Transforming Farming With Machine Intelligence: Plant Affliction Detection Plans, Request And Their Limitations

Authors

  • Faruk Abdulla
  • Dharmendra Kumar
  • Tanya Sinha
  • Rathod Kuldeep Singh
  • Utkarsh Dashora
  • Suraj Gupta
  • Lalit Kumar
  • Jaimin Bariya

DOI:

https://doi.org/10.63682/jns.v14i26S.6667

Keywords:

N\A

Abstract

This study investigates the merger of structure brilliance (AI) with Computer network of Accouterments (IoT) sensor electronics for mechanical leaf malady diagnosis in crops. The research outlines organized steps for trade plant syndromes through AI, containing idea addition, pre-convert, break-up, feature voting, and categorization. It contends miscellaneous system understanding and deep knowledge models used for ache detection, and reviews existent studies applying these methods. The paper repeated designates a collection of plant disease finding datasets. Challenges guide ML and DL uses are deliberate, apart from future research convenience, in the way that smart drones for on-ground illness discovery and hearing. The research aims to help specialists investigate various arrangements for plant affliction finding and discover the disadvantages of current methods.

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References

Fernández, J., & Gómez, M. (2020). AI in agriculture: Technologies and their potential for disease detection. Springer.

Liakos, K. G., et al. (2018). Machine learning in agriculture: A review. Computers and Electronics in Agriculture, 151, 1-13.

Kumar, A., & Sharma, P. (2022). Applications of artificial intelligence in plant disease management. Journal of Agricultural Technology, 13(2), 55-74.

Zhang, Y., & Li, B. (2019). Precision agriculture: Using AI to revolutionize crop monitoring and disease detection. AI in Agriculture, 10, 145-158.

Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). A Review of the Use of Remote Sensing and Machine Learning in Agriculture. Computers in Industry, 100, 79- 97.

Rahman, M. M., et al. (2021). A Comprehensive Review on Image Processing Techniques for Plant Disease Detection Using Machine Learning. Computers and Electronics in Agriculture, 183, 105953.

Baluja, R., & Soni, S. (2020). Application of Artificial Intelligence and Deep Learning in Agricultural Disease Prediction. Environmental Science and Pollution Research, 27, 4142-4154.

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Published

2025-05-28

How to Cite

1.
Abdulla F, Kumar D, Sinha T, Singh RK, Dashora U, Gupta S, Kumar L, Bariya J. Transforming Farming With Machine Intelligence: Plant Affliction Detection Plans, Request And Their Limitations. J Neonatal Surg [Internet]. 2025May28 [cited 2025Oct.25];14(26S):920-3. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/6667

Issue

Vol. 14 No. 26S (2025): Journal of Neonatal Surgery

Section

Original Article

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