| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A comparative analysis of paddy crop biotic stress classification using pre-trained deep neural networks
|
Malvade, Naveen N.
|
|
|
6 |
C |
p. 167-175
|
artikel
|
| 2 |
Analysis of land surface temperature using Geospatial technologies in Gida Kiremu, Limu, and Amuru District, Western Ethiopia
|
Moisa, Mitiku Badasa
|
|
|
6 |
C |
p. 90-99
|
artikel
|
| 3 |
Assessing the performance of YOLOv5 algorithm for detecting volunteer cotton plants in corn fields at three different growth stages
|
Yadav, Pappu Kumar
|
|
|
6 |
C |
p. 292-303
|
artikel
|
| 4 |
A study on deep learning algorithm performance on weed and crop species identification under different image background
|
G C, Sunil
|
|
|
6 |
C |
p. 242-256
|
artikel
|
| 5 |
A systematic review of machine learning techniques for cattle identification: Datasets, methods and future directions
|
Hossain, Md Ekramul
|
|
|
6 |
C |
p. 138-155
|
artikel
|
| 6 |
Automatic marker-free registration of single tree point-cloud data based on rotating projection
|
Xu, Xiuxian
|
|
|
6 |
C |
p. 176-188
|
artikel
|
| 7 |
Deep convolutional neural network for damaged vegetation segmentation from RGB images based on virtual NIR-channel estimation
|
Picon, Artzai
|
|
|
6 |
C |
p. 199-210
|
artikel
|
| 8 |
Deep convolutional neural network models for weed detection in polyhouse grown bell peppers
|
Subeesh, A.
|
|
|
6 |
C |
p. 47-54
|
artikel
|
| 9 |
Deep learning based computer vision approaches for smart agricultural applications
|
Dhanya, V.G.
|
|
|
6 |
C |
p. 211-229
|
artikel
|
| 10 |
Developing a multi-label tinyML machine learning model for an active and optimized greenhouse microclimate control from multivariate sensed data
|
Ihoume, Ilham
|
|
|
6 |
C |
p. 129-137
|
artikel
|
| 11 |
Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning
|
Kundu, Nidhi
|
|
|
6 |
C |
p. 276-291
|
artikel
|
| 12 |
Durum wheat yield forecasting using machine learning
|
Chergui, Nabila
|
|
|
6 |
C |
p. 156-166
|
artikel
|
| 13 |
Effect and economic benefit of precision seeding and laser land leveling for winter wheat in the middle of China
|
Chen, Jing
|
|
|
6 |
C |
p. 1-9
|
artikel
|
| 14 |
Evaluation of model generalization for growing plants using conditional learning
|
Ullah, Hafiz Sami
|
|
|
6 |
C |
p. 189-198
|
artikel
|
| 15 |
Examining the interplay between artificial intelligence and the agri-food industry
|
Rejeb, Abderahman
|
|
|
6 |
C |
p. 111-128
|
artikel
|
| 16 |
Explainable artificial intelligence and interpretable machine learning for agricultural data analysis
|
Ryo, Masahiro
|
|
|
6 |
C |
p. 257-265
|
artikel
|
| 17 |
Few-shot learning for biotic stress classification of coffee leaves
|
Tassis, Lucas M.
|
|
|
6 |
C |
p. 55-67
|
artikel
|
| 18 |
Land suitability analysis for maize production using geospatial technologies in the Didessa watershed, Ethiopia
|
Moisa, Mitiku Badasa
|
|
|
6 |
C |
p. 34-46
|
artikel
|
| 19 |
Non-destructive silkworm pupa gender classification with X-ray images using ensemble learning
|
Thomas, Sania
|
|
|
6 |
C |
p. 100-110
|
artikel
|
| 20 |
Nutrient optimization for plant growth in Aquaponic irrigation using Machine Learning for small training datasets
|
Dhal, Sambandh Bhusan
|
|
|
6 |
C |
p. 68-76
|
artikel
|
| 21 |
Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification
|
Raouhi, El Mehdi
|
|
|
6 |
C |
p. 77-89
|
artikel
|
| 22 |
Predicting the true density of commercial biomass pellets using near-infrared hyperspectral imaging
|
Pitak, Lakkana
|
|
|
6 |
C |
p. 266-275
|
artikel
|
| 23 |
Prediction of exchangeable potassium in soil through mid-infrared spectroscopy and deep learning: From prediction to explainability
|
Albinet, Franck
|
|
|
6 |
C |
p. 230-241
|
artikel
|
| 24 |
Review of agricultural IoT technology
|
Xu, Jinyuan
|
|
|
6 |
C |
p. 10-22
|
artikel
|
| 25 |
Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG
|
Paymode, Ananda S.
|
|
|
6 |
C |
p. 23-33
|
artikel
|
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