Nutr., vol. 67, no. 3, 2021, doi: https://doi.org/10.1080/00380768.2021.1897479.

V. T. P. Wei Zhao, Meini Wang, “Unmanned Aerial Vehicle and Geospatial Analysis in Smart Irrigation and Crop Monitoring on IoT Platform,” Mob. Inf. Syst., 2023, doi: https://doi.org/10.1155/2023/4213645.

C. L. Wei Li, Muhammad Awais, Weimin Ru, Weidong Shi, Muhammad Ajmal, Saad Uddin, “Review of Sensor Network-Based Irrigation Systems Using IoT and Remote Sensing,” Adv. Meteorol., 2020, [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1155/2020/8396164

I. W. M. Bastiaanssen, Wim G.M, David J Molden, “Remote sensing for irrigated agriculture: examples from research and possible applications,” Agric. Water Manag., vol. 46, no. 2, pp. 137–155, 2000, doi: https://doi.org/10.1016/S0378-3774(00)00080-9.

O. Diallo, J. J. P. C. Rodrigues, M. Sene, and J. Lloret, “Distributed database management techniques for wireless sensor networks,” IEEE Trans. Parallel Distrib. Syst., vol. 26, no. 2, pp. 604–620, Feb. 2015, doi: 10.1109/TPDS.2013.207.

F. A. Alaba, M. Othman, I. A. T. Hashem, and F. Alotaibi, “Internet of Things security: A survey,” J. Netw. Comput. Appl., vol. 88, pp. 10–28, Jun. 2017, doi: 10.1016/J.JNCA.2017.04.002.

R. Maheswari, H. Azath, P. Sharmila, and S. Sheeba Rani Gnanamalar, “Smart Village: Solar Based Smart Agriculture with IoT Enabled for Climatic Change and Fertilization of Soil,” 2019 IEEE 5th Int. Conf. Mechatronics Syst. Robot. ICMSR 2019, pp. 102–105, May 2019, doi: 10.1109/ICMSR.2019.8835454.

M. de la L. M. Marcela Calabi-Floody, Jorge Medina, Cornelia Rumpel, Leo M. Condron, Marcela Hernandez, Marc Dumont, “Chapter Three - Smart Fertilizers as a Strategy for Sustainable Agriculture,” Adv. Agron., vol. 147, pp. 119–157, 2018, doi: https://doi.org/10.1016/bs.agron.2017.10.003.

J. Sun, A. M. Abdulghani, M. A. Imran, and Q. H. Abbasi, “IoT Enabled Smart Fertilization and Irrigation Aid for Agricultural Purposes,” ACM Int. Conf. Proceeding Ser., pp. 71–75, Apr. 2020, doi: 10.1145/3398329.3398339;TOPIC:TOPIC:CONFERENCE-COLLECTIONS>CNIOT;PAGE:STRING:ARTICLE/CHAPTER.

N. M. Basavaraju, U. B. Mahadevaswamy, and S. Mallikarjunaswamy, “Design and Implementation of Crop Yield Prediction and Fertilizer Utilization Using IoT and Machine Learning in Smart Agriculture Systems,” 2nd IEEE Int. Conf. Networks, Multimed. Inf. Technol. NMITCON 2024, 2024, doi: 10.1109/NMITCON62075.2024.10699184.

W. X. TANG Han, WANG Jinwu, XU Changsu, ZHOU Wenqi, WANG Jinfeng, “Research Progress Analysis on Key Technology of Chemical Fertilizer Reduction and Efficiency Increase,” Nongye Jixie Xuebao/Transactions Chinese Soc. Agric. Mach., vol. 50, no. 4, 2019, [Online]. Available: https://www.nyjxxb.net/index.php/journal/article/view/792

E. R. Youness Tace, Mohamed Tabaa, Sanaa Elfilali, Cherkaoui Leghris, Hassna Bensag, “Smart irrigation system based on IoT and machine learning,” Energy Reports, vol. 8, no. 9, pp. 1025–1036, 2022, doi: https://doi.org/10.1016/j.egyr.2022.07.088.

W. Z. Xinpeng Xu, Ping He a, Mirasol F. Pampolino, Adrian M. Johnston, Shaojun Qiu, Shicheng Zhao, Limin Chuan, “Fertilizer recommendation for maize in China based on yield response and agronomic efficiency,” F. Crop. Res., vol. 157, pp. 27–34, 2014, doi: https://doi.org/10.1016/j.fcr.2013.12.013.

F. R. L. Tawseef Ayoub Shaikh, Tabasum Rasool, “Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming,” Comput. Electron. Agric., vol. 198, p. 107119, 2022, doi: https://doi.org/10.1016/j.compag.2022.107119.

E. M. Abdel-Rahman, F. B. Ahmed, and R. Ismail, “Random forest regression and spectral band selection for estimating sugarcane leaf nitrogen concentration using EO-1 Hyperion hyperspectral data,” Int. J. Remote Sens., vol. 34, no. 2, pp. 712–728, Jan. 2013, doi: 10.1080/01431161.2012.713142;JOURNAL:JOURNAL:TRES20.

M. L. Junfei Chen, “Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast,” Math. Probl. Eng., 2012, doi: https://doi.org/10.1155/2012/915053.

W. Z. Jian Gao, “A Fertilization Decision Model for Maize, Rice, and Soybean Based on Machine Learning and Swarm Intelligent Search Algorithms,” Agronomy, vol. 13, no. 5, p. 1400, 2023, doi: https://doi.org/10.3390/agronomy13051400.

J. B. BAUTISTA, MARK JOHN B., CRUZ, JERSHWIN MARC A., PAGULAYAN, EUNICE VIELLE L, TABOR, DENNIS JUSTIN, FUENTES, JUSTINE5, SHEINN REYES, “UREA: A Case Study on Identifying Organic Fertilizer, Nutrients based on Color Characteristics Using Random Forest Algorithm for Small-scale Vegetable Farms,” IRE Journals, vol. 7, no. 12, 2024, [Online]. Available: https://www.irejournals.com/formatedpaper/1705902.pdf

D. S. & G. I.-B. Yvette Everingham, Justin Sexton, “Accurate prediction of sugarcane yield using a random forest algorithm,” Agron. Sustain. Dev., vol. 36, no. 27, 2016, doi: https://doi.org/10.1007/s13593-016-0364-z.

R. S. Pavithra Mahesh, “Yield prediction for crops by gradient-based algorithms,” Plosone, 2024, doi: https://doi.org/10.1371/journal.pone.0291928.

K. O. Md Reazul Islam, “Machine learning enabled IoT system for soil nutrients monitoring and crop recommendation,” J. Agric. Food Res., vol. 14, p. 100880, 2023, doi: https://doi.org/10.1016/j.jafr.2023.100880.

M. A. E.-R. Nermeen Gamal Rezk, Abdel-Fattah Attia, “An Efficient IoT-based Crop Damage Prediction Framework in Smart Agricultural Systems,” Res. Sq., vol. 8, 2024, [Online]. Available: https://www.researchsquare.com/article/rs-4697940/v1

Atharva Ingle, “Crop Recommendation Dataset,” Kaggle, 2025, [Online]. Available: https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset

“cropsheets”, [Online]. Available: https://aesl.ces.uga.edu/publications/soil/cropsheets.pdf