Abstract
Given that it accounts for 17% of the nation’s GDP and employs more than 60% of the workforce, agriculture is one of India’s largest and most diverse economic sectors. Numerous biotic along with abiotic parameters are used to make crop suggestions to boost agricultural productivity. It helps keep food prices down and favors farmers and the entire nation. Indian farmers frequently struggle with the issue of improper crop choice concerning the needs of their land. As a result, their output has been severely hindered. So, the crop recommendation model in this chapter thus uses research-based data on soil characteristics and soil classifications for farms to select the appropriate according to site-specific crop factors. By analyzing the datasets and applying machine-learning classifiers, including Decision Tree, Logistic Regression, and Random Forest, this model calculates the optimal crop for each soil type. As a result, choosing the right crop is easier, increasing productivity.
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References
S.M. Pande, P.K. Ramesh, A. Anmol, B.R. Aishwarya, K. Rohilla, K. Shaurya, Crop recommender system using machine learning approach, in 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, vol. 2021, pp. 1066–1071. https://doi.org/10.1109/ICCMC51019.2021.9418351
T. Ruchirawya, Crop Recommendation System, (2020)
S. Pudumalar, E. Ramanujam, R.H. Rajashree, C. Kavya, T. Kiruthika, J. Nisha, Crop recommendation system for precision agriculture, in 2016 Eighth International Conference on Advanced Computing (ICoAC), Chennai, India, (2017), pp. 32–36. https://doi.org/10.1109/ICoAC.2017.7951740
F.-H. Tseng, H.-H. Cho, H.-T. Wu, Applying big data for intelligent agriculture-based crop selection analysis. IEEE Access 7, 116965–116974 (2019). https://doi.org/10.1109/ACCESS.2019.2935564
S. Condran, M. Bewong, M.Z. Islam, L. Maphosa, L. Zheng, Machine learning in precision agriculture: a survey on trends, applications and evaluations over two decades. IEEE Access 10, 73786–73803 (2022). https://doi.org/10.1109/ACCESS.2022.3188649
J. Liu et al., Crop yield estimation in the Canadian prairies using Terra/MODIS-derived crop metrics. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 13, 2685–2697 (2020). https://doi.org/10.1109/JSTARS.2020.2984158
D. Sykas, M. Sdraka, D. Zografakis, I. Papoutsis, A sentinel-2 multiyear, multicountry benchmark dataset for crop classification and segmentation with deep learning. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 15, 3323–3339 (2022). https://doi.org/10.1109/JSTARS.2022.3164771
A. Kumar, S. Sarkar, C. Pradhan, Recommendation system for crop identification and pest control technique in agriculture, in 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, vol. 2019, pp. 0185–0189. https://doi.org/10.1109/ICCSP.2019.8698099
S. Bangaru Kamatchi, R. Parvathi, Improvement of crop production using recommender system by weather forecasts. Proc. Comput. Sci. 165, 724–732., ISSN:1877-0509 (2019). https://doi.org/10.1016/j.procs.2020.01.023
Z. Doshi, S. Nadkarni, R. Agrawal, N. Shah, Agroconsultant: intelligent crop recommendation system using machine learning algorithms, in 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, vol. 2018, pp. 1–6. https://doi.org/10.1109/ICCUBEA.2018.8697349
S. Vaishnavi, M. Shobana, R. Sabitha, S. Karthik, Agricultural crop recommendations based on productivity and season, in 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, (2021), pp. 883–886. https://doi.org/10.1109/ICACCS51430.2021.9441736
S.K.S. Raja, R. Rishi, E. Sundaresan, V. Srijit, Demand based crop recommender system for farmers, in 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), Chennai, India, vol. 2017, pp. 194–199. https://doi.org/10.1109/TIAR.2017.8273714
Data source. https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset
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Gayatri, G., Praharsha, K.N.V., Hemanth, K., Owk, M. (2024). Crop Recommendation System Using Machine Learning. In: Lin, F.M., Patel, A., Kesswani, N., Sambana, B. (eds) Accelerating Discoveries in Data Science and Artificial Intelligence I. ICDSAI 2023. Springer Proceedings in Mathematics & Statistics, vol 421. Springer, Cham. https://doi.org/10.1007/978-3-031-51167-7_70
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