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Agricultural land suitability classification and crop suggestion using machine learning and spatial multicriteria decision analysis in semi-arid ecosystem

Author

Listed:
  • Neelam Agrawal

    (National Institute of Technology Raipur)

  • Himanshu Govil

    (National Institute of Technology Raipur)

  • Tarun Kumar

    (Dr. Rajendra Prasad Central Agricultural University)

Abstract

Agricultural land evaluation for crop cultivation plays a vital role in developing a sustainable and productive agricultural system. The traditional way of manual land evaluation is time-consuming, costlier, requires expert knowledge, and prone to human errors. The application of machine learning is making its impact on almost every field. To overcome the challenges of manual land evaluation automated approach of machine learning can play an influential role in agriculture. Therefore, the present study aimed to investigate and propose an automated solution for agricultural land suitability evaluation for two major crops, i.e., wheat and mustered, based on a hybrid approach of multicriteria decision analysis (MCDA) and machine learning (ML). The proposed framework is based on the land suitability assessment framework proposed by the Food and Agriculture Organization (FAO). For evaluating suitability levels of land units, three major influential categories of geospatial parameters are considered, i.e., soil, climatic, and topographic. The MCDA technique of analytical hierarchy process (AHP) has been used to analyze crop suitability utilizing these geospatial parameters. The ML models are constructed to map the quantified crop suitability results and suggest the most suitable crop to be cultivated. The obtained results indicate that the Balanced Bagging classifier achieved the highest balanced accuracies of 97.69% and 97.57% for mustered and wheat crop suitability predictions, respectively, and a balanced accuracy of 97.36% for crop classification. Since the AHP method is a systematic approach to decision analysis, while ML is a data-driven approach, the proposed unified approach offers more comprehensive predictions. The potential of the proposed framework is evaluated for the Abhanpur district of Chhattisgarh state, India. The proposed ML-based approach overcomes the limitations of the conventional methods and provides precise decisions; therefore, could turn the attention to ML in future cropland suitability evaluation studies.

Suggested Citation

  • Neelam Agrawal & Himanshu Govil & Tarun Kumar, 2025. "Agricultural land suitability classification and crop suggestion using machine learning and spatial multicriteria decision analysis in semi-arid ecosystem," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(6), pages 13689-13726, June.
    • Handle: RePEc:spr:endesu:v:27:y:2025:i:6:d:10.1007_s10668-023-04440-1
    DOI: 10.1007/s10668-023-04440-1
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    References listed on IDEAS

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    1. A. Suruliandi & G. Mariammal & S.P. Raja, 2021. "Crop prediction based on soil and environmental characteristics using feature selection techniques," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 27(1), pages 117-140, January.
    2. Yaren Aydın & Ümit Işıkdağ & Gebrail Bekdaş & Sinan Melih Nigdeli & Zong Woo Geem, 2023. "Use of Machine Learning Techniques in Soil Classification," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    3. José Escorcia-Gutierrez & Margarita Gamarra & Roosvel Soto-Diaz & Meglys Pérez & Natasha Madera & Romany F. Mansour, 2022. "Intelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques," Agriculture, MDPI, vol. 12(7), pages 1-16, July.
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