Multi-Spectral Image Fusion with Efficient Net and Novel Feature Extraction for Enhanced Disease Detection in Sal and Tendu Leaves

Authors

  • Nivedita S,Dr.Saurabh Mitra,

Keywords:

Multi-Spectral Imaging, Efficient Net, Local Binary Patterns, Zernike Moments, Plant Disease Detection

Abstract

The detection of diseases in the leaves of Sal and Tendu is very crucial as these plantscarry economic and ecological importance. Traditional methods of plant disease detectionessentially rely on RGB images that are capable of capturing only visible symptoms, not the earlysymptoms or hidden stress indicators. 

References

P. Garg et al., "Environmental and Soil Parameters for Germination of Leaf Spot Disease in the Groundnut Plant Using IoT-Enabled Sensor System," in IEEE Sensors Letters, vol. 7, no. 12, pp. 1-4, Dec. 2023, Art no. 4504104, doi: 10.1109/LSENS.2023.3330923.

C. Zhou, S. Zhou, J. Xing and J. Song, "Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network," in IEEE Access, vol. 9, pp. 28822-28831, 2021, doi: 10.1109/ACCESS.2021.3058947.

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Published

2024-07-15

How to Cite

Nivedita S,Dr.Saurabh Mitra,. (2024). Multi-Spectral Image Fusion with Efficient Net and Novel Feature Extraction for Enhanced Disease Detection in Sal and Tendu Leaves . Journal of Computational Analysis and Applications (JoCAAA), 33(07), 2202–2225. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3109

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Articles