Dong, R.; Shiraiwa, A.; Pawasut, A.; Sreechun, K.; Hayashi, T.
Diagnosis of Citrus Greening Using Artificial Intelligence: A Faster Region-Based Convolutional Neural Network Approach with Convolution Block Attention Module-Integrated VGGNet and ResNet Models. Plants 2024, 13, 1631.
https://doi.org/10.3390/plants13121631
AMA Style
Dong R, Shiraiwa A, Pawasut A, Sreechun K, Hayashi T.
Diagnosis of Citrus Greening Using Artificial Intelligence: A Faster Region-Based Convolutional Neural Network Approach with Convolution Block Attention Module-Integrated VGGNet and ResNet Models. Plants. 2024; 13(12):1631.
https://doi.org/10.3390/plants13121631
Chicago/Turabian Style
Dong, Ruihao, Aya Shiraiwa, Achara Pawasut, Kesaraporn Sreechun, and Takefumi Hayashi.
2024. "Diagnosis of Citrus Greening Using Artificial Intelligence: A Faster Region-Based Convolutional Neural Network Approach with Convolution Block Attention Module-Integrated VGGNet and ResNet Models" Plants 13, no. 12: 1631.
https://doi.org/10.3390/plants13121631
APA Style
Dong, R., Shiraiwa, A., Pawasut, A., Sreechun, K., & Hayashi, T.
(2024). Diagnosis of Citrus Greening Using Artificial Intelligence: A Faster Region-Based Convolutional Neural Network Approach with Convolution Block Attention Module-Integrated VGGNet and ResNet Models. Plants, 13(12), 1631.
https://doi.org/10.3390/plants13121631
