REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS

65 citations as recorded by crossref.

1. Machine learning in agriculture: a review of crop management applications I. Attri et al. https://doi.org/10.1007/s11042-023-16105-2
2. Weed Management of the Future S. Amend et al. https://doi.org/10.1007/s13218-019-00617-x
3. Maize Tassel Detection From UAV Imagery Using Deep Learning A. Alzadjali et al. https://doi.org/10.3389/frobt.2021.600410
4. Seeding Crop Detection Framework Using Prototypical Network Method in UAV Images D. Zhang et al. https://doi.org/10.3390/agriculture12010026
5. Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey I. Zualkernan et al. https://doi.org/10.3390/drones7060382
6. Crop classification using aerial images by analyzing an ensemble of DCNNs under multi-filter & multi-scale framework I. Kalita et al. https://doi.org/10.1007/s11042-022-13946-1
7. A survey of deep learning techniques for weed detection from images A. Hasan et al. https://doi.org/10.1016/j.compag.2021.106067
8. Training instance segmentation neural network with synthetic datasets for crop seed phenotyping Y. Toda et al. https://doi.org/10.1038/s42003-020-0905-5
9. A deep learning model to predict lower temperatures in agriculture M. Guillén-Navarro et al. https://doi.org/10.3233/AIS-200546
10. An open source workflow for weed mapping in native grassland using unmanned aerial vehicle: using Rumex obtusifolius as a case study O. Lam et al. https://doi.org/10.1080/22797254.2020.1793687
11. Role of digital, hyper spectral, and SAR images in detection of plant disease with deep learning network V. Bhujade & V. Sambhe https://doi.org/10.1007/s11042-022-13055-z
12. Deep Convolutional Neural Networks for Weeds and Crops Discrimination From UAS Imagery L. Hashemi-Beni et al. https://doi.org/10.3389/frsen.2022.755939
13. A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images K. Osorio et al. https://doi.org/10.3390/agriengineering2030032
14. Weed detection in soybean crops using custom lightweight deep learning models N. Razfar et al. https://doi.org/10.1016/j.jafr.2022.100308
15. Aprendizaje profundo para la detección de malezas en sistemas agrícolas y forestales mesoamericanos M. Guevara-Bonilla et al. https://doi.org/10.15517/eyatrf23
16. Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research M. Ouhami et al. https://doi.org/10.3390/rs13132486
17. Focus on the Crop Not the Weed: Canola Identification for Precision Weed Management Using Deep Learning M. Mckay et al. https://doi.org/10.3390/rs16112041
18. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing T. Kattenborn et al. https://doi.org/10.1016/j.isprsjprs.2020.12.010
19. Image-based deep learning automated sorting of date fruit A. Nasiri et al. https://doi.org/10.1016/j.postharvbio.2019.04.003
20. Automated crop plant detection based on the fusion of color and depth images for robotic weed control J. Gai et al. https://doi.org/10.1002/rob.21897
21. A survey: to identify plant leaf diseases by feature extraction methods and classification techniques K. Soni & R. Chandra Gangwar https://doi.org/10.1007/s00425-025-04797-9
22. CRowNet: Deep Network for Crop Row Detection in UAV Images M. Bah et al. https://doi.org/10.1109/ACCESS.2019.2960873
23. A W-shaped convolutional network for robust crop and weed classification in agriculture S. Moazzam et al. https://doi.org/10.1007/s11119-023-10027-7
24. Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images M. Bah et al. https://doi.org/10.3390/rs10111690
25. Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images C. Rasmussen & T. Moeslund https://doi.org/10.3390/s19163506
26. Joint Plant Instance Detection and Leaf Count Estimation for In-Field Plant Phenotyping J. Weyler et al. https://doi.org/10.1109/LRA.2021.3060712
27. Design and Development of a Smart Variable Rate Sprayer Using Deep Learning N. Hussain et al. https://doi.org/10.3390/rs12244091
28. Smart Agriculture Applications Using Deep Learning Technologies: A Survey M. Altalak et al. https://doi.org/10.3390/app12125919
29. Automatic Parameter Tuning for Adaptive Thresholding in Fruit Detection E. Zemmour et al. https://doi.org/10.3390/s19092130
30. Using time-series imagery and 3DLSTM model to classify individual tree species C. Chen et al. https://doi.org/10.1080/17538947.2024.2308728
31. Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review H. Qu & W. Su https://doi.org/10.3390/agronomy14020363
32. Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge C. Lammie et al. https://doi.org/10.1109/ACCESS.2019.2911709
33. WEED DETECTION ON CARROTS USING CONVOLUTIONAL NEURAL NETWORK AND INTERNET OF THING BASED SMARTPHONE L. Patria et al. https://doi.org/10.35784/iapgos.5968
