MECHANICAL & APPLIED SCIENCE DOMAIN
Published August 31, 2023 | Version v1
Journal article Open

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

Authors/Creators

  • 1. Department of Mechanical Engineering, Cambridge Institute of Technology, Bengaluru, Karnataka, India

Description

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.

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References

  • Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-90.
  • Zhang, S., Guo, Y., & He, X. (2020). Application of convolutional neural network in soil nutrient prediction. Computers and Electronics in Agriculture, 177, 105761.
  • Liu, Y., & Gitelson, A. A. (2018). A review of reflectance measurements from field crops for agronomic and remote sensing applications. Remote Sensing of Environment, 224, 347-362.
  • Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
  • Joachims, T. (1998). Making large-scale SVM learning practical. Advances in kernel methods-support vector learning, 41(11), 169-184.
  • Rakwatin, P., & Tongrod, P. (2021). Application of artificial neural networks in estimating soil nutrient contents: A case study in Northeast Thailand. Journal of Soil Science and Plant Nutrition, 21(1), 24-35.
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, 1097-1105.
  • Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning, New York: springer, 4(4), 738.
  • Poggio, T., & Girosi, F. (1990). Networks for approximation and learning. Proceedings of the IEEE, 78(9), 1481-1497.
  • Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics Surveys, 4, 40-79.