SDPEMDL: Enhanced Sugarcane Disease Detection and Prediction via An Ensemble Multimodal Deep Learning Approach Using Advanced Deep Learning Models
DOI:
https://doi.org/10.48047/wd8k7012Keywords:
SugarcaneDisease, Classification, Segmentation, DeepLearning, Optimizations.Abstract
The emergence of diverse and complicated diseases in sugarcane has necessitated the development of advanced, multifaceted models capable of detecting and predicting diseases with heightened accuracy and precision. Existing models, although proficient, exhibit limitations in handling multi source data, offering restricted accuracy, and providing futuristic insights into disease occurrence. In this study, we integrate multimodal datasets— consisting of RGB images refined using VGGNet 19, Near-Infrared (NIR), Hyperspectral, and Thermal images processed with Inception Net to construct an innovative model that surmounts existing limitations.
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References
S. I. Moazzam et al., "A Patch-Image Based Classification Approach for Detection of Weeds in Sugar Beet Crop," in IEEE
Access, vol. 9, pp. 121698-121715, 2021, doi: 10.1109/ACCESS.2021.3109015.
V. Tanwar, S. Lamba, B. Sharma and A. Sharma, "Deep Learning-based Approach for Leaf Disease of Sugarcane Classification,"
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