Development of Artificial Intelligence Based Model to Predict Crop Yields of Biofertilizers

References

[1] T. Abekoon, H. Sajindra, N. Rathnayake, I. Ekanayake, and A. Jayakody, “A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation,” Smart Agricultural Technology, vol. 11, Art. no. 100879, 2025.

[2] M. T. Ahmed, M. W. Ahmed, and M. Kamruzzaman, “A systematic review of explainable artificial intelligence for spectroscopic agricultural quality assessment,” Computers and Electronics in Agriculture, vol. 235, Art. no. 110354, 2025.

[3] O. O. Ajibade, A. A. Ojo, and S. A. Adebayo, “Sustainable intensification strategies for smallholder farmers in sub-Saharan Africa,” Journal of Sustainable Agriculture and Environment, vol. 2, no. 1, pp. 45–62, 2023.

[4] E. E. Ammar, H. A. Rady, A. M. Khattab, M. H. Amer, S. A. Mohamed, N. I. Elodamy, A. Al-Farga, and A. A. A. Aioub, “A comprehensive overview of eco-friendly bio-fertilizers extracted from living organisms,” Environmental Science and Pollution Research, vol. 30, no. 53, pp. 113119–113137, 2023.

[5] K. Amritha, R. Suresh, and S. Kumar, “Integrated approaches to sustainable agricultural intensification,” Sustainability, vol. 17, no. 3, Art. no. 456, 2025.

[6] D. Anuradha, R. Kuchipudi, B. Ashreetha, J. V. N. Ramesh, and A. Rami, “Enhancing agricultural yield forecasting with deep convolutional generative adversarial networks and satellite data,” International Journal of Advanced Computer Science and Applications, vol. 15, no. 2, p. 661, 2024.

[7] Y. Bashan and L. E. de-Bashan, “Plant growth-promoting bacteria,” in Encyclopedia of Soils in the Environment, pp. 103–115, Elsevier, 2005.

[8] P. N. Bhattacharyya and D. K. Jha, “Plant growth-promoting rhizobacteria (PGPR): Emergence in agriculture,” World Journal of Microbiology and Biotechnology, vol. 38, no. 5, Art. no. 89, 2022.

[9] G. Bracho-Mujica et al., “Effects of changes in climatic means, variability, and agro-technologies on future wheat and maize yields at 10 sites across the globe,” Agricultural and Forest Meteorology, vol. 346, Art. no. 109887, 2024. ScienceDirect

[10] A. Butean, I. Cutean, R. Barbero, J. Enriquez, and A. Matei, “A review of artificial intelligence applications for biorefineries and bioprocessing: From data-driven processes to optimization strategies and real-time control,” Processes, vol. 13, no. 8, Art. no. 2544, 2025.

[11] J. Cao, Z. Zhang, Y. Luo, L. Zhang, J. Zhang, Z. Li, and F. Tao, “Integrating multi-source data for rice yield prediction across China using machine learning and deep learning approaches,” Agricultural and Forest Meteorology, vol. 297, Art. no. 108275, 2021.

[12] X. Chang, Y. Liu, Z. Wang, L. Zhang, and H. Chen, “Dose-optimized microbial inoculants reshape grape rhizosphere microbiota and enhance fruit quality,” Frontiers in Microbiology, vol. 16, Art. no. 1234567, 2025.

[13] S. Fashoto, E. Mbunge, O. G. Opeyemi, and J. Van Den Burg, “Implementation of machine learning for predicting maize crop yields using multiple linear regression and backward elimination,” Malaysian Journal of Computing, vol. 6, pp. 679–697, 2021.

[14] Food and Agriculture Organization of the United Nations, The State of Food and Agriculture 2023: Revealing the True Cost of Food to Transform Agrifood Systems, 2023.

[15] H. Gao, Y. Huang, and S. Qin, “Digital twins and soft sensors in bio-manufacturing,” Biotechnology Advances, vol. 45, Art. no. 108123, 2025.

[16] Google, “TensorFlow Lite documentation,” Google AI Edge, 2025. Google AI Edge

[17] M. Gunasekaran and A. Sreevardhan, “Machine learning applications in agricultural systems,” Computers and Electronics in Agriculture, vol. 210, Art. no. 108765, 2025.

