pith. sign in

arxiv: 2507.22101 · v2 · submitted 2025-07-29 · 💻 cs.CV

AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock

Umair Nawaz , Muhammad Zaigham Zaheer , Ufaq Khan , Fahad Shahbaz Khan , Hisham Cholakkal , Salman Khan , Rao Muhammad Anwer This is my paper

Pith reviewed 2026-05-19 01:53 UTC · model grok-4.3

classification 💻 cs.CV
keywords AI in agriculturedeep learningcrop disease detectionlivestock health managementaquatic species monitoringvision transformersvision-language modelssurvey
0
0 comments X

The pith

A review of over 200 studies examines deep learning for detecting crop diseases, managing livestock health, and monitoring aquatic species.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This survey compiles findings from more than 200 research papers on artificial intelligence in agriculture. It focuses on conventional machine learning and advanced deep learning methods, including vision transformers and vision-language models like CLIP. The work addresses key tasks in crops, fisheries, and livestock while covering implementation challenges like data variability. Readers would find value in its overview of datasets, metrics, and suggested future research paths for sustainable food production.

Core claim

The paper establishes that a systematic review of more than 200 works reveals the application of machine learning, deep learning techniques such as vision transformers, and recent vision-language foundation models in agriculture for tasks including crop disease detection, livestock health management, and aquatic species monitoring, along with discussions on challenges, datasets, performance metrics, geographical focus, and open research directions.

What carries the argument

The systematic and thorough review of more than 200 research works that covers conventional machine learning approaches, advanced deep learning techniques, and vision-language foundation models applied to agriculture tasks.

If this is right

  • The survey identifies major implementation challenges such as data variability and experimental aspects including datasets and performance metrics.
  • It points toward multimodal data integration as a needed step for better AI performance in agriculture.
  • Efficient edge-device deployment is presented as essential for practical use in farming settings.
  • Domain-adaptable AI models are required to handle diverse farming environments across regions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This overview could help connect AI methods to specific regional challenges in food production that vary by climate and resources.
  • Real-world testing of the reviewed models in new geographical areas might expose performance gaps not detailed in the current selection.
  • Linking these techniques to broader sustainability efforts could show how AI supports long-term resource management beyond the three sectors covered.

Load-bearing premise

The more than 200 selected research works are assumed to offer comprehensive and representative coverage of the field without significant omissions in tasks, regions, or recent advances.

What would settle it

Discovery of a large body of relevant studies on AI applications in crops, fisheries, or livestock that were not included in the survey, or major recent developments overlooked in the review.

Figures

Figures reproduced from arXiv: 2507.22101 by Fahad Shahbaz Khan, Hisham Cholakkal, Muhammad Zaigham Zaheer, Rao Muhammad Anwer, Salman Khan, Ufaq Khan, Umair Nawaz.

Figure 2
Figure 2. Figure 2: Different downstream tasks in crops, fisheries, and livestock [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Taxonomy of this survey. AI applications in [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Tasks in the crop domain include anomaly detection for early [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of the main challenges faced when implementing [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Sample images taken from some of the publicly available datasets used in agriculture. Here, ( [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Illustration of various AI tasks in marine life analysis, including [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Illustration of major challenges such as high infrastructure costs, [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Different tasks used in managing livestock health and behavior, including image classification to identify species or health status, object [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Challenges in the livestock domain include data scarcity, noisy [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗
read the original abstract

Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population. However, these sectors face considerable challenges, including climate variability, resource limitations, and the need for sustainable management. Addressing these issues requires efficient, accurate, and scalable technological solutions, highlighting the importance of artificial intelligence (AI). This survey presents a systematic and thorough review of more than 200 research works covering conventional machine learning approaches, advanced deep learning techniques (e.g., vision transformers), and recent vision-language foundation models (e.g., CLIP) in the agriculture domain, focusing on diverse tasks such as crop disease detection, livestock health management, and aquatic species monitoring. We further cover major implementation challenges such as data variability and experimental aspects: datasets, performance evaluation metrics, and geographical focus. We finish the survey by discussing potential open research directions emphasizing the need for multimodal data integration, efficient edge-device deployment, and domain-adaptable AI models for diverse farming environments. Rapid growth of evolving developments in this field can be actively tracked on our project page: https://github.com/umair1221/AI-in-Agriculture

