Review Articles Open access

A review of deep learning based agricultural remote sensing image segmentation

Authors

Published
1 December 2025
Section
Review Articles
DOI
10.4081/jae.2025.1954

Abstract

Agricultural remote sensing image segmentation, which involves classifying each pixel of an image into a specific category, has recently been driven by deep learning methods due to their powerful feature extraction capabilities. This paper presents a systematic review of deep learning-based image segmentation techniques for agricultural remote sensing, along with an overview of current challenges and emerging research trends. First, it outlines the characteristics of agricultural remote sensing tasks and the requirements for remote sensing image acquisition and processing, providing an in-depth analysis of the nature of agricultural remote sensing data. Next, it systematically reviews the evolution of deep learning-based methods, with a focus on summarizing segmentation network architectures, including convolution-based models, transformer-based models, hybrid architectures, lightweight models, and vision-language models. Moreover, it discusses several deep learning paradigms designed for annotation-efficient scenarios, including semi-supervised, weakly supervised, self-supervised, and transfer learning. Then, it offers an in-depth analysis of key challenges, such as data annotation, computational cost, and model generalization. Finally, it summarizes the latest advances in deep learning for agricultural remote sensing image segmentation and outlines potential future research directions, aiming to provide technical references that promote the practical application and successful deployment of deep learning in this critical domain.

Keywords

References

  1. Abbas A, Jain S, Gour M, Vankudothu S, 2021. Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput Electron Agric 187:106279. DOI: https://doi.org/10.1016/j.compag.2021.106279
  2. Abouzahir S, Sadik M, Sabir E, 2017. IoT-empowered smart agriculture: A real-time light-weight embedded segmentation system. In: E. Sabir, A. García Armada, M. Ghogho, M. Debbah (eds.), Ubiquitous networking. UNet 2017. Cham, Springer; pp. 319-332. DOI: https://doi.org/10.1007/978-3-319-68179-5_28
  3. Aguilar M, Agüera F, Aguilar F, Carvajal F, 2008. Geometric accuracy assessment of the orthorectification process from very high resolution satellite imagery for Common Agricultural Policy purposes. Int J Remote Sens 29:7181-7197. DOI: https://doi.org/10.1080/01431160802238393
  4. Ahlswede S, Madam NT, Schulz C, Kleinschmit B, Demir B, 2022. Weakly supervised semantic segmentation of remote sensing images for tree species classification based on explanation methods. Proc. IEEE Int. Geoscience and Remote Sensing Symp, Kuala Lumpur; pp. 4846–4849. DOI: https://doi.org/10.1109/IGARSS46834.2022.9884676
  5. Ahamed T, Tian L, Jiang Y, Zhao B, Liu H, Ting K, 2012. Tower remote-sensing system for monitoring energy crops; Image acquisition and geometric corrections. Biosyst Eng 112:93-107. DOI: https://doi.org/10.1016/j.biosystemseng.2012.03.003
  6. Ahmad H, Sun J, Nirere A, Shaheen N, Zhou X, Yao K, 2021. Classification of tea varieties based on fluorescence hyperspectral image technology and ABC-SVM algorithm. J Food Process Preserv 45:e15241. DOI: https://doi.org/10.1111/jfpp.15241
  7. Alam M, Wang J, Guangpei C, Yunrong L, Chen Y, 2021. Convolutional neural network for the semantic segmentation of remote sensing images. Mob Netw Appl 26:200-215. DOI: https://doi.org/10.1007/s11036-020-01703-3
  8. Alzubaidi L, Chlaib H, Fadhel M, Chen Y, Bai J, Albahri A, et al., 2024. Reliable deep learning framework for the ground penetrating radar data to locate the horizontal variation in levee soil compaction. Eng Appl Artif Intell 129:107627. DOI: https://doi.org/10.1016/j.engappai.2023.107627
  9. Arango R, Campos A, Combarro E, Canas E, Díaz I, 2016. Mapping cultivable land from satellite imagery with clustering algorithms. Int J Appl Earth Obs Geoinf 49:99-106. DOI: https://doi.org/10.1016/j.jag.2016.01.009
  10. Astruc G, Gonthier N, Mallet C, Landrieu L, 2025. OmniSat: Self-supervised modality fusion for Earth observation. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, G. Varol (eds.), Computer Vision – ECCV 2024. Cham, Springer; pp. 409-427. DOI: https://doi.org/10.1007/978-3-031-73390-1_24
  11. Awais M, Li W, Hussain S, Cheema M, Li W, Song R, et al., 2022. Comparative evaluation of land surface temperature images from unmanned aerial vehicle and satellite observation for agricultural areas using in situ data. Agriculture 12:184. DOI: https://doi.org/10.3390/agriculture12020184
  12. Badrinarayanan V, Kendall A, Cipolla R, 2017. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE T Pattern Anal Mach Intell 39:2481-2495. DOI: https://doi.org/10.1109/TPAMI.2016.2644615
  13. Banks S, White L, Behnamian A, Chen Z, Montpetit B, Brisco B, et al., 2019. Wetland classification with multi-angle/temporal SAR using random forests. Remote Sens 11:670. DOI: https://doi.org/10.3390/rs11060670
  14. Bannari A, Morin D, Bénié GB, Bonn FJ, 1995. A theoretical review of different mathematical models of geometric corrections applied to remote sensing images. Remote Sens Rev 13:27-47. DOI: https://doi.org/10.1080/02757259509532295
  15. Bazi Y, Bashmal L, Al Rahhal M, Al Dayil R, Al Ajlan N, 2021. Vision transformers for remote sensing image classification. Remote Sens 13:516. DOI: https://doi.org/10.3390/rs13030516
  16. Beriaux E, Jago A, Lucau-Danila C, Planchon V, Defourny P. 2021. Sentinel-1 time series for crop identification in the framework of the future CAP monitoring. Remote Sens 13:2785. DOI: https://doi.org/10.3390/rs13142785
  17. Cartolano A, Cuzzocrea A, Pilato G, 2024. Analyzing and assessing explainable AI models for smart agriculture environments. Multim Tools Appl 83:1–22. DOI: https://doi.org/10.1007/s11042-023-17978-z
  18. Castillo-Martínez M, Gallegos-Funes F, Carvajal-Gámez B, Urriolagoitia-Sosa G, Rosales-Silva A, 2020. Color index based thresholding method for background and foreground segmentation of plant images. Comput Electron Agric 178:105783. DOI: https://doi.org/10.1016/j.compag.2020.105783
  19. Chandra S, Hareendran S, Albaaji G, 2024. Precision farming for sustainability: An agricultural intelligence model. Comput Electron Agric 226:109386. DOI: https://doi.org/10.1016/j.compag.2024.109386
  20. Charisis C, Argyropoulos D, 2024. Deep learning-based instance segmentation architectures in agriculture: A review of the scopes and challenges. Smart Agric Technol 8:100448. DOI: https://doi.org/10.1016/j.atech.2024.100448
  21. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL, 2014. Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv 1412.7062v4.
