Artificial Intelligence and Machine Learning based Nickel Nutrient Content Analyzer in Soil: An Overview

Abstract

Nickel is a vital element required for the growth and development of plants; yet, its excessive accumulation may lead to toxicity. The precise quantification of nickel nutrient levels in soil is of paramount significance in order to ascertain the optimal nickel concentration required for plants to flourish. The conventional techniques used for assessing the nickel nutrient concentration in soil are characterised by a significant investment of time and labour. Additionally, the use of specialised equipment is necessary, and this might incur significant costs. The use of artificial intelligence (AI) and machine learning (ML) presents a potentially advantageous methodology for the assessment of nickel nutrient levels in soil. Artificial intelligence (AI) and machine learning (ML) algorithms have the capability to undergo training processes in order to discern patterns within soil data that might serve as indicators of nickel concentration. This enables the advancement of expeditious, precise, and economical techniques for assessing the concentration of nickel nutrients in soil. This study provides an overview of the present advancements in artificial intelligence (AI) and machine learning (ML) techniques used in nickel nutrient content analyzers for soil analysis. The article examines the benefits of using artificial intelligence (AI) and machine learning (ML) in this context, while also delineating the remaining obstacles that need attention. The concepts of scalability and efficiency are crucial in several domains and industries. The distributed nature of Federated Learning enables the training of machine learning models in large-scale industrial processes, thereby mitigating problems related to scalability. Federated Learning enhances computational performance and enables manufacturers to use collective wisdom while minimising resource usage by circumventing centralised data storage. The abstract highlights the significance of Federated Learning in facilitating data-driven decision-making within the industrial sector. Through the use of knowledge derived from dispersed data sources, manufacturers have the ability to make choices that are more informed and more adaptable, resulting in enhanced productivity and a decrease in periods of operational inactivity.

Cite as

Sagar K. G. (2023). Artificial Intelligence and Machine Learning based Nickel Nutrient Content Analyzer in Soil: An Overview. Journal of Advanced Research in Industrial Engineering, 5(2), 26–33. https://doi.org/10.5281/zenodo.8304902