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Peer-reviewedCC BYGeocarto International

AgroAdvisor: crop yield prediction, crop and fertilizer recommendation system using random forest with gradient boosting and DeepFM for precise agriculture

  • Ashima Kukkar1,2
  • Gagandeep Kaur1,3
  • Rajni Mohana4,3
  • Anand Muni Mishra5,6
  • Santar Pal Singh7,8
  • Shashi Prakash Dwivedi9
  • Shubham Sharma9,10
  • Rajeev Agrawal11
  • Mirjalol Ismoilov Ruziboy Ugli12
  • Medhat M. Helal13
  • Krishna Prakash Arunachalam14
  • Ishwar Bhiradi15
15 affiliations
  1. 1Department of CSE, Chitkara University Institute of Engineering and Technology, Chitkara University
  2. 2Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India;
  3. 3CS/IT Department, Jaypee University of Information and Technology Technology, Solan, India;
  4. 4Department of CS/IT, Jaypee University of Information and Technology
  5. 5Department of Information Technology, GL Bajaj Institute of Technology and Management
  6. 6Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA 02115, USA;
  7. 7Department of CSE, Rashtrakavi Ramdhari Singh Dinkar College of Engineering
  8. 8Department of Economics, Kardan University, Parwane Du, 1001, Kabul, Afghanistan;
  9. 9Department of Mechanical Engineering, Lloyd Institute of Engineering & Technology, Knowledge Park II
  10. 10Department of Technical Sciences, Western Caspian University
  11. 11Delhi Technical Campus, Knowledge Park III
  12. 12Department of Transport Systems, Urgench State University named after Abu Rayhan Biruni
  13. 13Civil Engineering Department, College of Engineering and Architecture, Umm Al-Qura University
  14. 14Departamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica Metropolitana
  15. 15Manipal Institute of Technology, Manipal Academy of Higher Education
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Abstract

Crop yield prediction plays a very important role in productivity growth. Prediction of the crop yield in particular area helps the farmer to choose the right crop to be grown in the land. With crop yield prediction crop recommendation boosts up the productivity of crop. Recommending the correct type of crop in particular land on the factors of soil pH, rainfall, temperature, humidity etc. helps the farmer to choose specific and most suitable crop. With recommendation and yield prediction of crop, fertilizer recommendation is also necessary for more productivity and yield. It is necessary to use suitable fertilizers on optimal timing for the growth of crops. Therefore, in this paper, we have attempted to address these issues by proposing three model systems that will efficiently manage crop production. In this paper, we designed an integrated system named as AgroAdvisor using the hybrid proposed technique such as Random Forest with Extreme Gradient Boosting (RFXGB) and Deep Factorization Machine (DeepFM). RFGB is applied for processing the features, which improves the DeepFM ability to handle the dense numerical features and increase the prediction performance. The result of RFXGB-DeepFM is compared with classical machine learning and deep learning techniques by using recall, F-value, precision and accuracy parameters. The results show that the proposed RFGB-DeepFM technique gives better accuracy than the classical techniques. The impact of RFGXB on existing techniques is also analyzed using Friedman and post hoc statistical testing and results show that in most cases RFGXB enhanced the performance.

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Cite

BibTeXRIS
BibTeX
@article{kukkar2026agroadvisor,
  title = {{AgroAdvisor: crop yield prediction, crop and fertilizer recommendation system using random forest with gradient boosting and DeepFM for precise agriculture}},
  author = {Ashima Kukkar and Gagandeep Kaur and Rajni Mohana and Anand Muni Mishra and Santar Pal Singh and Shashi Prakash Dwivedi and Shubham Sharma and Rajeev Agrawal and Mirjalol Ismoilov Ruziboy Ugli and Medhat M. Helal and Krishna Prakash Arunachalam and Ishwar Bhiradi},
  journal = {Geocarto International},
  year = {2026},
  month = jul,
  doi = {10.1080/10106049.2026.2709721},
  url = {https://doi.org/10.1080/10106049.2026.2709721},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - AgroAdvisor: crop yield prediction, crop and fertilizer recommendation system using random forest with gradient boosting and DeepFM for precise agriculture
AU  - Ashima Kukkar
AU  - Gagandeep Kaur
AU  - Rajni Mohana
AU  - Anand Muni Mishra
AU  - Santar Pal Singh
AU  - Shashi Prakash Dwivedi
AU  - Shubham Sharma
AU  - Rajeev Agrawal
AU  - Mirjalol Ismoilov Ruziboy Ugli
AU  - Medhat M. Helal
AU  - Krishna Prakash Arunachalam
AU  - Ishwar Bhiradi
PY  - 2026
DA  - 2026/07/31
JO  - Geocarto International
DO  - 10.1080/10106049.2026.2709721
UR  - https://doi.org/10.1080/10106049.2026.2709721
AB  - Crop yield prediction plays a very important role in productivity growth. Prediction of the crop yield in particular area helps the farmer to choose the right crop to be grown in the land. With crop yield prediction crop recommendation boosts up the productivity of crop. Recommending the correct type of crop in particular land on the factors of soil pH, rainfall, temperature, humidity etc. helps the farmer to choose specific and most suitable crop. With recommendation and yield prediction of crop, fertilizer recommendation is also necessary for more productivity and yield. It is necessary to use suitable fertilizers on optimal timing for the growth of crops. Therefore, in this paper, we have attempted to address these issues by proposing three model systems that will efficiently manage crop production. In this paper, we designed an integrated system named as AgroAdvisor using the hybrid proposed technique such as Random Forest with Extreme Gradient Boosting (RFXGB) and Deep Factorization Machine (DeepFM). RFGB is applied for processing the features, which improves the DeepFM ability to handle the dense numerical features and increase the prediction performance. The result of RFXGB-DeepFM is compared with classical machine learning and deep learning techniques by using recall, F-value, precision and accuracy parameters. The results show that the proposed RFGB-DeepFM technique gives better accuracy than the classical techniques. The impact of RFGXB on existing techniques is also analyzed using Friedman and post hoc statistical testing and results show that in most cases RFGXB enhanced the performance.
SN  - 1010-6049
SN  - 1752-0762
ER  -