- author = "Janmejay Pant and Hitesh Kumar Pant and Priyanshi Kotlia and Devendra Singh and Praveen Kumar Joshi and Himanshu Pant",
- title = "Automated Machine Learning with {TPOT} for Predictive Soil Fertility Modeling",
- booktitle = "2026 6th International Conference on Pervasive Computing and Social Networking (ICPCSN)",
- year = "2026",
- pages = "1278--1283",
- address = "Salem, India",
- month = "6-8 " # may,
- publisher = "IEEE",
- keywords = "genetic algorithms, genetic programming, Modeling, Soil, Machine learning, Machining, Automated machine learning, Pipelines, Optimization, Printing, Tools, Algorithms, Machine Learning, Auto ML, TPOT, DEAP, Soil Fertility",
- isbn13 = "979-8-3315-7237-2",
-
DOI = "
10.1109/ICPCSN68523.2026.11543590",
- size = "6 pages",
- abstract = "An accurate assessment of soil fertility is crucial for enhancing crop production and for sustainable use of agricultural land, but conventional soil fertility evaluation methods can be time-consuming and labour-intensive. This research examines the use of Automated Machine Learning (AutoML), in particular the Tree-based Pipeline Optimization Tool (TPOT), to create a fast and accurate prediction model for soil fertility assessment. The study used a dataset soil samples from a soil testing laboratory and Soil Health Cards. The properties include important soil physicochemical, macronutrients, micronutrients and electrical conductivity. TPOT was used to automatically design machine learning pipelines with feature selection and hyperparameter tuning using genetic programming. The model was highly accurate (97.63 percent cross-validation accuracy) in predicting soil fertility. The research showcases the benefits of AutoML in terms of efficiency and performance. The results indicate that TPOT methods can assist in data-driven agriculture, and offer a scalable approach for soil fertility evaluation.",
-
notes = "also known as \cite{11543590}
CFP26EZ5-ART
School of Computing, Graphic Era Hill University, India",
