A Data-Driven Analysis of Machine Learning Classification Models for Reliable Crop Yield Prediction

Year : 2026 | Volume : 15 | Issue : 01 | Page : 12 18
    By

    Mr. Raghunath Maji,

  • Mr. Swarup Ghosh,

  • Mr. Dipankar Barui,

  • Mr. Sourav Chowdhury,

  • Ms. Shreya Patra,

  • Dr. Biswajit Gayen,

  1. Assistant Professor, Department of Computer Science and Engineering Greater Kolkata College of Engineering and Management Baruipur, Kolkata-700144, India
  2. Student, Department of Computer Science and Engineering Greater Kolkata College of Engineering and Management Baruipur, Kolkata-700144, India
  3. Assistant Professor, Department of AI & ML, St. Thomas’ College of Engineering and Technology Diamond Harbour Rd, Kidderpore, Kolkata, West Bengal 700023, India
  4. Student, Department of Computer Science and Engineering Greater Kolkata College of Engineering and Management Baruipur, Kolkata-700144, India
  5. Student, Department of Computer Science and Engineering Dr. Sudhir Chandra Sur Institute of Technology and Sports Complex Dum Dum, Kolkata-700074 WB, India
  6. Assistant Professor, at Department of Basic Science Greater Kolkata College of Engineering and Management Baruipur, Kolkata-700144, WB,, India

Abstract

The adoption of ML technologies in agriculture is reshaping farming practices, empowering producers to make informed, data-oriented decisions that improve yields, sustainability, and long-term resilience. In mango cultivation, ML analyzes data from weather, soil, and pests to optimize irrigation, fertilization, and pest control. Predictive analytics help forecast ideal farming practices, minimizing resource wastage and improving yield. Real-time monitoring and image-based disease detection allow timely interventions to maintain plant health and fruit quality. After harvesting, machine learning improves supply chain operations by forecasting market needs and limiting product deterioration. Combined with satellite imagery and drones, ML supports precision and eco-friendly farming. Additionally, ML-based fertilization and pest detection reduce chemical use and promote sustainability. Integration with blockchain ensures transparency and food safety. Overall, ML empowers mango farmers with precision tools to improve crop resilience, efficiency, and profitability amid changing climatic conditions. The adoption of ML-based decision support systems encourages data-backed planning rather than traditional intuition-driven farming, assisting farmers in selecting suitable mango varieties, optimizing planting density, and scheduling harvest operations to maximize market value. ML-powered mobile and cloud platforms enhance accessibility for small and marginal farmers by providing real-time insights, alerts, and recommendations at a low cost. By integrating historical trends with real-time sensor data, ML helps reduce uncertainty in farming operations and improves risk management. As climate variability intensifies, such intelligent systems play a critical role in ensuring stable production and long-term agricultural sustainability. In addition, continuous model learning enables adaptive responses to evolving field conditions, ensuring scalable deployment across diverse agro-climatic zones and production systems, ultimately strengthening food security while supporting farmer livelihoods and environmental conservation

Keywords: Machine learning, precision farming, mango production, crop yield, predictive analytics, sustainability, real-time monitoring, disease detection, post-harvest optimization, satellite imagery, drone technology, soil fertilization, blockchain, agricultural resilience, climate change

[This article belongs to Research and Reviews : Journal of Crop science and Technology ]

How to cite this article:
Mr. Raghunath Maji, Mr. Swarup Ghosh, Mr. Dipankar Barui, Mr. Sourav Chowdhury, Ms. Shreya Patra, Dr. Biswajit Gayen. A Data-Driven Analysis of Machine Learning Classification Models for Reliable Crop Yield Prediction. Research and Reviews : Journal of Crop science and Technology. 2026; 15(01):12-18.
How to cite this URL:
Mr. Raghunath Maji, Mr. Swarup Ghosh, Mr. Dipankar Barui, Mr. Sourav Chowdhury, Ms. Shreya Patra, Dr. Biswajit Gayen. A Data-Driven Analysis of Machine Learning Classification Models for Reliable Crop Yield Prediction. Research and Reviews : Journal of Crop science and Technology. 2026; 15(01):12-18. Available from: https://journals.stmjournals.com/rrjocst/article=2026/view=235668