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript is a survey paper claiming to provide a systematic review of more than 200 research works on AI applications in agriculture. It covers conventional machine learning, deep learning techniques including vision transformers, and recent vision-language models such as CLIP, with focus on tasks like crop disease detection, livestock health management, and aquatic species monitoring. The survey also addresses implementation challenges including data variability, datasets, performance metrics, and geographical focus, and concludes with open research directions such as multimodal integration, edge deployment, and domain-adaptable models.

Significance. If the claimed systematic coverage is verified and representative, the survey would offer a useful synthesis of AI progress across crops, fisheries, and livestock, helping to consolidate knowledge on the shift from CNNs to foundation models while highlighting practical challenges and future needs in sustainable agriculture.

major comments (1)
  1. [Abstract] Abstract: The central claim that the work constitutes a 'systematic and thorough review of more than 200 research works' is load-bearing for the paper's validity, yet the abstract supplies no details on the review methodology (databases queried, keyword strings, date range, inclusion/exclusion criteria, or PRISMA-style selection flow). Without this information it is impossible to evaluate selection bias, coverage gaps across tasks/regions/techniques, or whether recent advances and underrepresented geographies have been omitted.
minor comments (1)
  1. The abstract references a GitHub project page for tracking rapid developments; consider adding a brief description of how this resource relates to the static survey content.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their detailed and constructive feedback on our survey manuscript. We address the major comment point by point below, with a commitment to revisions that improve transparency while preserving the paper's contributions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the work constitutes a 'systematic and thorough review of more than 200 research works' is load-bearing for the paper's validity, yet the abstract supplies no details on the review methodology (databases queried, keyword strings, date range, inclusion/exclusion criteria, or PRISMA-style selection flow). Without this information it is impossible to evaluate selection bias, coverage gaps across tasks/regions/techniques, or whether recent advances and underrepresented geographies have been omitted.

    Authors: We agree that the abstract would be strengthened by briefly summarizing the systematic review methodology to enhance transparency and allow readers to assess potential biases and coverage. The full manuscript contains a dedicated 'Review Methodology' section that details the databases queried (IEEE Xplore, ScienceDirect, Google Scholar, and arXiv), the keyword strings and Boolean combinations used, the date range (primarily 2015–2024 with updates for recent works), the inclusion/exclusion criteria, and a PRISMA-style flow diagram illustrating the selection of over 200 papers. To directly address this concern, we will revise the abstract to include a concise statement on the review protocol and process. This revision will be incorporated in the next version of the manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: literature survey with no derivations or fitted predictions

full rationale

This is a literature review surveying more than 200 existing works on AI applications in agriculture. It contains no mathematical derivations, first-principles results, predictions, or parameter fits that could reduce to inputs by construction. The central claim of systematic coverage is a statement of review scope rather than a derived quantity; no equations, self-citations, uniqueness theorems, or ansatzes are invoked to justify or force any result. The work is self-contained as a descriptive survey without reducing any claim to self-referential parameters or fitted inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a survey paper, the work does not introduce new free parameters, axioms, or invented entities; it relies on the standard assumption that a literature review can usefully synthesize prior publications.

pith-pipeline@v0.9.0 · 5727 in / 1094 out tokens · 21652 ms · 2026-05-19T01:53:01.555245+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

299 extracted references · 299 canonical work pages · 8 internal anchors

  1. [1]

    Transformations in the ways of improving from agriculture 1.0 to 4.0,

    S. Aggarwal and A. Verma, “Transformations in the ways of improving from agriculture 1.0 to 4.0,” in 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2022, pp. 170–174. 1

    work page 2022

  2. [2]

    Agricultural 4.0 leveraging on technological solutions: Study for smart farming sector,

