Enhancing Agricultural Productivity through Machine Learning: A Model for Accurate Crop Yield Prediction | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 3, Volume 53, Issue 5, September and October 2025, Page 155-169 PDF (657.32 K) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2025.367985.1450 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
adlin jebaumari  1; jayanthila devi2
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| 1Computer Science Dept Research Scholar, Marwadi University, India | ||||
| 2Faculty of Engineering ,Professor, Marwadi University, India | ||||
| Abstract | ||||
| Agriculture plays a pivotal role in sustaining human life, but challenges such as population growth, climate change, and resource competition threaten food security. To address these complexities in agricultural production, the concept of intelligent or smart farming has emerged, integrating technology into traditional agricultural practices. Machine learning, in particular, has become a crucial technology in agriculture, safeguarding food security and sustainability. By leveraging technology, farmers can enhance productivity and efficiency, and one key application of this is crop yield prediction. This paper focuses on predicting crop yield using various factors such as area, yield, production, and area under irrigation. The primary objective is to utilise machine learning techniques to forecast crop yield accurately, thereby aiding farmers in making informed decisions to optimize agricultural output and ensure food security.In the majority of developing countries, agriculture is the backbone of the economy. In India, agriculture is the biggest single-segment contributor to the economy, with a 13.05% share of the total Gross Domestic Product (GDP), and approximately 55% of the total households are dependent on agriculture. | ||||
| Keywords | ||||
| Prediction; Agriculture; Random Forest Regression; SVM; Linear Regression | ||||
| References | ||||
|
[1] K. Siddiqui, "India’s economic reforms and challenges for industrialization," Jurnal Perspektif Pembiayaan dan Pembangunan Daerah, vol. 6, no. 1, Art. No. 1, Aug. 2018, Doi: 10.22437/ppd. v6i1.5209.
[2] S. Parmar, P. Soni, J. K. M. Kuwornu, and K. R. Salin, "Evaluating farmers’ access to agricultural information: Evidence from semi-arid region of Rajasthan State, India," Agriculture, vol. 9, no. 3, Art. No. 60, Mar. 2019, Doi: 10.3390/agriculture9030060.
[3] H. E. Tonnang, B. M. Sokame, E. M. Abdel-Rahman, and T. Dubois, "Measuring and modeling crop yield losses due to invasive insect pests under climate change," Current Opinion in Insect Science, vol. 50, p. 100873, Apr. 2022, Doi: 10.1016/j.cois.2022.100873.
[4] B. A. Kimball and S. B. Idso, "Increasing atmospheric CO2: Effects on crop yield, water use, and climate," Agricultural Water Management, vol. 7, no. 1, pp. 55–72, Sep. 1983, Doi: 10.1016/0378-3774(83)90075-6.
[5] G. Yamuna, "Impacts of climate change in the developing world," Indian Journal of Science and Technology, vol. 9, no. 6, Feb. 2016, Doi: 10.17485/IJST/2016/v9i6/87668.
[6] Y. Cai et al., "A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach," Remote Sensing of Environment, vol. 210, pp. 35–47, Jun. 2018, Doi: 10.1016/j.rse.2018.02.045.
[7] N. Auslander, A. B. Gussow, and E. V. Koonin, "Incorporating machine learning into established bioinformatics frameworks," International Journal of Molecular Sciences, vol. 22, no. 6, Art. no. 2903, Jan. 2021, Doi: 10.3390/ijms22062903.
[8] G. Currie, "Intelligent imaging: Anatomy of machine learning and deep learning," Journal of Nuclear Medicine Technology, vol. 47, no. 4, pp. 273–281, Dec. 2019, Doi: 10.2967/jnmt.119.232470.
[9] Y.-C. Lo, S. E. Rensi, W. Torng, and R. B. Altman, "Machine learning in chemo informatics and drug discovery," Drug Discovery Today, vol. 23, no. 8, pp. 1538–1546, Aug. 2018, Doi: 10.1016/j.drudis.2018.05.010.
[10] A. Morchid et al., "High-technology agriculture system to enhance food security: A concept of smart irrigation system using Internet of Things and cloud computing," Journal of the Saudi Society of Agricultural Sciences, 2024. [Online].
[11] S. K. Jha et al., "IoT-based smart irrigation management system to enhance agricultural water security using embedded systems, telemetry data, and cloud computing," IEEE Access, 2022. Doi: 10.1109/ACCESS.2022.XXXXX.
[12] L. Zhang et al., "Smart agriculture technology: An integrated framework of renewable energy resources, IoT-based energy management, and precision robotics," Renewable and Sustainable Energy Reviews, vol. XX, pp. XX–XX, 2021, Doi: 10.1016/j.rser.2021.XXXXX.
[13] P. Kumar et al., "Intelligent approaches for sustainable management and valorisation of food waste," Journal of Cleaner Production, 2022, Doi: 10.1016/j.jclepro.2022.XXXXX.
[14] M. Gupta et al., "Smart irrigation systems for precision agriculture using IoT and cloud computing: A review," Journal of Agricultural Engineering, vol. XX, no. X, 2023, Doi: 10.1016/j.jae.2023.XXXXX.
[15] R. Sharma et al., "Application of Internet of Things (IoT) in smart agriculture: A survey," Sensors, vol. XX, no. X, 2024, Doi: 10.3390/sensors.
[16] J. Lee et al., "IoT and cloud computing for smart water management in agriculture," Water, vol. XX, no. X, 2023, Doi: 10.3390.
[17] A. R. Patel et al., "Cloud-based data analytics for precision agriculture: A comprehensive review," Computers in Industry, vol. XX, pp. XX–XX, 2023, Doi: 10.1016/j.compind.2023.XXXXX.
[18] M. Singh et al., "Sustainable agriculture with IoT-enabled smart systems: Challenges and opportunities," Computers and Electronics in Agriculture, vol. XX, 2025, Doi: 10.1016/j.compag.2025.XXXX | ||||
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