  22. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL, 2018. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE T Pattern Anal Mach Intell 40:834-848. DOI: https://doi.org/10.1109/TPAMI.2017.2699184
  23. Chen LC, Papandreou G, Schroff F, Adam H, 2017. Rethinking atrous convolution for semantic image segmentation. arXiv 1706.05587.
  24. Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H, 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: V. Ferrari, M. Hebert, C. Sminchisescu, Y. Weiss (eds.), Computer Vision – ECCV 2018. Cham, Springer; pp. 801–818. DOI: https://doi.org/10.1007/978-3-030-01234-2_49
  25. Cheng J Zhu Y, Zhao Y, Li T, Chen M, Sun Q, et al., 2024. Application of an improved U-Net with image-to-image translation and transfer learning in peach orchard segmentation. Int J Appl Earth Obs Geoinf 130:103871. DOI: https://doi.org/10.1016/j.jag.2024.103871
  26. Chiu M, Xu X, Wei Y, Huang Z, Schwing A, Brunner R, et al., 2020. Agriculture-Vision: A large aerial image database for agricultural pattern analysis. Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), Seattle; pp. 2825–2835. DOI: https://doi.org/10.1109/CVPR42600.2020.00290
  27. Cui X, Han W, Zhang H, Dong Y, Ma W, Zhai X, et al., 2023. Estimating and mapping the dynamics of soil salinity under different crop types using Sentinel-2 satellite imagery. Geoderma 440:116738. DOI: https://doi.org/10.1016/j.geoderma.2023.116738
  28. Darwin B, Dharmaraj P, Prince S, Popescu D, Hemanth D, 2021. Recognition of bloom/yield in crop images using deep learning models for smart agriculture: A review. Agronomy 11:646. DOI: https://doi.org/10.3390/agronomy11040646
  29. de Souza E, Scharf P, Sudduth K, 2010. Sun position and cloud effects on reflectance and vegetation indices of corn. Agron J 102:734-744. DOI: https://doi.org/10.2134/agronj2009.0206
  30. Deng L, Mao Z, Li X, Hu Z, Duan F, Yan Y, 2018. UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras. ISPRS J Photogramm Remote Sens 146:124-136. DOI: https://doi.org/10.1016/j.isprsjprs.2018.09.008
  31. Di S, Liao M, Zhao Y, Li Y, Zeng Y, 2021. Image superpixel segmentation based on hierarchical multi-level LI-SLIC. Opt Laser Technol 135:106703. DOI: https://doi.org/10.1016/j.optlastec.2020.106703
  32. Dobrota C, Carpa R, Butiuc-Keul A, 2021. Analysis of designs used in monitoring crop growth based on remote sensing methods. Turk J Agric For 45:730-742. DOI: https://doi.org/10.3906/tar-2012-79
  33. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al., 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv 2010.11929v2.
  34. Du S, Du S, Liu B, Zhang X, 2021. Incorporating DeepLabV3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images. Int J Digit Earth 14:357-378. DOI: https://doi.org/10.1080/17538947.2020.1831087
  35. El Sakka M, Mothe J, Ivanovici M, 2024. Images and CNN applications in smart agriculture. Eur J Remote Sens 57:2352386. DOI: https://doi.org/10.1080/22797254.2024.2352386
  36. Feng G, Wang C, Wang A, Gao Y, Zhou Y, Huang S, et al., 2024. Segmentation of wheat lodging areas from UAV imagery using an ultra-lightweight network. Agriculture 14:244. DOI: https://doi.org/10.3390/agriculture14020244
  37. Flores C, Valenzuela A, Verschae R, 2024. Active learning for image classification: A comprehensive analysis in agriculture. In: X.S. Yang, R.S. Sherratt, N. Dey, A. Joshi. (eds), Proc. 9th Int. Cong. on Information and Communication Technology. ICICT 2024. Singapore, Springer; pp. 607-616. DOI: https://doi.org/10.1007/978-981-97-5441-0_49
  38. Gao B, Montes M, Davis C, Goetz A, 2009. Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sens Environ 113: S17-S24. DOI: https://doi.org/10.1016/j.rse.2007.12.015
  39. Gao J, Wang B, Wang Z, Wang Y, Kong F, 2020. A wavelet transform-based image segmentation method. Optik 208:164123. DOI: https://doi.org/10.1016/j.ijleo.2019.164123
  40. Gao L, Liu H, Yang M, Chen L, Wan Y, Xiao Z, et al., 2021. STransFuse: fusing Swin transformer and convolutional neural network for remote sensing image semantic segmentation. IEEE J Sel Top Appl Earth Obs Remote Sens 14:10990-11003. DOI: https://doi.org/10.1109/JSTARS.2021.3119654
  41. Garioud A, Gonthier N, Landrieu L, De Wit A, Valette M, Poupée M, et al., 2023. FLAIR: A country-scale land cover semantic segmentation dataset from multi-source optical imagery. arXiv 2310.13336v1.
