| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
CactiViT: Image-based smartphone application and transformer network for diagnosis of cactus cochineal
|
Berka, Anas
|
|
|
9 |
C |
p. 12-21
|
artikel
|
| 2 |
Comparison of CNN-based deep learning architectures for rice diseases classification
|
Ahad, Md Taimur
|
|
|
9 |
C |
p. 22-35
|
artikel
|
| 3 |
Corn kernel classification from few training samples
|
Suárez, Patricia L.
|
|
|
9 |
C |
p. 89-99
|
artikel
|
| 4 |
Deep learning methods for biotic and abiotic stresses detection and classification in fruits and vegetables: State of the art and perspectives
|
Houetohossou, Sèton Calmette Ariane
|
|
|
9 |
C |
p. 46-60
|
artikel
|
| 5 |
Detecting broiler chickens on litter floor with the YOLOv5-CBAM deep learning model
|
Guo, Yangyang
|
|
|
9 |
C |
p. 36-45
|
artikel
|
| 6 |
Development and evaluation of temperature-based deep learning models to estimate reference evapotranspiration
|
Singh, Amninder
|
|
|
9 |
C |
p. 61-75
|
artikel
|
| 7 |
Estimation of morphological traits of foliage and effective plant spacing in NFT-based aquaponics system
|
Abbasi, R.
|
|
|
9 |
C |
p. 76-88
|
artikel
|
| 8 |
Low-cost livestock sorting information management system based on deep learning
|
Pan, Yuanzhi
|
|
|
9 |
C |
p. 110-126
|
artikel
|
| 9 |
Machine learning in nutrient management: A review
|
Ennaji, Oumnia
|
|
|
9 |
C |
p. 1-11
|
artikel
|
| 10 |
Rice disease identification method based on improved CNN-BiGRU
|
Lu, Yang
|
|
|
9 |
C |
p. 100-109
|
artikel
|
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