An Automatic Crop Recommendation System is a system that makes use of data analysis and algorithms to recommend crops that are suitable and proper with regard to soil quality, climate, and local factors. Such a system eases the decision-making process for farmers. The necessity for efficient agricultural techniques is growing rapidly, and it is impossible without the application of modern technology that would promote the quality of the ideal crop selection list and production. This paper introduces a new concept of the Automatic Crop Recommendation System, integrating the LightGBM and the Decision Tree algorithms. The research uses the strengths of LightGBM, a type of gradient boosting framework, and Decision Tree, a conventional machine learning model, to form a powerful mixed ensemble approach. These approaches are combined to exploit their complementary strengths, leading to a more accurate and dependable agricultural advisory system. The effectiveness of the proposed algorithm’s approach is verified through several experimental results; it has the following accuracies, recalls, and F-1 scores. The process has proven very successful; an accuracy of 98.64% makes it possible to recommend appropriate and accurate crops.
A. Suruliandi, G. Mariammal, and S. P. Raja, “Crop prediction based on soil and environmental characteristics using feature selection techniques,” Mathematical and Computer Modelling of Dynamical Systems, vol. 27, no. 1, pp. 117–140, Jan. 2021, doi: 10.1080/13873954.2021.1882505.
H. Fraga, J. G. Pinto, F. Viola, and J. A. Santos, “Climate change projections for olive yields in the Mediterranean Basin,” International Journal of Climatology, vol. 40, no. 2, pp. 769–781, Jul. 2019, doi: 10.1002/joc.6237.
V. Balaska, Z. Adamidou, Z. Vryzas, and A. Gasteratos, “Sustainable Crop Protection via Robotics and Artificial Intelligence Solutions,” Machines, vol. 11, no. 8, p. 774, Jul. 2023, doi: 10.3390/machines11080774.
H. Ait Issad, R. Aoudjit, and J. J. P. C. Rodrigues, “A comprehensive review of Data Mining techniques in smart agriculture,” Engineering in Agriculture, Environment and Food, vol. 12, no. 4, pp. 511–525, Oct. 2019, doi: 10.1016/j.eaef.2019.11.003.
A. Weselek, A. Bauerle, J. Hartung, S. Zikeli, I. Lewandowski, and P. Högy, “Agrivoltaic system impacts on microclimate and yield of different crops within an organic crop rotation in a temperate climate,” Agronomy for Sustainable Development, vol. 41, no. 5, Aug. 2021, doi: 10.1007/s13593-021-00714-y.
S. Rani, A. K. Mishra, A. Kataria, S. Mallik, and H. Qin, “Machine learning-based optimal crop selection system in smart agriculture,” Scientific Reports, vol. 13, no. 1, Sep. 2023, doi: 10.1038/s41598-023-42356-y.
M. K. Senapaty, A. Ray, and N. Padhy, “IoT-Enabled Soil Nutrient Analysis and Crop Recommendation Model for Precision Agriculture,” Computers, vol. 12, no. 3, p. 61, Mar. 2023, doi: 10.3390/computers12030061.
N. Farmonov et al., “Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 1576–1588, 2023, doi: 10.1109/jstars.2023.3239756.
Sawant, Ankit R., Yash Sivramkrishnan, Hemish Ganatra, and Ameya A. Kadam. "Smart Crop–Precision Agriculture using Machine Learning." International Journal of Research in Engineering, Science and Management 6, no. 10 (2023): 13-17.
T. Ayoub Shaikh, T. Rasool, and F. Rasheed Lone, “Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming,” Computers and Electronics in Agriculture, vol. 198, p. 107119, Jul. 2022, doi: 10.1016/j.compag.2022.107119.
E. Ben Abdallah, R. Grati, and K. Boukadi, “A machine learning-based approach for smart agriculture via stacking-based ensemble learning and feature selection methods,” 2022 18th International Conference on Intelligent Environments (IE), pp. 1–8, Jun. 2022, doi: 10.1109/ie54923.2022.9826767.
K. Bakthavatchalam et al., “IoT Framework for Measurement and Precision Agriculture: Predicting the Crop Using Machine Learning Algorithms,” Technologies, vol. 10, no. 1, p. 13, Jan. 2022, doi: 10.3390/technologies10010013.
A. Gupta and P. Nahar, “Classification and yield prediction in smart agriculture system using IoT,” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 8, pp. 10235–10244, Jan. 2022, doi: 10.1007/s12652-021-03685-w.
M. Rakhra, S. Sanober, N. N. Quadri, N. Verma, S. Ray, and E. Asenso, “Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment,” Journal of Food Quality, vol. 2022, pp. 1–17, Feb. 2022, doi: 10.1155/2022/4721547.
P. A, S. Chakraborty, A. Kumar, and O. R. Pooniwala, “Intelligent Crop Recommendation System using Machine Learning,” 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 843–848, Apr. 2021, doi: 10.1109/iccmc51019.2021.9418375.
S. M. PANDE, P. K. RAMESH, A. ANMOL, B. R. AISHWARYA, K. ROHILLA, and K. SHAURYA, “Crop Recommender System Using Machine Learning Approach,” 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1066–1071, Apr. 2021, doi: 10.1109/iccmc51019.2021.9418351.
S. Yang, L. Gu, X. Li, F. Gao, and T. Jiang, “Fully Automated Classification Method for Crops Based on Spatiotemporal Deep-Learning Fusion Technology,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–16, 2022, doi: 10.1109/tgrs.2021.3113014.
S. Chakraborty and S. Mishra, “A Smart Farming-Based Recommendation System Using Collaborative Machine Learning and Image Processing,” Cognitive Informatics and Soft Computing, pp. 703–716, 2022, doi: 10.1007/978-981-16-8763-1_58.
Banerjee, Saikat, and Abhoy Chand Mondal. "A Region-Wise Weather Data-Based Crop Recommendation System Using Different Machine Learning Algorithms." International Journal of Intelligent Systems and Applications in Engineering 11, no. 3 (2023): 283-297.
R. Chaudhry, V. Rishiwal, P. Yadav, K. R. Singh, and M. Yadav, “Automatic Smart Irrigation Method for Agriculture Data,” Towards the Integration of IoT, Cloud and Big Data, pp. 57–73, 2023, doi: 10.1007/978-981-99-6034-7_4.
Acknowledgements
Author(s) thanks to Dr. Ramana Murthy B V for this research completion and support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Ravi Kumar Banoth
Department of Computer Science and Engineering, Osmania University College of Engineering, Osmania University, Hyderabad, Telangana, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Ravi Kumar Banoth and Ramana Murthy B V, “Automatic Crop Recommendation System Using LightGBM and Decision Tree Machine Learning Models”, Journal of Machine and Computing. doi: 10.53759/7669/jmc202505026.