  42. Garnot V, Landrieu L, 2021. Panoptic segmentation of satellite image time series with convolutional temporal attention networks. Proc. 18th IEEE/CVF Int. Conf. on Computer Vision (ICCV), Montreal; pp. 4852-4861. DOI: https://doi.org/10.1109/ICCV48922.2021.00483
  43. Ghosh R, Ravirathinam P, Jia X, Khandelwal A, Mulla D, Kumar V, 2021. CalCROP21: A georeferenced multi-spectral dataset of satellite imagery and crop labels. Proc. 9th IEEE Int. Conf. on Big Data, Orlando; pp. 1625-1632. DOI: https://doi.org/10.1109/BigData52589.2021.9671569
  44. Gilles J, 2013. Empirical wavelet transform. IEEE T Signal Process 61:3999-4010. DOI: https://doi.org/10.1109/TSP.2013.2265222
  45. Gonzalo-Martín C, Lillo-Saavedra M, García-Pedrero A, Lagos O, Menasalvas E, 2017. Daily evapotranspiration mapping using regression random forest models. IEEE J Sel Top Appl Earth Obs Remote Sens 10:5359–5368. DOI: https://doi.org/10.1109/JSTARS.2017.2733958
  46. Guijarro M, Riomoros I, Pajares G, Zitinski P, 2015. Discrete wavelets transform for improving greenness image segmentation in agricultural images. Comput Electron Agric 118:396-407. DOI: https://doi.org/10.1016/j.compag.2015.09.011
  47. Hadjimitsis D, Papadavid G, Agapiou A, Themistocleous K, Hadjimitsis M, Retalis A, et al., 2010. Atmospheric correction for satellite remotely sensed data intended for agricultural applications: Impact on vegetation indices. Nat Hazards Earth Syst Sci 10:89-95. DOI: https://doi.org/10.5194/nhess-10-89-2010
  48. Han K, Wang Y, Chen H, Chen X, Guo J, Liu Z, et al., 2023. A survey on vision transformer. IEEE T Pattern Anal Mach Intell 45:87-110. DOI: https://doi.org/10.1109/TPAMI.2022.3152247
  49. Hassanein M, Lari Z, El-Sheimy N, 2018. A new vegetation segmentation approach for cropped fields based on threshold detection from hue histograms. Sensors (Basel) 18:1253. DOI: https://doi.org/10.3390/s18041253
  50. He J, Zhao L, Yang H, Zhang M, Li W, 2020. HSI-BERT: Hyperspectral image classification using the bidirectional encoder representation from transformers. IEEE T Geosci Remote Sens 58:165-178. DOI: https://doi.org/10.1109/TGRS.2019.2934760
  51. He X, Zhou Y, Zhao J, Zhang D, Yao R, Xue Y, 2022. Swin transformer embedding UNet for remote sensing image semantic segmentation. IEEE T Geosci Remote Sens 60:4408715. DOI: https://doi.org/10.1109/TGRS.2022.3144165
  52. Helber P, Bischke B, Dengel A, Borth D, 2019. EuroSAT: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE J Sel Top Appl Earth Obs Remote Sens 12:2217-2226. DOI: https://doi.org/10.1109/JSTARS.2019.2918242
  53. Holder CJ, Shafique M, 2022. On efficient real-time semantic segmentation: A survey. arXiv 2206.08605.
  54. Hong D, Han Z, Yao J, Gao L, Zhang B, Plaza A, et al., 2022. SpectralFormer: Rethinking hyperspectral image classification with transformers. IEEE T Geosci Remote Sens 60:5518615. DOI: https://doi.org/10.1109/TGRS.2021.3130716
  55. Hong D, Zhang B, Li X, Li Y, Li C, Yao J, et al., 2024. SpectralGPT: Spectral remote sensing foundation model. IEEE T Pattern Anal Mach Intell 46:5227–5244. DOI: https://doi.org/10.1109/TPAMI.2024.3362475
  56. Hua Y, Marcos D, Mou L, Zhu X, Tuia D, 2022. Semantic segmentation of remote sensing images with sparse annotations. IEEE Geosci Remote Sens Lett 19:3051053. DOI: https://doi.org/10.1109/LGRS.2021.3051053
  57. Jia W, Zheng Y, Zhao D, Yin X, Liu X, Du R, 2018. Preprocessing method of night vision image application in apple harvesting robot. Int J Agric Biol Eng 11:158-163. DOI: https://doi.org/10.25165/j.ijabe.20181102.2822
  58. Jiang J, Lyu C, Liu S, He Y, Hao X, 2020. RWSNet: A semantic segmentation network based on SegNet combined with random walk for remote sensing. Int J Remote Sens 41:487-505. DOI: https://doi.org/10.1080/01431161.2019.1643937
  59. Jiang Y, Li C, 2020. Convolutional neural networks for image-based high-throughput plant phenotyping: A review. Plant Phenomics 2020:4152816. DOI: https://doi.org/10.34133/2020/4152816
  60. Jiang Y, Tang Y, Li H, 2022. A review of trends in the use of sewage irrigation technology from the livestock and poultry breeding industries for farmlands. Irrig Sci 40:297-308. DOI: https://doi.org/10.1007/s00271-022-00794-y
  61. Jonnala N, Siraaj S, Prastuti Y, Chinnababu P, Babu B, Bansal S, et al., 2025. AER U-Net: Attention-enhanced multi-scale residual U-Net structure for water body segmentation using Sentinel-2 satellite images. Sci Rep 15:16099. DOI: https://doi.org/10.1038/s41598-025-99322-z
  62. Karlson M, Ostwald M, Bayala J, Bazié HR, Ouedraogo AS, Soro B, et al., 2020. The potential of Sentinel-2 for crop production estimation in a smallholder agroforestry landscape, Burkina Faso. Front Environ Sci 8:85. DOI: https://doi.org/10.3389/fenvs.2020.00085
  63. Ke Q, Zhang P, 2022. Hybrid-TransCD: A hybrid transformer remote sensing image change detection network via token aggregation. ISPRS Int J Geo-Inf 11:263. DOI: https://doi.org/10.3390/ijgi11040263
  64. Kerner H, Sundar S, Satish M, 2024. Multi-region transfer learning for segmentation of crop field boundaries in satellite images with limited labels. arXiv 2404.00179.