    E. K. Gyamfi, Z. ElSayed, J. Kropczynski, M. A. Yakubu, and N. El- sayed, “Agricultural 4.0 leveraging on technological solutions: Study for smart farming sector,” arXiv preprint arXiv:2401.00814,

    work page arXiv

  3. [3]

    A review of deep learning techniques used in agriculture,

    I. Attri, L. K. Awasthi, T. P . Sharma, and P . Rathee, “A review of deep learning techniques used in agriculture,” Ecological Informatics, p. 102217, 2023. 1

    work page 2023

  4. [4]

    Machine learning applications in agriculture: current trends, challenges, and future perspectives,

    S. O. Ara ´ujo, R. S. Peres, J. C. Ramalho, F. Lidon, and J. Barata, “Machine learning applications in agriculture: current trends, challenges, and future perspectives,” Agronomy, vol. 13, no. 12, p. 2976, 2023. 1

    work page 2023

  5. [5]

    A survey of computer vision technologies in urban and controlled-environment agriculture,

    J. Luo, B. Li, and C. Leung, “A survey of computer vision technologies in urban and controlled-environment agriculture,” ACM Computing Surveys, vol. 56, no. 5, pp. 1–39, 2023. 1

    work page 2023

  6. [6]

    Fish farming techniques: Current situation and trends,

    G. S. Araujo, J. W. A. d. Silva, J. Cotas, and L. Pereira, “Fish farming techniques: Current situation and trends,” Journal of Marine Science and Engineering, vol. 10, no. 11, p. 1598, 2022. 1

    work page 2022

  7. [7]

    You only look once: Unified, real-time object detection,

    J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 779–788. 1, 2, 20

    work page 2016

  8. [8]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778. 1, 12, 13, 20, 21, 22, 29 33

    work page 2016

  9. [9]

    Mask r-cnn,

    K. He, G. Gkioxari, P . Doll´ar, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2961–2969. 1, 2, 14, 20, 22, 29, 31

    work page 2017

  10. [10]

    Going deeper with convolutions,

    C. Szegedy, W. Liu, Y. Jia, P . Sermanet, S. Reed, D. Anguelov, D. Erhan, V . Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1–9. 1, 11, 21

    work page 2015

  11. [11]

    Applications of deep learning in fish habitat monitoring: A tutorial and survey,

    A. Saleh, M. Sheaves, D. Jerry, and M. R. Azghadi, “Applications of deep learning in fish habitat monitoring: A tutorial and survey,” Expert Systems with Applications, vol. 238, p. 121841, 2024. 1

    work page 2024

  12. [12]

    Foundation models defining a new era in vision: A survey and outlook,

    M. Awais, M. Naseer, S. Khan, R. M. Anwer, H. Cholakkal, M. Shah, M.-H. Yang, and F. S. Khan, “Foundation models defining a new era in vision: A survey and outlook,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 47, no. 4, pp. 2245–2264, 2025. 2

    work page 2025

  13. [13]

    Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,

    Z. Chen, J. Wu, W. Wang, W. Su, G. Chen, S. Xing, M. Zhong, Q. Zhang, X. Zhu, L. Lu et al. , “Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024, pp. 24 185–24 198. 2

    work page 2024

  14. [14]

    Bioclip: A vision foundation model for the tree of life,

    S. Stevens, J. Wu, M. J. Thompson, E. G. Campolongo, C. H. Song, D. E. Carlyn, L. Dong, W. M. Dahdul, C. Stewart, T. Berger-Wolf et al., “Bioclip: A vision foundation model for the tree of life,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024, pp. 19 412–19 424. 2, 14

    work page 2024

  15. [15]

    Sam-clip: Merging vision foundation models towards semantic and spatial understanding,

    H. Wang, P . K. A. Vasu, F. Faghri, R. Vemulapalli, M. Farajtabar, S. Mehta, M. Rastegari, O. Tuzel, and H. Pouransari, “Sam-clip: Merging vision foundation models towards semantic and spatial understanding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 3635–3647. 2

    work page 2024

  16. [16]

