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Engineering Review : International journal for publishing of original researches from the aspect of structural analysis, materials and new technologies in the field of mechanical engineering, shipbuilding, fundamental engineering sciences, computer sciences, electrical engin, Vol. 45 No. 1, 2025.

Original scientific paper

https://doi.org/10.30765/er.2561

Water stress diagnosis in maize crop using enhanced extreme learning machine model for precision irrigation systems

Subeesh A. orcid id orcid.org/0000-0001-6128-3292 ; Agricultural Mechanization Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India *
Naveen Chauhan ; Department of Computer Science and Engineering, National Institute of Technology, Hamirpur
Narendra Chandel ; Agricultural Mechanization Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India
Yogesh Rajwade ; Irrigation and Drainage Engineering Division, ICAR-Central Institute of Agricultural Engineering, Bhopal, India

* Corresponding author.

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Abstract

Water stress is a significant component that limits crop productivity globally, particularly affecting maize yields. The adverse effects of water stress on maize necessitate an efficient method for rapid and accurate monitoring. An intelligent crop water stress identification model is an important component of the development of a decision support system for smart irrigation.  The present study proposes an intelligent kernel extreme learning machine model (CS-KELM) to recognize water stress in maize crops. To optimize the model's performance, the meta-heuristic cuckoo search algorithm is integrated to fine-tune the model. The proposed approach has demonstrated an accuracy of 94.20% and an F1-score of 94.39%. Integrating the cuckoo search algorithm into the extreme leanring machine (ELM) model has enhanced the model performance, resulting in an improvement of 4.27% in accuracy and a 4.32% increase in F1 score compared to the ELM model. The improved model performance underscores its potential effectiveness in deploying the model into a decision support system for IoT-based irrigation solutions, enabling efficient and precise water delivery based on real-time stress detection

Keywords

agricultural engineering; kernel ELM; machine learning; precision agriculture; smart irrigation systems

Hrčak ID:

330871

URI

https://hrcak.srce.hr/330871

Publication date:

27.2.2025.

Visits: 305 *