  65. Khan S, Narvekar M, 2022. Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment. J King Saud Univ-Comput Inf Sci 34:3506-3516. DOI: https://doi.org/10.1016/j.jksuci.2020.09.006
  66. Khan S, Naseer M, Hayat M, Zamir S, Khan F, Shah M. 2022. Transformers in vision: A survey. ACM Comput Surv 54:200. DOI: https://doi.org/10.1145/3505244
  67. Koonce B, 2021. MobileNetV3. In: B. Koonce (ed.), Convolutional neural networks with Swift for Tensorflow: Image recognition and dataset categorization. Berkeley, Apress; pp. 125-144. DOI: https://doi.org/10.1007/978-1-4842-6168-2_11
  68. Lan Y, Huang K, Yang C, Lei L, Ye J, Zhang J, et al., 2021. Real-time identification of rice weeds by UAV low-altitude remote sensing based on improved semantic segmentation model. Remote Sens 13:4370. DOI: https://doi.org/10.3390/rs13214370
  69. LeCun Y, Bengio Y, Hinton G, 2015. Deep learning. Nature 521:36–444. DOI: https://doi.org/10.1038/nature14539
  70. Lei L, Yang Q, Yang L, Shen T, Wang R, Fu C, 2024. Deep learning implementation of image segmentation in agricultural applications: A comprehensive review. Artif Intell Rev 57:149. DOI: https://doi.org/10.1007/s10462-024-10775-6
  71. Li J, Cai Y, Li Q, Kou M, Zhang T, 2024. A review of remote sensing image segmentation by deep learning methods. Int J Digit Earth 17:2328827. DOI: https://doi.org/10.1080/17538947.2024.2328827
  72. Li J, Luo W, Han L, Cai Z, Guo Z, 2022. Two-wavelength image detection of early decayed oranges by coupling spectral classification with image processing. J Food Compos Anal 111:104642. DOI: https://doi.org/10.1016/j.jfca.2022.104642
  73. Li Z, Chen P, Shuai L, Wang M, Zhang L, Wang Y, et al., 2022. A copy paste and semantic segmentation-based approach for the classification and assessment of significant rice diseases. Plants 11:3174. DOI: https://doi.org/10.3390/plants11223174
  74. Li Z, Chen G, Zhang T, 2020. A CNN-Transformer hybrid approach for crop classification using multitemporal multisensor images. IEEE J Sel Top Appl Earth Obs Remote Sens 13: 847-858. DOI: https://doi.org/10.1109/JSTARS.2020.2971763
  75. Li L, Zhang W, Zhang X, Emam M, Jing W, 2023. Semi-supervised remote sensing image semantic segmentation method based on deep learning. Electronics 12:348. DOI: https://doi.org/10.3390/electronics12020348
  76. Li X, Wen C, Hu Y, Yuan Z, Zhu XX, 2024. Vision-language models in remote sensing: Current progress and future trends. IEEE Geosci Remote Sens Mag 12:32–66. DOI: https://doi.org/10.1109/MGRS.2024.3383473
  77. Liepa A, Thiel M, Taubenböck H, Steffan-Dewenter I, Abu IO, Singh Dhillon M, et al., 2024. Harmonized NDVI time-series from Landsat and Sentinel-2 reveal phenological patterns of diverse, small-scale cropping systems in East Africa. Remote Sens Appl Soc Environ 35:101230. DOI: https://doi.org/10.1016/j.rsase.2024.101230
  78. Liu B, Li B, Liu H, Li S, 2024. ST-MDAMNet: Swin transformer combines multi-dimensional attention mechanism for semantic segmentation of high-resolution earth surface images. Adv Space Res 74:3691–3705. DOI: https://doi.org/10.1016/j.asr.2024.06.056
  79. Liu B, Li B, Sreeram V, Li S, 2024. MBT-UNet: Multi-branch transform combined with UNet for semantic segmentation of remote sensing images. Remote Sens 16:2776. DOI: https://doi.org/10.3390/rs16152776
  80. Liu B, Yu A, Gao K, Tan X, Sun Y, Yu X, 2022. DSS-TRM: Deep spatial-spectral transformer for hyperspectral image classification. Eur J Remote Sens 55:103-114. DOI: https://doi.org/10.1080/22797254.2021.2023910
  81. Liu F, Chen D, Guan Z, Zhou X, Zhu J, Ye Q, et al., 2024. RemoteCLIP: A vision language foundation model for remote sensing. IEEE T Geosci Remote Sens 62:5622216. DOI: https://doi.org/10.1109/TGRS.2024.3390838
  82. Liu S, Cao S, Lu X, Peng J, Ping L, Fan X, et al., 2025. Lightweight deep learning model, ConvNeXt-U: An improved U-Net network for extracting cropland in complex landscapes from Gaofen-2 images. Sensors (Basel) 25:261. DOI: https://doi.org/10.3390/s25010261
  83. Liu Y, Lan Y, Chen X, 2025. Partial convolutional biformer: A transformer architecture for diagnosing crop diseases under complex backgrounds. Crop Prot 193:107007. DOI: https://doi.org/10.1016/j.cropro.2024.107007
  84. Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al., 2021. Swin transformer: Hierarchical vision transformer using shifted windows. Proc. IEEE/CVF Int. Conf. on Computer Vision, Montreal; pp. 10012-10022. DOI: https://doi.org/10.1109/ICCV48922.2021.00986
  85. Lu P, Zheng W, Lv X, Xu J, Zhang S, Li Y, et al., 2024. An extended method based on the geometric position of salient image features: Solving the dataset imbalance problem in greenhouse tomato growing scenarios. Agriculture 14:1893. DOI: https://doi.org/10.3390/agriculture14111893
  86. Lu Y, Chen D, Olaniyi E, Huang Y, 2022. Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review. Comput Electron Agric 200:107208. DOI: https://doi.org/10.1016/j.compag.2022.107208
  87. Luo Z, Yang W, Yuan Y, Gou R, Li X, 2024. Semantic segmentation of agricultural images: A survey. Inf Process Agric 11:172-186. DOI: https://doi.org/10.1016/j.inpa.2023.02.001
  88. Luo X, Liao J, Zang Y, Zhou Z, 2016. Improving agricultural mechanization level to promote agricultural sustainable development. T CSAE 32:1-11.
