Precision Agriculture based on Machine Learning and Remote Sensing Techniques

Authors

  • Fahad Alaieri Department of Management Information Systems and Production Management, College of Business and Economics, Qassim University, Saudi Arabia
Volume: 14 | Issue: 3 | Pages: 14206-14211 | June 2024 | https://doi.org/10.48084/etasr.6986

Abstract

In today's rapidly evolving agricultural landscape, the integration of precision techniques and data-driven approaches has become essential, driven by technological innovations, such as the Internet of Things (IoT), Artificial Intelligence (AI), and cutting-edge aerial and satellite technologies. Precision agriculture aims to maximize productivity by closely monitoring soil health and employing advanced machine learning methods for precise data analysis. This study explores the evaluation of soil quality, placing particular emphasis on leveraging remote sensing technology to collect comprehensive data and imagery to analyze soil conditions related to olive cultivation. By harnessing cloud platforms integrated with satellite data, several analytical tools are made available, offering valuable insights for informed decision-making and operational efficiency across various sectors. Furthermore, this study introduces an AI-driven application tailored to predict the soil moisture levels. This application facilitates in-depth analysis, feature extraction, and the prediction of different vegetation indices using time-series satellite imagery. The study's findings highlight the exceptional accuracy achieved by the decision tree and extra tree regression models, with soil moisture estimation reaching approximately 91%, underscoring the importance and effectiveness of the proposed method in advancing agricultural practices.

Keywords:

remote sensing, precision agriculture, artificial intelligence, soil moisture

Downloads

Download data is not yet available.

References

J. R. Irons, J. L. Dwyer, and J. A. Barsi, "The next Landsat satellite: The Landsat Data Continuity Mission," Remote Sensing of Environment, vol. 122, pp. 11–21, Jul. 2012.

A. Massaoudi, A. Berguiga, A. Harchay, M. Ben Ayed, and H. Belmabrouk, "Spectral and Energy Efficiency Trade-Off in UAV-Based Olive Irrigation Systems," Applied Sciences, vol. 13, no. 19, Jan. 2023, Art. no. 10739.

L. Yang et al., "Effects of Superabsorbent Polymers on Infiltration and Evaporation of Soil Moisture Under Point Source Drip Irrigation," Irrigation and Drainage, vol. 64, no. 2, pp. 275–282, 2015.

W. Dorigo et al., "A New International Network for in Situ Soil Moisture Data," Eos, Transactions American Geophysical Union, vol. 92, no. 17, pp. 141–142, 2011.

M. N. Navidi, J. Seyedmohammadi, and S. A. Seyed Jalali, "Predicting soil water content using support vector machines improved by meta-heuristic algorithms and remotely sensed data," Geomechanics and Geoengineering, vol. 17, no. 3, pp. 712–726, May 2022.

S. A. B. Anas, R. S. S. Singh, and N. A. B. Kamarudin, "Designing an IoT Agriculture Monitoring System for Improving Farmer’s Acceptance of Using IoT Technology," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 8157–8163, Feb. 2022.

J. Guo, Q. Bai, W. Guo, Z. Bu, and W. Zhang, "Soil moisture content estimation in winter wheat planting area for multi-source sensing data using CNNR," Computers and Electronics in Agriculture, vol. 193, p. 106670, Feb. 2022.

Y. A, G. Wang, P. Hu, X. Lai, B. Xue, and Q. Fang, "Root-zone soil moisture estimation based on remote sensing data and deep learning," Environmental Research, vol. 212, Sep. 2022, Art. no. 113278.

N. C. Eli-Chukwu, "Applications of Artificial Intelligence in Agriculture: A Review," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4377–4383, Aug. 2019.

S. Adeli, B. Salehi, M. Mahdianpari, L. J. Quackenbush, and B. Chapman, "Moving Toward L-Band NASA-ISRO SAR Mission (NISAR) Dense Time Series: Multipolarization Object-Based Classification of Wetlands Using Two Machine Learning Algorithms," Earth and Space Science, vol. 8, no. 11, 2021, Art. no. e2021EA001742.

N. C. Kundur and P. B. Mallikarjuna, "Deep Convolutional Neural Network Architecture for Plant Seedling Classification," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9464–9470, Dec. 2022.

M. Xing, L. Chen, J. Wang, J. Shang, and X. Huang, "Soil Moisture Retrieval Using SAR Backscattering Ratio Method during the Crop Growing Season," Remote Sensing, vol. 14, no. 13, 2022.