References

  1. Bhanu KN, Jasmine HJ, Mahadevaswamy HS. Machine learning implementation in IoT-based intelligent system for agriculture. In: Proc Int Conf Emerg Technol (INCET); 2020. p. 1–5. doi: 10.1109/INCET49848.2020.9153978.
  2. Sharma A, Jain A, Gupta P, Chowdary V. Machine learning applications for precision agriculture: a comprehensive review. IEEE Access. 2021;9:4843–4873. doi: 10.1109/ACCESS.2020.3048415.
  3. Raghuvanshi A, Singh U, Sajja G, Pallathadka H, Asenso E, Kamal M, et al. Intrusion detection using machine learning for risk mitigation in IoT-enabled smart irrigation in smart farming. J Food Qual. 2022;2022:1–8. doi: 10.1155/2022/3955514.
  4. Killeen P, Kiringa I, Yeap T, Branco P. Corn grain yield prediction using UAV-based high spatiotemporal resolution imagery, machine learning, and spatial cross-validation. Remote Sens. 2024;16(4):683. doi: 10.3390/rs16040683.
  5. Haji Seyed Asadollah SB, Jodar-Abellan A, Pardo MA. Optimizing machine learning for agricultural productivity: a novel approach with RScv and remote sensing data over Europe. Agric Syst. 2024;218:103955. doi: 10.1016/j.agsy.2024.103955.
  6. Gradl L, Reis L, Buettner R. Industrial maturity of machine learning solutions within the food industry. IEEE Access. 2025;13:25534–25544. doi: 10.1109/ACCESS.2025.3558091.
  7. Bhargava A, Shukla A, Goswami OP, Alsharif MH, Uthansakul P, Uthansakul M, et al. Plant leaf disease detection, classification, and diagnosis using computer vision and artificial intelligence: A review. IEEE Access. 2024;12:12345–12357. doi: 10.1109/ACCESS.2024.3373001.
  8. Joseph DS, Pawar PM, Chakradeo K. Real-time plant disease dataset development and detection of plant disease using deep learning. IEEE Access. 2024;12:12345–12358. doi: 10.1109/ACCESS.2024.3358333.
  9. Haji Seyed Asadollah SB, Jodar-Abellan A, Pardo MA. Optimizing machine learning for agricultural productivity: A novel approach with RScv and remote sensing data over Europe. Agric Syst. 2024;218:103955. doi: 10.1016/j.agsy.2024.103955.
  10. Qu HR, Su WH. Deep learning-based weed–crop recognition for smart agricultural equipment: A review. Agronomy. 2024;14(2):363. doi: 10.3390/agronomy14020363.
  11. Mishra S, Mishra D, Santra GH. Applications of machine learning techniques in agricultural crop production: A review paper. Indian J Sci Technol. 2016;9(38):1–8. doi: 10.17485/ijst/2016/v9i38/95032.
  12. El-Kenawy EM, Alhussan AA, Khodadadi N, Mirjalili S, Eid MM. Predicting potato crop yield with machine learning and deep learning for sustainable agriculture. Potato Res. 2025;68:759–792. doi: 10.1007/s11540-024-09753-w.
  13. Nagesh OS, Budaraju RR, Kulkarni SS, Vinay M, Ajibade SSM, Chopra M, et al. Boosting-enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture. Discover Sustain. 2024;5:78. doi: 10.1007/s43621-024-00254-x.
  14. Harinath D, Patil A, Bandi M, Raju AVS, Ramana Murthy MV, Spandana D. Smart farming system—an efficient technique for predicting agriculture yields using machine learning. Technische Sicherheit. 2024 Dec. Available from: https://www.researchgate.net/publication/387306143
  15. Zamani LS, Anand KPR, Prabhu P, Buttar AM, Pallathadka ARH, Dugbakie BN. Performance of machine learning and image processing in plant leaf disease detection. J Food Qual. 2022;2022:1–7. doi: 10.1155/2022/1598796.
Regular Issue Subscription Review Article
Volume 15
Issue 01
Received 08/12/2025
Accepted 17/12/2025
Published 06/01/2026
Publication Time 29 Days
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