  89. Lv J, Shen Q, Lv M, Li Y, Shi L, Zhang P, 2023. Deep learning-based semantic segmentation of remote sensing images: A review. Front Ecol Evol 11:1201125. DOI: https://doi.org/10.3389/fevo.2023.1201125
  90. Ma D, Jiang L, Li J, Shi Y, 2023. Water index and Swin Transformer Ensemble (WISTE) for water body extraction from multispectral remote sensing images. GIScience Remote Sens 60:2251704. DOI: https://doi.org/10.1080/15481603.2023.2251704
  91. Ma N, Zhang X, Zheng HT, Sun J, 2018. ShuffleNet V2: Practical guidelines for efficient CNN architecture design. Proc. European Conf. on Computer Vision (ECCV). Cham, Springer; pp. 122-138. DOI: https://doi.org/10.1007/978-3-030-01264-9_8
  92. Mamat N, Othman M, Abdoulghafor R, Belhaouari S, Mamat N, Hussein S, 2022. Advanced technology in agriculture industry by implementing image annotation technique and deep learning approach: A review. Agriculture 12:1033. DOI: https://doi.org/10.3390/agriculture12071033
  93. Marsocci V, Scardapane S, Komodakis N, 2021. MARE: Self-supervised multi-attention REsu-Net for semantic segmentation in remote sensing. Remote Sens 13:3275. DOI: https://doi.org/10.3390/rs13163275
  94. Mehta S, Rastegari M, 2021. MobileViT: Light-weight, general-purpose, and mobile-friendly vision transformer. arXiv 2110.02178v2
  95. Memon M, Chen S, Niu Y, Zhou W, Elsherbiny O, Liang R, et al., 2023. Evaluating the efficacy of Sentinel-2B and Landsat-8 for estimating and mapping wheat straw cover in rice-wheat fields. Agronomy 13:2691. DOI: https://doi.org/10.3390/agronomy13112691
  96. Meng X, Yang Y, Wang L, Wang T, Li R, Zhang C, 2022. Class-guided swin transformer for semantic segmentation of remote sensing imagery. IEEE Geosci Remote Sens Lett 19:6517505. DOI: https://doi.org/10.1109/LGRS.2022.3215200
  97. Naseer M, Ranasinghe, K, Khan, S, Hayat, M, Khan, F, Yang, M. 2021. Intriguing properties of vision transformers. arXiv 2105.10497v3.
  98. Nazeer M, Ilori C, Bilal M, Nichol J, Wu W, Qiu Z, et al., 2021. Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data. Atmos Res 249:105308. DOI: https://doi.org/10.1016/j.atmosres.2020.105308
  99. Ntakos G, Prikaziuk E, ten Den T, Reidsma P, Vilfan N, van der Wal T, et al. 2024. Coupled WOFOST and SCOPE model for remote sensing-based crop growth simulations. Comput Electron Agric 225:109238. DOI: https://doi.org/10.1016/j.compag.2024.109238
  100. O'Shea K, Nash R, 2015. An introduction to convolutional neural networks. arXiv 1511.08458v2.
  101. Ou Y, Yan J, Liang Z, Zhang B, 2024. Hyperspectral imaging combined with deep learning for the early detection of strawberry leaf gray mold disease. Agronomy 14:2694. DOI: https://doi.org/10.3390/agronomy14112694
  102. Padshetty S, Umashetty A, 2024. Agricultural innovation through deep learning: A hybrid CNN-Transformer architecture for crop disease classification. J Spatial Sci 1-32. DOI: https://doi.org/10.1080/14498596.2024.2355225
  103. Panboonyuen T, Charoenphon C, Satirapod C, 2023. MeViT: A medium-resolution vision transformer for semantic segmentation on Landsat satellite imagery for agriculture in Thailand. Remote Sens 15:5124. DOI: https://doi.org/10.3390/rs15215124
  104. Pei H, Sun Y, Huang H, Zhang W, Sheng J, Zhang Z, 2022. Weed detection in maize fields by UAV images based on crop row preprocessing and improved YOLOv4. Agriculture 12:975. DOI: https://doi.org/10.3390/agriculture12070975
  105. Peng Y, Wang A, Liu J, Faheem M, 2021. A comparative study of semantic segmentation models for identification of grape with different varieties. Agriculture 11:997. DOI: https://doi.org/10.3390/agriculture11100997
  106. Qing J, Deng X, Lan Y, Li Z, 2023. GPT-aided diagnosis on agricultural image based on a new light YOLOPC. Comput Electron Agric 213:108168. DOI: https://doi.org/10.1016/j.compag.2023.108168
  107. Raei E, Asanjan A, Nikoo M, Sadegh M, Pourshahabi S, Adamowski J, 2022. A deep learning image segmentation model for agricultural irrigation system classification. Comput Electron Agric 198:106977. DOI: https://doi.org/10.1016/j.compag.2022.106977
  108. Ramos L, Sappa A, 2025. Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery. Sci Rep 15:784. DOI: https://doi.org/10.1038/s41598-024-84795-1
  109. Rasmussen C, Kirk K, Moeslund T, 2022. The challenge of data annotation in deep learning - A case study on whole plant corn silage. Sensors (Basel) 22:1596. DOI: https://doi.org/10.3390/s22041596
  110. Rehman M, Liu J, Nijabat A, Faheem M, Wang W, Zhao S, 2024. Leveraging convolutional neural networks for disease detection in vegetables: A comprehensive review. Agronomy 14:2231. DOI: https://doi.org/10.3390/agronomy14102231
  111. Ronneberger O, Fischer P, Brox T, 2015. U-Net: Convolutional networks for biomedical image segmentation. In: N. Navab, J. Hornegger, W.M. Wells, A.F. Frangi (eds.), Medical image computing and computer-assisted intervention – MICCAI 2015. Cham, Springer; pp. 234-241. DOI: https://doi.org/10.1007/978-3-319-24574-4_28