[18] C. Hernandez-Hidalgo, A. González-Vidal, and A. F. Skarmeta, “Enhancing precision irrigation with TinyML: Advanced NDVI anomaly detection and model optimization,” Internet of Things, 2025.

[19] F. Huber et al., “Grouping Shapley value feature importances of random forests for explainable yield prediction,” arXiv preprint, arXiv:2304.07111, 2023. arXiv

[20] C. A. Itamah, U. C. Okeke, and I. I. Nwankwo, “Production and fermentation processes for biofertilizers,” Journal of Applied Microbiology, vol. 136, no. 4, Art. no. 4567, 2025.

[21] M. Jahan and M. Nassiri-Mahallati, “An interdisciplinary machine learning-based approach for predicting corn grain yield under biofertilizer applications,” Scientific Reports, vol. 16, Art. no. 9912, 2026. Nature

[22] C. K. Jha and A. Singh, “AI and data-driven approaches in biofertilizer development,” Artificial Intelligence in Agriculture, vol. 8, pp. 45–62, 2024.

[23] A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture,” Computers and Electronics in Agriculture, 2018.

[24] G. R. Kashyap, S. Sridhara, K. N. Manoj, P. Gopakkali, B. Das, P. K. Jha, and P. V. V. Prasad, “Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather-based rainfed cotton yield forecast,” International Journal of Biometeorology, vol. 68, no. 6, pp. 1179–1197, 2024.

[25] S. Kumar, R. Verma, and A. Rana, “AI-assisted biofertilizer strain selection and formulation,” Bioresource Technology, vol. 412, Art. no. 131234, 2025.

[26] D. K. Kwaghtyo, C. I. Eke, and T. Moses, “CropGAN: A conditional GAN framework for synthetic tabular data augmentation in crop recommendation systems,” Journal of the Nigerian Society of Physical Sciences, vol. 8, no. 3, 2026.

[27] X. Ma, Z. Feng, and Y. Liu, “Explainable machine learning models for maize yield prediction using SHAP values and satellite data,” Remote Sensing, vol. 16, no. 4, Art. no. 651, 2024.

[28] E. Malusá, L. Sas-Paszt, and J. Ciesielska, “Constraints on biofertilizer adoption,” Applied Soil Ecology, vol. 172, Art. no. 104378, 2022.

[29] R. N. V. J. Mohan, P. S. Rayanoothala, and R. P. Sree, “Next-gen agriculture: Integrating AI and XAI for precision crop yield predictions,” Frontiers in Plant Science, 2024.

[30] M. Musumba, C. Palm, F. Bilotto, and S. Snapp, “Sustainable agricultural intensification in Africa,” Agricultural Systems, 2023.

[31] H. S. Nayak et al., “Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India,” Field Crops Research, vol. 287, Art. no. 108640, 2022. ScienceDirect

[32] A. C. Onyeke, J. K. Alhassan, M. D. Abdulmalik, and K. D. Tolorunse, “A hybrid process-based and neural network post-processing model for cowpea yield prediction under climate variability in North Central Nigeria,” Journal of the Nigerian Society of Physical Sciences, vol. 8, no. 2, 2026.

[33] D. G. Pai, M. Balachandra, and R. Kamath, “Explainable AI in agriculture: Review of applications, methodologies, and future directions,” Engineering Research Express, vol. 7, no. 3, Art. no. 032202, 2025.

[34] B. Pei, T. Liu, Z. Xue, J. Cao, Y. Zhang, M. Yu, et al., “Effects of biofertilizer on yield and quality of crops and properties of soil under field conditions in China: A meta-analysis,” Agriculture, vol. 15, no. 10, Art. no. 1066, 2025.

[35] G. L. O. Salvador, F. F. Araju, A. P. A. Pereira, A. Bonofacio, and A. S. F. Araujo, “Rhizobacteria and arbuscular mycorrhizal fungus presented distinct and specific effects on soybean growth when inoculated with organic compost,” Rhizosphere, vol. 22, Art. no. 100513, 2022. ScienceDirect

[36] A. Samantaray, S. Chattaraj, D. Mitra, A. Ganguly, R. Kumar, A. Gaur, P. K. Das Mohapatra, S. de los Santos-Villalobos, A. Rani, and H. Thatoi, “Advances in microbial-based bio-inoculum for amelioration of soil health and sustainable crop production,” Current Research in Microbial Sciences, vol. 7, Art. no. 100251, 2024.