S. Chatterjee, J. Huang, and A. E. Hartemink, "Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data," Remote Sensing, vol. 12, no. 8, 2020.

J. Ezzahar et al., "Evaluation of Backscattering Models and Support Vector Machine for the Retrieval of Bare Soil Moisture from Sentinel-1 Data," Remote Sensing, vol. 12, no. 1, 2020.

Y. Wang, J. Yang, Y. Chen, P. De Maeyer, Z. Li, and W. Duan, "Detecting the Causal Effect of Soil Moisture on Precipitation Using Convergent Cross Mapping," Scientific Reports, vol. 8, no. 1, Art. no. 12171, Aug. 2018.

S. K. Dash and R. Sinha, "Spatiotemporal dynamics and interrelationship between soil moisture and groundwater over the Critical Zone Observatory in the Central Ganga plain, North India," Hydrology and Earth System Sciences Discussions, pp. 1–32, Feb. 2022.

O. Eroglu, M. Kurum, D. Boyd, and A. C. Gurbuz, "High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks," Remote Sensing, vol. 11, no. 19, Jan. 2019, Art. no. 2272.

Q. Yuan, H. Xu, T. Li, H. Shen, and L. Zhang, "Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental U.S," Journal of Hydrology, vol. 580, Jan. 2020, Art. no. 124351.

A. Sure and O. Dikshit, "Estimation of root zone soil moisture using passive microwave remote sensing: A case study for rice and wheat crops for three states in the Indo-Gangetic basin," Journal of Environmental Management, vol. 234, pp. 75–89, Mar. 2019.

K. Edokossi, A. Calabia, S. Jin, and I. Molina, "GNSS-Reflectometry and Remote Sensing of Soil Moisture: A Review of Measurement Techniques, Methods, and Applications," Remote Sensing, vol. 12, no. 4, Jan. 2020, Art. no. 614.

N. Ye et al., "The Soil Moisture Active Passive Experiments: Validation of the SMAP Products in Australia," IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2922–2939, Jul. 2021.

R. L. F. Cunha, B. Silva, and M. A. S. Netto, "A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast," in 2018 IEEE 14th International Conference on e-Science (e-Science), Amsterdam, Netherlands, Nov. 2018, pp. 423–430.

A. A. Sarangdhar and V. R. Pawar, "Machine learning regression technique for cotton leaf disease detection and controlling using IoT," in 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, Apr. 2017, vol. 2, pp. 449–454.

P. K. Dutta and S. Mitra, "Application of Agricultural Drones and IoT to Understand Food Supply Chain During Post COVID-19," in Agricultural Informatics, John Wiley & Sons, Ltd, 2021, pp. 67–87.

G. Ruß and R. Kruse, "Regression Models for Spatial Data: An Example from Precision Agriculture," in Advances in Data Mining. Applications and Theoretical Aspects, Berlin, Germany, 2010, pp. 450–463.

T. Waheed, R. B. Bonnell, S. O. Prasher, and E. Paulet, "Measuring performance in precision agriculture: CART-A decision tree approach," Agricultural Water Management, vol. 84, no. 1, pp. 173–185, Jul. 2006.

V. Sirisha and G. Sahitya, "Smart irrigation system for the reinforcement of Precision agriculture using prediction algorithm: SVR based smart irrigation," in 2021 6th International Conference on Inventive Computation Technologies (ICICT), Jan. 2021, pp. 1059–1066.

M. K. Sudha, M. Manorama, and T. Aditi, Eds., "Smart Agricultural Decision Support Systems for Predicting Soil Nutrition Value Using IoT and Ridge Regression," AGRIS on-line Papers in Economics and Informatics, vol. XIV, no. 1, pp. 95–106, 2022.

María Fernanda Restrepo Suescún, "Machine learning approaches for tomato crop yield prediction in precision agriculture," NOVA Information Management School, Lisbon, Portugal, Aug. 2021.

Z. Chen and P. de Boves Harrington, "Self-Optimizing Support Vector Elastic Net," Analytical Chemistry, vol. 92, no. 23, pp. 15306–15316, Dec. 2020.

Downloads

How to Cite

[1]
F. Alaieri, “Precision Agriculture based on Machine Learning and Remote Sensing Techniques”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14206–14211, Jun. 2024.

Metrics

Abstract Views: 127
PDF Downloads: 81

Metrics Information