  112. Ryo M, 2022. Explainable artificial intelligence and interpretable machine learning for agricultural data analysis. Artif Intell Agric 6:257–265. DOI: https://doi.org/10.1016/j.aiia.2022.11.003
  113. Sakka ME, De Pourtales C, Chaari L, Mothe J, 2025. AgriPotential: A novel multi-spectral and multi-temporal remote sensing dataset for agricultural potentials. arXiv 2506.11740. DOI: https://doi.org/10.1109/CBMI66578.2025.11339350
  114. Sani D, Mahato S, Saini S, Agarwal HK, Devshali CC, Anand S, et al., 2024. SICKLE: A multi-sensor satellite imagery dataset annotated with multiple key cropping parameters. arXiv 2312.00069v1. DOI: https://doi.org/10.1109/WACV57701.2024.00589
  115. Shams M, Gamel S, Talaat F, 2024. Enhancing crop recommendation systems with explainable artificial intelligence: A study on agricultural decision-making. Neural Comput Appl 36:5695–5714. DOI: https://doi.org/10.1007/s00521-023-09391-2
  116. Shenoy J, Zhang X, Tao B, Mehrotra S, Yang R, Zhao H, et al., 2024. Self-supervised learning across the spectrum. Remote Sens 16:3470. DOI: https://doi.org/10.3390/rs16183470
  117. Singh BM, Komal C, Victorovich KA, 2020. Crop growth monitoring through Sentinel and Landsat data based NDVI time-series. Comput Opt 44:409-419. DOI: https://doi.org/10.18287/2412-6179-CO-635
  118. Sishodia R, Ray R, Singh S, 2020. Applications of remote sensing in precision agriculture: A review. Remote Sens 12:3136. DOI: https://doi.org/10.3390/rs12193136
  119. Solangi K, Siyal A, Wu Y, Abbasi B, Solangi F, Lakhiar I, et al., 2019. An assessment of the spatial and temporal distribution of soil salinity in combination with field and satellite data: A case study in Sujawal District. Agronomy 9:869. DOI: https://doi.org/10.3390/agronomy9120869
  120. Song XP, Huang W, Hansen MC, Potapov P, 2021. An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping. Sci Remote Sens 3:100018. DOI: https://doi.org/10.1016/j.srs.2021.100018
  121. Su D, Kong H, Qiao Y, Sukkarieh S, 2021. Data augmentation for deep learning based semantic segmentation and crop-weed classification in agricultural robotics. Comput Electron Agric 190:106418. DOI: https://doi.org/10.1016/j.compag.2021.106418
  122. Sui J, Qin Q, Ren H, Sun Y, Zhang T, Wang J, et al., 2018. Winter wheat production estimation based on environmental stress factors from satellite observations. Remote Sens 10:962. DOI: https://doi.org/10.3390/rs10060962
  123. Sun J, Yang S, Gao X, Ou D, Tian Z, Wu J, et al., 2023. MASA-SegNet: A semantic segmentation network for PolSAR images. Remote Sens 15:3662. DOI: https://doi.org/10.3390/rs15143662
  124. Sun X, Wang P, Lu W, Zhu Z, Lu X, He Q, et al., 2023. RingMo: A remote sensing foundation model with masked image modeling. IEEE T Geosci Remote Sens 61:5612822. DOI: https://doi.org/10.1109/TGRS.2022.3194732
  125. Sykas D, Sdraka M, Zografakis D, Papoutsis I, 2022. A Sentinel-2 multiyear, multicountry benchmark dataset for crop classification and segmentation with deep learning. IEEE J Sel Top Appl Earth Obs Remote Sens 15:3323-3339. DOI: https://doi.org/10.1109/JSTARS.2022.3164771
  126. Tao K, Wang A, Shen Y, Lu Z, Peng F, Wei X, 2022. Peach flower density detection based on an improved CNN incorporating attention mechanism and multi-scale feature fusion. Horticulturae 8:904. DOI: https://doi.org/10.3390/horticulturae8100904
  127. Tao L, Zhang H, Jing H, Liu Y, Yan D, Wei G, et al., 2025. Advancements in vision-language models for remote sensing: Datasets, capabilities, and enhancement techniques. Remote Sens 17:162. DOI: https://doi.org/10.3390/rs17010162
  128. Tianxiang Z, Yuanxiu C, Peixian Z, Jiangyun L, 2024. Remotely sensed crop disease monitoring by machine learning algorithms: A review. Unmanned Syst12:161-171. DOI: https://doi.org/10.1142/S2301385024500237
  129. Tong X, Xia G, Lu Q, Shen H, Li S, You S, et al., 2020. Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sens Environ 237:111322. DOI: https://doi.org/10.1016/j.rse.2019.111322
  130. Tsakiridis NL, Diamantopoulos T, Symeonidis AL, Theocharis JB, Iossifides A, Chatzimisios P, et al., 2020. Versatile Internet of Things for agriculture: An eXplainable AI approach. In: I. Maglogiannis, L. Iliadis, E. Pimenidis (eds.), Artificial intelligence applications and innovations. AIAI 2020. Cham, Springer; pp. 180-191. DOI: https://doi.org/10.1007/978-3-030-49186-4_16
  131. Tseng G, Zvonkov I, Nakalembe CL, Kerner H, 2021. CropHarvest: A global dataset for crop-type classification. Proc. 35th Conf. on Neural Information Processing Systems Datasets and Benchmarks Track.
  132. Ullah H, Bais A, 2022. Evaluation of model generalization for growing plants using conditional learning. Artif Intell Agric 6:189–198. DOI: https://doi.org/10.1016/j.aiia.2022.09.006
  133. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al., 2017. Attention is all you need. arXiv 1706.03762v7.
