A Deep Learning-Based Semantic Segmentation Model for Discriminating Cultivated Plants from Weeds in Agricultural Scenes

Eshonqulova, Feruza

Al-Farg'oniy avlodlari · 2026-yil

Annotatsiya

This article analyzes the problem of pixel-level separation of crop plants from weeds in agricultural images. In this study, the semantic segmentation models DeepLabV3+, U-Net, and U-Net++ were evaluated under identical conditions using the publicly available CWFID dataset. To reduce severe class imbalance, a combination of Dice and Focal loss functions was applied. According to the obtained results, the U-Net++ model achieved the highest performance on the test set, reaching 77.87% mIoU and 97.57% pixel accuracy, confirming its high effectiveness. The research findings can serve as a methodological basis for developing specialized local models for agricultural crops in future studies.

Maqola ma’lumotlari
MualliflarEshonqulova, Feruza
JurnalAl-Farg'oniy avlodlari
NashriyotТАТУ Фарғона филиали
ISSN2181-4252
Nashr sanasi2026-05-30
Son2
Betlar265-272
TilIngliz
Ilmiy sohaTexnika
Indeksga qo‘shilgan2026-08-26

Maqolaning asl sahifasi

Kalit so‘zlar

deep learning, semantic segmentation, U-Net++, DeepLabV3+, U-Net, convolutional neural network, weed detection, precision agriculture, CWFID, глубoкoе oбучение, семaнтическaя сегментaция, U-Net++, DeepLabV3+, U-Net, свертoчнaя нейрoннaя сеть, oбнaружение сoрнякoв, тoчнoе земледелие, CWFID, chuqur o'rganish, semantik segmentatsiya, U-Net++, DeepLabV3+, U-Net, konvolyutsion neyron tarmoq, begona o'tlarni aniqlash, aniq dehqonchilik, CWFID

Ilmiy soha

Texnika

Iqtibos keltirish

APAEshonqulova, Feruza (2026). A Deep Learning-Based Semantic Segmentation Model for Discriminating Cultivated Plants from Weeds in Agricultural Scenes. Al-Farg'oniy avlodlari, (2), 265-272. https://al-fargoniy.uz/index.php/journal/article/view/1184
GOST (OAK)Eshonqulova, Feruza A Deep Learning-Based Semantic Segmentation Model for Discriminating Cultivated Plants from Weeds in Agricultural Scenes // Al-Farg'oniy avlodlari. – 2026. – № 2. – P. 265-272.
MLAEshonqulova, Feruza. “A Deep Learning-Based Semantic Segmentation Model for Discriminating Cultivated Plants from Weeds in Agricultural Scenes.” Al-Farg'oniy avlodlari, (2), 265-272, 2026. https://al-fargoniy.uz/index.php/journal/article/view/1184
ChicagoEshonqulova, Feruza. “A Deep Learning-Based Semantic Segmentation Model for Discriminating Cultivated Plants from Weeds in Agricultural Scenes.” Al-Farg'oniy avlodlari, (2), 265-272 (2026). https://al-fargoniy.uz/index.php/journal/article/view/1184
HarvardEshonqulova, Feruza (2026) ‘A Deep Learning-Based Semantic Segmentation Model for Discriminating Cultivated Plants from Weeds in Agricultural Scenes’, Al-Farg'oniy avlodlari, (2), 265-272. https://al-fargoniy.uz/index.php/journal/article/view/1184
BibTeX
@article{eshonqulovaferuza2026,
  title = {A Deep Learning-Based Semantic Segmentation Model for Discriminating Cultivated Plants from Weeds in Agricultural Scenes},
  author = {Eshonqulova, Feruza},
  journal = {Al-Farg'oniy avlodlari},
  year = {2026},
  number = {2},
  pages = {265-272},
  url = {https://al-fargoniy.uz/index.php/journal/article/view/1184}
}

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