34. Development of Deep Learning-Based Variable Rate Agrochemical Spraying System for Targeted Weeds Control in Strawberry Crop J. Liu et al. https://doi.org/10.3390/agronomy11081480
35. Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review B. Tugrul et al. https://doi.org/10.3390/agriculture12081192
36. Corn emergence uniformity estimation and mapping using UAV imagery and deep learning C. Vong et al. https://doi.org/10.1016/j.compag.2022.107008
37. Robust joint stem detection and crop‐weed classification using image sequences for plant‐specific treatment in precision farming P. Lottes et al. https://doi.org/10.1002/rob.21901
38. Plant image recognition with deep learning: A review Y. Chen et al. https://doi.org/10.1016/j.compag.2023.108072
39. Plant Disease Detection and Classification by Deep Learning M. Saleem et al. https://doi.org/10.3390/plants8110468
40. Şeker Pancarı Üretiminde Kullanılan Yapay Zekâ Teknikleri Y. ÇİÇEK et al. https://doi.org/10.53608/estudambilisim.1102769
41. Transforming Apple Grading: Standards Survey and Deep Learning Insights Z. Feng et al. https://doi.org/10.1007/s11947-025-03822-9
42. Remote sensing for precision weed management Y. Huang et al. https://doi.org/10.1002/ps.8833
43. Deep learning in agriculture: A survey A. Kamilaris & F. Prenafeta-Boldú https://doi.org/10.1016/j.compag.2018.02.016
44. Real-Time Detection of Seedling Maize Weeds in Sustainable Agriculture S. Liu et al. https://doi.org/10.3390/su142215088
45. Deep Orange: Mask R-CNN based Orange Detection and Segmentation P. Ganesh et al. https://doi.org/10.1016/j.ifacol.2019.12.499
46. Fully Convolutional Networks With Sequential Information for Robust Crop and Weed Detection in Precision Farming P. Lottes et al. https://doi.org/10.1109/LRA.2018.2846289
47. Classification of plant diseases using machine and deep learning M. Lamba et al. https://doi.org/10.1515/comp-2020-0122
48. Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN L. Quan et al. https://doi.org/10.1016/j.biosystemseng.2019.05.002
49. Deep Learning-Based Object Detection System for Identifying Weeds Using UAS Imagery A. Etienne et al. https://doi.org/10.3390/rs13245182
50. Goosegrass Detection in Strawberry and Tomato Using a Convolutional Neural Network S. Sharpe et al. https://doi.org/10.1038/s41598-020-66505-9
51. Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images J. Yi et al. https://doi.org/10.3390/s20205893
52. Synthetic bootstrapping of convolutional neural networks for semantic plant part segmentation R. Barth et al. https://doi.org/10.1016/j.compag.2017.11.040
53. Identification of Tobacco Crop Based on Machine Learning for a Precision Agricultural Sprayer M. Tufail et al. https://doi.org/10.1109/ACCESS.2021.3056577
54. Towards Domain Generalization in Crop and Weed Segmentation for Precision Farming Robots J. Weyler et al. https://doi.org/10.1109/LRA.2023.3262417
55. Controlled Lighting and Illumination-Independent Target Detection for Real-Time Cost-Efficient Applications. The Case Study of Sweet Pepper Robotic Harvesting B. Arad et al. https://doi.org/10.3390/s19061390
56. Transformer Neural Network for Weed and Crop Classification of High Resolution UAV Images R. Reedha et al. https://doi.org/10.3390/rs14030592
57. Coconut trees detection and segmentation in aerial imagery using mask region‐based convolution neural network M. Iqbal et al. https://doi.org/10.1049/cvi2.12028
58. Advances in site‐specific weed management in agriculture—A review R. Gerhards et al. https://doi.org/10.1111/wre.12526
59. Joint Plant and Leaf Instance Segmentation on Field-Scale UAV Imagery J. Weyler et al. https://doi.org/10.1109/LRA.2022.3147462
60. Improved generalization of a plant-detection model for precision weed control T. Ruigrok et al. https://doi.org/10.1016/j.compag.2022.107554
61. Application-Specific Evaluation of a Weed-Detection Algorithm for Plant-Specific Spraying T. Ruigrok et al. https://doi.org/10.3390/s20247262
62. Vegetation detection and discrimination within vegetable plasticulture row-middles using a convolutional neural network S. Sharpe et al. https://doi.org/10.1007/s11119-019-09666-6
63. Computer Vision and Machine Learning based approaches for Food Security: A Review S. Sood & H. Singh https://doi.org/10.1007/s11042-021-11036-2
64. Applications of deep learning in precision weed management: A review N. Rai et al. https://doi.org/10.1016/j.compag.2023.107698
65. Weed Identification in Maize, Sunflower, and Potatoes with the Aid of Convolutional Neural Networks G. Peteinatos et al. https://doi.org/10.3390/rs12244185