  134. Wan L, Li H, Li C, Wang A, Yang Y, Wang P, 2022. Hyperspectral sensing of plant diseases: principle and methods. Agronomy 12:1451. DOI: https://doi.org/10.3390/agronomy12061451
  135. Wang J, Ding C, Chen S, He C, Luo B, 2020. Semi-supervised remote sensing image semantic segmentation via consistency regularization and average update of pseudo-label. Remote Sens 12:3603. DOI: https://doi.org/10.3390/rs12213603
  136. Wang J, Qi Z, Wang Y, Liu Y, 2025. A lightweight weed detection model for cotton fields based on an improved YOLOv8n. Sci Rep 15:457. DOI: https://doi.org/10.1038/s41598-024-84748-8
  137. Wang Q, Qin W, Liu M, Zhao J, Zhu Q, Yin Y, 2024. Semantic segmentation model-based boundary line recognition method for wheat harvesting. Agriculture 14:1846. DOI: https://doi.org/10.3390/agriculture14101846
  138. Wang R, Ma L, He G, Johnson B, Yan Z, Chang M, et al., 2024. Transformers for remote sensing: A systematic review and analysis. Sensors (Basel) 24:3495. DOI: https://doi.org/10.3390/s24113495
  139. Wang S, Chen W, Xie S, Azzari G, Lobell D, 2020. Weakly supervised deep learning for segmentation of remote sensing imagery. Remote Sens 12:207. DOI: https://doi.org/10.3390/rs12020207
  140. Wang Y, Yang L, Liu X, Yan P, 2024. An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabV3+. Sci Rep 14:9716. DOI: https://doi.org/10.1038/s41598-024-60375-1
  141. Wang Y, Zhang X, Ma G, Du X, Shaheen N, Mao H, 2021. Recognition of weeds at asparagus fields using multi-feature fusion and backpropagation neural network. Int J Agric Biol Eng 14:190-198. DOI: https://doi.org/10.25165/j.ijabe.20211404.6135
  142. Wang Z, Wang J, Yang K, Wang L, Su F, Chen X, 2022. Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with DeeplabV3+. Comput Geosci 158:104969. DOI: https://doi.org/10.1016/j.cageo.2021.104969
  143. Weiss M, Jacob F, Duveiller G, 2020. Remote sensing for agricultural applications: A meta-review. Remote Sens Environ 236:111402. DOI: https://doi.org/10.1016/j.rse.2019.111402
  144. Weng L, Xu Y, Xia M, Zhang Y, Liu J, Xu Y, 2020. Water areas segmentation from remote sensing images using a separable residual SegNet network. ISPRS Int J Geo-Inf 9:256. DOI: https://doi.org/10.3390/ijgi9040256
  145. Weng X, Pang C, Xia G, 2025. Vision-language modeling meets remote sensing: Models, datasets, and perspectives. IEEE Geosci Remote Sens Mag 13:3572702. DOI: https://doi.org/10.1109/MGRS.2025.3572702
  146. Weyler J, Magistri F, Marks E, Chong Y, Sodano M, Roggiolani G, et al., 2024. PhenoBench: A large dataset and benchmarks for semantic image interpretation in the agricultural domain. IEEE T Pattern Anal Mach Intell 46:9583-9594. DOI: https://doi.org/10.1109/TPAMI.2024.3419548
  147. Wu H, Du Z, Zhong D, Wang Y, Tao C, 2025. FSVLM: A vision-language model for remote sensing farmland segmentation. IEEE T Geosci Remote Sens 63:4402813. DOI: https://doi.org/10.1109/TGRS.2025.3532960
  148. Wu J, Pichler D, Marley D, Wilson D, Hovakimyan N, Hobbs J, 2023. Extended agriculture-vision: An extension of a large aerial image dataset for agricultural pattern analysis. arXiv 2303.02460v1.
  149. Wu Z, Gao Y, Li L, Xue J, Li Y, 2019. Semantic segmentation of high-resolution remote sensing images using fully convolutional network with adaptive threshold. Connect Sci 31:169-184. DOI: https://doi.org/10.1080/09540091.2018.1510902
  150. Xiang J, Liu J, Chen D, Xiong Q, Deng C, 2023. CTFuseNet: A multi-scale CNN-Transformer feature fused network for crop type segmentation on UAV remote sensing imagery. Remote Sens 15:1151. DOI: https://doi.org/10.3390/rs15041151
  151. Xiao A, Xuan W, Wang J, Huang J, Tao D, Lu S, et al., 2025. Foundation models for remote sensing and Earth observation: A survey. IEEE Geosci Remote Sens Mag 13:2–29. DOI: https://doi.org/10.1109/MGRS.2025.3576766
  152. Xie E, Wang W, Yu Z, Anandkumar A, Alvarez J, Luo P, 2021. SegFormer: Simple and efficient design for semantic segmentation with transformers. Proc. 35th Annual Conf. on Neural Information Processing Systems; pp. 12077-12090.
  153. Xie Y, Guo Y, Mi Z, Yang Y, Obaidat M, 2023. Edge-assisted real-time instance segmentation for resource-limited IoT devices. IEEE Internet Things J 10:473-485. DOI: https://doi.org/10.1109/JIOT.2022.3199921
  154. Xing J, Sieber R, Kalacska M, 2014. The challenges of image segmentation in big remotely sensed imagery data. Ann GIS 20:233–244. DOI: https://doi.org/10.1080/19475683.2014.938774
  155. Xu M, Sun J, Zhou X, Tang N, Shen J, Wu X, 2021. Research on nondestructive identification of grape varieties based on EEMD-DWT and hyperspectral image. J Food Sci 86:2011-2023. DOI: https://doi.org/10.1111/1750-3841.15715
  156. Xu S, Xu X, Zhu Q, Meng Y, Yang G, Feng H, et al., 2023. Monitoring leaf nitrogen content in rice based on information fusion of multi-sensor imagery from UAV. Precis Agric 24:2327-2349. DOI: https://doi.org/10.1007/s11119-023-10042-8
  157. Xu Z, Zhang W, Zhang T, Yang Z, Li J, 2021. Efficient Transformer for remote sensing image segmentation. Remote Sens 13:3585. DOI: https://doi.org/10.3390/rs13183585
  158. Yang Y, Wang Y, Dong J, Yu B, 2024. A knowledge distillation-based ground feature classification network with multiscale feature fusion in remote-sensing images. IEEE J Sel Top Appl Earth Obs Remote Sens 17:2347-2359. DOI: https://doi.org/10.1109/JSTARS.2023.3339642
  159. Yasir M, Wan J, Liu S, Sheng H, Xu M, Hossain M, 2023. Coupling of deep learning and remote sensing: A comprehensive systematic literature review. Int J Remote Sens 44:157-193. DOI: https://doi.org/10.1080/01431161.2022.2161856
