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Crop protection and disease detection using artificial intelligence and computer vision: a comprehensive review

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Abstract

The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previous versions by implementing a new model or using better hardware devices. This has been helpful for the farmers in increasing crop productivity, and the life expectancy of crops has also increased as the diseases inside or outside the crops can be detected much earlier, and learning at an early stage helps prevent other crops. In this paper, we have presented a study where many varieties of fruits and vegetables have been taken to determine which method was used for a particular crop. By analyzing the various works carried out by the authors, it was inferred that most of the works revolved around image processing and hyperspectral imaging. Due to this, we had also included most of the papers, particularly as the models and hardware components used were much better than other works. Then, a comparative study was done where different fruits and vegetables highlighted the two main areas: the method used and the accuracy obtained.

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Acknowledgements

The authors are grateful to Walmart, Atmiya Vidya Mandir, Department of Chemical Engineering, School of Energy Technology, Pandit Deendayal Energy University, Span Inspection Systems Pvt. Ltd, Northeastern University, Hofstra University, and S S Agrawal Institute of Engineering and Technology for the permission to publish this research.

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Authors and Affiliations

  1. Software Engineer, Walmart, Sunnyvale, CA, USA

    Kanish Shah

  2. Atmiya Vidya Mandir, Surat, Gujarat, India

    Rajat Sushra

  3. Department of Chemical Engineering, School of Energy Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

    Manan Shah

  4. Robotics, ECE Concentration, Northeastern University, Boston, MA, USA

    Dhairya Shah & Haard Shah

  5. Department of Computer Science, Concentration in AI, Hofstra University, Hempstead, NY, USA

    Megh Raval

  6. Department of Chemical Engineering, S. S Agrawal Institute of Engineering & Technology, Navsari, Gujarat, India

    Mitul Prajapati

  7. R&D Engineer, Span Inspection Systems Pvt. Ltd, Gandhinagar, Gujarat, India

    Dhairya Shah

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All the authors make substantial contributions to this manuscript. KS, RS, MS, DS, HS, MR and MP participated in drafting the manuscript. KS, MS, and MP wrote the main manuscript, and all the authors discussed the results and implications of the manuscript at all stages.

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Shah, K., Sushra, R., Shah, M. et al. Crop protection and disease detection using artificial intelligence and computer vision: a comprehensive review. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19205-9

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