This paper examines the combined effects of density stratifications in porous media due to both g... more This paper examines the combined effects of density stratifications in porous media due to both gravitactic microorganism motion and thermal or solutal solicitation. These effects are coined in the literature by the term "double diffusion." In this case, an equation of conservation of ...
International Journal of Heat and Mass Transfer, Apr 1, 2008
This paper examines the onset of negative gravitactic bioconvection in a porous square cavity. Th... more This paper examines the onset of negative gravitactic bioconvection in a porous square cavity. The porous medium is assumed to be anisotropic in permeability with its principal axes arbitrarily oriented with respect to the gravity vector, and contains a concentration of ...
Computers and Electronics in Agriculture, Apr 1, 2021
Abstract In this study, Deep Learning (DL) was used to detect powdery mildew (PM), persistent fun... more Abstract In this study, Deep Learning (DL) was used to detect powdery mildew (PM), persistent fungal disease in strawberries to reduce the amount of unnecessary fungicide use, and the need for field scouts. This study optimised and evaluated several well-established learners, including AlexNet, SqueezeNet, GoogLeNet, ResNet-50, SqueezeNet-MOD1, and SqueezeNet-MOD2. Data augmentation was carried out from among 1450 healthy and infected leaf images to prevent overfitting and to consider the various shapes and direction of the leaves in the field. A total of eight clockwise rotations (0°; the original data, 45°, 90°, 135°, 180°, 225°, 270°, and 315°) was performed to generate 11,600 data points. Overall, the six DL algorithms that were used in this study showed on average of >92% in classification accuracy (CA). ResNet-50 gave the highest CA of 98.11% in classifying the healthy and infected leaves; however, considering the computation time, AlexNet had the fastest processing time, at 40.73 s, to process 2320 images with a CA of 95.59%.When considering the memory requirements for hardware deployment, SqueezeNet-MOD2 would be recommended for PM detection on strawberry leaves with a CA of 92.61%.
This paper examines the combined effects of density stratifications in porous media due to both g... more This paper examines the combined effects of density stratifications in porous media due to both gravitactic microorganism motion and thermal or solutal solicitation. These effects are coined in the literature by the term "double diffusion." In this case, an equation of conservation of ...
International Journal of Heat and Mass Transfer, Apr 1, 2008
This paper examines the onset of negative gravitactic bioconvection in a porous square cavity. Th... more This paper examines the onset of negative gravitactic bioconvection in a porous square cavity. The porous medium is assumed to be anisotropic in permeability with its principal axes arbitrarily oriented with respect to the gravity vector, and contains a concentration of ...
Computers and Electronics in Agriculture, Apr 1, 2021
Abstract In this study, Deep Learning (DL) was used to detect powdery mildew (PM), persistent fun... more Abstract In this study, Deep Learning (DL) was used to detect powdery mildew (PM), persistent fungal disease in strawberries to reduce the amount of unnecessary fungicide use, and the need for field scouts. This study optimised and evaluated several well-established learners, including AlexNet, SqueezeNet, GoogLeNet, ResNet-50, SqueezeNet-MOD1, and SqueezeNet-MOD2. Data augmentation was carried out from among 1450 healthy and infected leaf images to prevent overfitting and to consider the various shapes and direction of the leaves in the field. A total of eight clockwise rotations (0°; the original data, 45°, 90°, 135°, 180°, 225°, 270°, and 315°) was performed to generate 11,600 data points. Overall, the six DL algorithms that were used in this study showed on average of >92% in classification accuracy (CA). ResNet-50 gave the highest CA of 98.11% in classifying the healthy and infected leaves; however, considering the computation time, AlexNet had the fastest processing time, at 40.73 s, to process 2320 images with a CA of 95.59%.When considering the memory requirements for hardware deployment, SqueezeNet-MOD2 would be recommended for PM detection on strawberry leaves with a CA of 92.61%.
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