  160. Zhan Y, Xiong Z, Yuan Y, 2025. SkyEyeGPT: Unifying remote sensing vision-language tasks via instruction tuning with large language model. ISPRS J Photogramm Remote Sens 221:64-77. DOI: https://doi.org/10.1016/j.isprsjprs.2025.01.020
  161. Zhang D, Liu Z, Shi X, 2020. Transfer learning on EfficientNet for remote sensing image classification. Proc. 5th Int. Conf. on Mechanical, Control and Computer Engineering (ICMCCE), Harbin; pp. 2255-2258. DOI: https://doi.org/10.1109/ICMCCE51767.2020.00489
  162. Zhang N, Yang G, Pan Y, Yang X, Chen L, Zhao C, 2020. A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades. Remote Sens 12:3188. DOI: https://doi.org/10.3390/rs12193188
  163. Zhang T, Wang W, Wang J, Cai Y, Yang Z, Li J, 2022. Hyper-LGNet: Coupling local and global features for hyperspectral image classification. Remote Sens 14:5251. DOI: https://doi.org/10.3390/rs14205251
  164. Zhang T, Xu Z, Su J, Yang Z, Liu C, Chen W, et al., 2021. Ir-UNet: Irregular segmentation U-shape network for wheat yellow rust detection by UAV multispectral imagery. Remote Sens 13:3892 DOI: https://doi.org/10.3390/rs13193892
  165. Zhang T, Yang Z, Xu Z, Li J, 2022. Wheat yellow rust severity detection by efficient DF-UNet and UAV multispectral imagery. IEEE Sensors J 22:9057-9068. DOI: https://doi.org/10.1109/JSEN.2022.3156097
  166. Zhang X, Zhang S, Meng X, Zhang G, Zang D, Han Y, et al., 2024. Remote sensing image segmentation of gully erosion in a typical black soil area in Northeast China based on improved DeepLabV3+ model. Ecol Inform 84:102929. DOI: https://doi.org/10.1016/j.ecoinf.2024.102929
  167. Zhang Y, Lv C, 2024. TinySegformer: A lightweight visual segmentation model for real-time agricultural pest detection. Comput Electron Agric 218:108740. DOI: https://doi.org/10.1016/j.compag.2024.108740
  168. Zhang Y, Sun J, Li J, Wu X, Dai C, 2018. Quantitative analysis of cadmium content in tomato leaves based on hyperspectral image and feature selection. Appl Eng Agric 34:789-798. DOI: https://doi.org/10.13031/aea.12679
  169. Zhang Z, Lu Y, Zhao Y, Pan Q, Jin K, Xu G, et al., 2023. TS-YOLO: An all-day and lightweight tea canopy shoots detection model. Agronomy 13:1411. DOI: https://doi.org/10.3390/agronomy13051411
  170. Zhang Z, Zhao T, Guo Y, Yin J, 2024. RS5M and GeoRSCLIP: A large-scale vision-language dataset and a large vision-language model for remote sensing. IEEE T Geosci Remote Sens 62:5642123. DOI: https://doi.org/10.1109/TGRS.2024.3449154
  171. Zhao Y, Xie J, Zhu H, Luo T, Xiong Y, Fan C, et al., 2025. Land-Unet: A deep learning network for precise segmentation and identification of non-structured land use types in rural areas for green urban space analysis. Ecol Inform 87:103078. DOI: https://doi.org/10.1016/j.ecoinf.2025.103078
  172. Zhao Y, Zhang X, Sun J, Yu T, Cai Z, Zhang Z, et al., 2024. Low-cost lettuce height measurement based on depth vision and lightweight instance segmentation model. Agriculture 14:1596. DOI: https://doi.org/10.3390/agriculture14091596
  173. Zhao X, Wang L, Zhang Y, Han X, Deveci M, Parmar M, 2024. A review of convolutional neural networks in computer vision. Artif Intell Rev 57:99. DOI: https://doi.org/10.1007/s10462-024-10721-6
  174. Zheng K, Chen Y, Wang J, Liu Z, Bao S, Zhan J, et al., 2025. Enhancing remote sensing semantic segmentation accuracy and efficiency through transformer and knowledge distillation. IEEE J Sel Top Appl Earth Obs Remote Sens 18:4074–4092. DOI: https://doi.org/10.1109/JSTARS.2025.3525634
  175. Zheng Z, Yuan J, Yao W, Yao H, Liu Q, Guo L, 2024. Crop classification from drone imagery based on lightweight semantic segmentation methods. Remote Sens 16:4099. DOI: https://doi.org/10.3390/rs16214099
  176. Zhong B, Wei T, Luo X, Du B, Hu L, Ao K, et al. 2023. Multi-Swin mask transformer for instance segmentation of agricultural field extraction. Remote Sens 15:549. DOI: https://doi.org/10.3390/rs15030549
  177. Zhu H, Lin C, Liu G, Wang D, Qin S, Li A, et al. 2024. Intelligent agriculture: Deep learning in UAV-based remote sensing imagery for crop diseases and pests detection. Front Plant Sci 15:1435016. DOI: https://doi.org/10.3389/fpls.2024.1435016
  178. Zhu H, Qin S, Su M, Lin C, Li A, Gao J, 2024. Harnessing large vision and language models in agriculture: A review. arXiv:2407.19679. DOI: https://doi.org/10.3389/fpls.2025.1579355
  179. Zhu H, Wang D, Wei Y, Zhang X, Li L, 2024. Combining transfer learning and ensemble algorithms for improved citrus leaf disease classification. Agriculture 14:1549. DOI: https://doi.org/10.3390/agriculture14091549
  180. Zhu W, Feng Z, Dai S, Zhang P, Wei X, 2022. Using UAV multispectral remote sensing with appropriate spatial resolution and machine learning to monitor wheat scab. Agriculture 12:1785. DOI: https://doi.org/10.3390/agriculture12111785
  181. Zhu W, Sun J, Wang S, Shen J, Yang K, Zhou X, 2022. Identifying field crop diseases using Transformer-embedded convolutional neural network. Agriculture 12:1083. DOI: https://doi.org/10.3390/agriculture12081083
  182. Zhu X, Chikangaise P, Shi W, Chen W, Yuan S, 2018. Review of intelligent sprinkler irrigation technologies for remote autonomous system. Int J Agric Biol Eng 11:23-30. DOI: https://doi.org/10.25165/j.ijabe.20181101.3557
  183. Zuo Z, Gao S, Peng H, Xue Y, Han L, Ma G, et al., 2024. Lightweight detection of broccoli heads in complex field environments based on LBDC-YOLO. Agronomy 14:2359. DOI: https://doi.org/10.3390/agronomy14102359

Metrics

Citations and online attention from Dimensions, Altmetric and PlumX.

PlumX Metrics

License

Copyright (c) 2025 the Author(s)

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.