Almond Tree Variety Identification Based on Bark Photographs Using Deep Learning Approach and Wavelet Transform

Benassi, Amin; Kardous, Faten; Grayaa, Khaled

doi:10.1007/s13369-024-08743-x

Arab J Sci Eng

2024

DOI: 10.1007/s13369-024-08743-x

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Almond Tree Variety Identification Based on Bark Photographs Using Deep Learning Approach and Wavelet Transform

Amin Benassi,

Faten Kardous,

Khaled Grayaa

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Cited by 3 publications

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ASFESRN: bridging the gap in real-time corn leaf disease detection with image super-resolution

Yeswanth,

Deivalakshmi

2024

Multimedia Systems

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ASFESRN: bridging the gap in real-time corn leaf disease detection with image super-resolution

Yeswanth,

Deivalakshmi

2024

Multimedia Systems

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A forestry investigation: Exploring factors behind improved tree species classification using bark images

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Ecological Informatics

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Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree

Wang,

Li,

Deng

et al. 2024

Agriculture

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Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens.

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Almond Tree Variety Identification Based on Bark Photographs Using Deep Learning Approach and Wavelet Transform

Cited by 3 publications

References 20 publications

ASFESRN: bridging the gap in real-time corn leaf disease detection with image super-resolution

ASFESRN: bridging the gap in real-time corn leaf disease detection with image super-resolution

A forestry investigation: Exploring factors behind improved tree species classification using bark images

Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree

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