Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{169603,
        author = {Mrs. Bhagyashri Kadam and Ms. Sakshi Bhosale and Mr. Pratik Lavate and Mr. Harshad Shelar and Mr. Prasad Zadokar},
        title = {Survey Paper on Smart Agriculture Assistant},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1289-1292},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169603},
        abstract = {A quickly emerging field, precision agriculture aims to address contemporary issues of agricultural sustainability. Precision agriculture relies on machine learning, a state-of-the-art technology that makes it possible to create sophisticated disease detection and categorization techniques. A review of the use of deep learning and machine learning methods in precision agriculture—more especially, their detection and classification of plant diseases—is presented in this study. All pertinent works are categorized in the related classes using our innovative classification scheme. Depending on whether the research employ classification or object detection as their methodology, we divide them into two major groups.},
        keywords = {Precision agriculture, disease detection and categorization techniques.},
        month = {November},
        }