STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews : Journal of Crop science and Technology Review Article

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

  1. Mr. Raghunath Maji Department of Computer Science and Engineering Greater Kolkata College of Engineering and Management Baruipur
  2. Mr. Swarup Ghosh Department of Computer Science and Engineering Greater Kolkata College of Engineering and Management Baruipur
  3. Mr. Dipankar Barui Department of AI & ML, St. Thomas' College of Engineering and Technology Diamond Harbour Rd, Kidderpore
  4. Mr. Sourav Chowdhury Department of Computer Science and Engineering Greater Kolkata College of Engineering and Management Baruipur
  5. Ms. Shreya Patra Department of Computer Science and Engineering Dr. Sudhir Chandra Sur Institute of Technology and Sports Complex Dum Dum
  6. Dr. Biswajit Gayen at Department of Basic Science Greater Kolkata College of Engineering and Management Baruipur

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

References (15)

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