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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
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Published Paper Details:
Paper Title
A 3D-CNN Approach for Temporal and Spatial Analysis in Banana Disease Recognition
Authors
Surendra Ramteke, Nilima Ramteke
Keywords
3D-CNN, banana disease detection, deep learning, precision agriculture
Abstract
Banana crops are highly vulnerable to diseases like Black Sigatoka and Fusarium Wilt, which can significantly impact yield. Early detection and accurate monitoring are essential but challenging with traditional manual methods. This paper presents a 3D Convolutional Neural Network (3D-CNN) approach for automated detection and severity estimation of banana diseases. Unlike 2D CNNs, 3D-CNNs capture both spatial and temporal features from sequences of RGB and multispectral images, enabling the model to analyze disease progression. Our experiments show that the 3D-CNN model achieves superior accuracy and precision in detecting diseases and estimating severity. By predicting disease progression, this model offers a valuable tool for early intervention and disease management in banana plantations. This research highlights the potential of 3D-CNNs in precision agriculture, improving disease detection and crop management.
IJCRT's Publication Details
Unique Identification Number - IJCRT2402836
Paper ID - 269471
Page Number(s) - h120-h127
Pubished in - Volume 12 | Issue 2 | February 2024
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Surendra Ramteke, Nilima Ramteke, "A 3D-CNN Approach for Temporal and Spatial Analysis in Banana Disease Recognition", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 2, pp.h120-h127, February 2024, Available at :http://www.ijcrt.org/papers/IJCRT2402836.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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