Hybrid AI for Crop Health: Jute Leaf Disease Classification Using ResNet 50 and KNN

Authors

  • B. Poojitha, Ch. Vinod, Md. Haseeb Ahamed Siddiqui, A. Vamsi Chaithanya, E. Vishnu Sai

Keywords:

Jute Leaf Disease, Image Classification, Feature Extraction, K-Nearest Neighbors (KNN), ResNet50.

Abstract

Jute is one of the most important cash crops in India, playing a vital role in the livelihood of millionsof farmers and contributing significantly to the national economy. Ensuring the health and productivityof jute crops is essential for sustainable agriculture 

References

Zeraatgari, Fatemeh Zahra, Fatemeh Hafezianzadeh, Yanxia Zhang, Liquan Mei, Ashraf

Ayubinia, Amin Mosallanezhad, and Jingyi Zhang. "Machine learning-based photometric

classification of galaxies, quasars, emission-line galaxies, and stars." Monthly Notices of the

Royal Astronomical Society 527, no. 3 (2024): 4677-4689.

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Published

2025-04-15

How to Cite

B. Poojitha, Ch. Vinod, Md. Haseeb Ahamed Siddiqui, A. Vamsi Chaithanya, E. Vishnu Sai. (2025). Hybrid AI for Crop Health: Jute Leaf Disease Classification Using ResNet 50 and KNN . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 1328–1335. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3131

Issue

Vol. 34 No. 4 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.