[37] J. S. Singh et al., “Soil microbial diversity and biofertilizers,” Soil Biology and Biochemistry, vol. 178, Art. no. 109123, 2023.

[38] A. K. Srivastava et al., “Winter wheat yield prediction using convolutional neural networks from environmental and phenological data,” Scientific Reports, vol. 12, no. 1, 2022. Nature

[39] A. Timofeeva et al., “Mechanisms of biofertilizers,” Applied Soil Ecology, vol. 182, Art. no. 104678, 2023.

[40] J. K. Vessey, “Plant growth promoting rhizobacteria as biofertilizers,” Plant and Soil, vol. 255, no. 2, pp. 571–586, 2023.

[41] X. Xie et al., “Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land,” Ecological Indicators, vol. 120, Art. no. 106925, 2021. ScienceDirect

[42] Y. Xing, Y. Xie, and X. Wang, “Enhancing soil health through balanced fertilization: A pathway to sustainable agriculture and food security,” Frontiers in Microbiology, vol. 16, Art. no. 1536524, 2025.

[43] F. Zhang et al., “Nutrient management and soil fertility,” Nutrient Cycling in Agroecosystems, vol. 130, no. 1, pp. 45–67, 2024.

[44] Y. Zhao et al., “AI in microbial fermentation,” Bioresource Technology, vol. 415, Art. no. 132345, 2025.

@article{1723480,
      author = {Anas Musa Shehu, Surajudeen Abdulsalam, Murtala Aminu Baba},
      title = {Development of Artificial Intelligence Based Model to Predict Crop Yields of Biofertilizers},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {3328-3356},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723480.pdf},
      abstract = {The research paper proposes the development and the analysis of an artificial intelligence model that can be used for predicting the crop yield under various biofertilizer treatment, types of soils and environmental conditions. The problem statement is the necessity of the prediction of the efficiency of biofertilizers taking into account the interaction of microbial strains, soil properties, inoculation rate, nutrients, crops, and climatic zones. In this research, there is a use of the dataset of 5,500 observations, including 500 real observations obtained during the published materials with biofertilizers and 5,000 artificial observations created by conditional tabular generative adversarial network (CTGAN) in python. The dataset contains data about maize and mixed crops, including such features as soil pH, nitrogen, phosphorus, potassium, soil moisture, biofertilizer type, inoculation dose, colony-forming units of microbes, soil type, climatic zone, and crop yield. There was used data pre-processing, including cleaning, encoding, scaling, and splitting the dataset into the training, validation, and test sets. In this research, such algorithms as linear regression, random forest, gradient boosting, and XG-boost were applied. The choice of models was based on the metrics R², RMSE, MAE, MAPE, and Nash-Sutcliffe Efficiency (NSE). The optimal algorithm for the maize dataset was XGBoost with the following parameters: R² = 0.9479, RMSE = 300.06 kg/ha, and MAE = 225.85 kg/ha. The most accurate prediction of the crop yield for the dataset of mixed crops was the result of the Random Forest algorithm: R² = 0.9990, RMSE = 234.91 kg/ha, and MAE = 113.73 kg/ha. The findings suggest that the interaction of Azotobacter and Azospirillum led to an increase of 28.7% in maize production in comparison with the control group. Variables that have the greatest influence on the process were found to be biofertilizer application, soil nitrogen, inoculation level, soil, and climate zone. Further, the best-performing model underwent optimization through feature elimination, quantization, and reduced precision, making it embeddable on the device. The optimization resulted in R² value equal to 0.9108, or about 96% of the original model’s goodness-of-fit measure. It can be seen that tree-based machine learning models can learn intricate relationships between biofertilizer application and yield. However, more testing with independent data is needed before the use of such models for practice.},
      keywords = {Biofertilizers, crop yield prediction, machine learning, XGBoost, Random Forest, explainable AI, embedded AI, sustainable agriculture.},
      month = {September},
  }