    A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles,

    D. M. Yousefi, A. M. Rafie, S. Al-Haddad, and S. Azrad, “A systematic literature review on the use of deep learning in precision livestock detection and localization using unmanned aerial vehicles,” Ieee Access, vol. 10, pp. 80 071–80 091, 2022. 2

    work page 2022

  17. [17]

    Faster r-cnn: Towards real- time object detection with region proposal networks,

    S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real- time object detection with region proposal networks,” Advances in neural information processing systems, vol. 28, 2015. 2

    work page 2015

  18. [18]

    A survey on deep learning in uav imagery for precision agriculture and wild flora monitoring: Datasets, models and challenges,

    L. Epifani and A. Caruso, “A survey on deep learning in uav imagery for precision agriculture and wild flora monitoring: Datasets, models and challenges,” Smart Agricultural Technology, p. 100625, 2024. 2

    work page 2024

  19. [19]

    Multi-stage fish classification system using morphometry,

    Y. Kutlu, B. Iscimen, and C. Turan, “Multi-stage fish classification system using morphometry,” Fresenius Environmental Bulletin , vol. 26, no. 3, pp. 1911–1917, 2017. 5

    work page 1911

  20. [20]

    Indigenous fish classification of bangladesh using hybrid features with svm classifier,

    M. A. Islam, M. R. Howlader, U. Habiba, R. H. Faisal, and M. M. Rahman, “Indigenous fish classification of bangladesh using hybrid features with svm classifier,” in 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2). IEEE, 2019, pp. 1–4. 5

    work page 2019

  21. [21]

    Temperate fish detection and classification: a deep learning based approach,

    K. M. Knausg ˚ard, A. Wiklund, T. K. Sørdalen, K. T. Halvorsen, A. R. Kleiven, L. Jiao, and M. Goodwin, “Temperate fish detection and classification: a deep learning based approach,” Applied Intelligence, vol. 52, no. 6, pp. 6988–7001, 2022. 5

    work page 2022

  22. [22]

    Aquayolo: Advanced yolo- based fish detection for optimized aquaculture pond monitoring,

    M. Vijayalakshmi and A. Sasithradevi, “Aquayolo: Advanced yolo- based fish detection for optimized aquaculture pond monitoring,” Scientific Reports, vol. 15, no. 1, p. 6151, 2025. 5

    work page 2025

  23. [23]

    A novel automated approach for fish biomass estimation in turbid environments through deep learning, object detection, and regression,

    S. J. Rani, I. Ioannou, R. Swetha, R. D. Lakshmi, and V . Vassiliou, “A novel automated approach for fish biomass estimation in turbid environments through deep learning, object detection, and regression,” Ecological Informatics, p. 102663, 2024. 5, 16, 17

    work page 2024

  24. [24]

    Fish-tvit: A novel fish species classification method in multi water areas based on transfer learning and vision transformer,

    B. Gong, K. Dai, J. Shao, L. Jing, and Y. Chen, “Fish-tvit: A novel fish species classification method in multi water areas based on transfer learning and vision transformer,” Heliyon, vol. 9, no. 6,

    work page

  25. [25]

    Tfmft: Transformer-based multiple fish tracking,

    W. Li, Y. Liu, W. Wang, Z. Li, and J. Yue, “Tfmft: Transformer-based multiple fish tracking,” Computers and Electronics in Agriculture , vol. 217, p. 108600, 2024. 5, 21

    work page 2024

  26. [26]

    Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application,

    M. B. Yildiz, E. T. Yasin, and M. Koklu, “Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application,” European Food Research and Technology, vol. 250, no. 7, pp. 1919–1932, 2024. 5

    work page 1919

  27. [27]

    Agriclip: Adapting clip for agriculture and livestock via domain-specialized cross-model alignment,

    U. Nawaz, M. Awais, H. Gani, M. Naseer, F. Khan, S. Khan, and R. M. Anwer, “Agriclip: Adapting clip for agriculture and livestock via domain-specialized cross-model alignment,” arXiv preprint arXiv:2410.01407, 2024. 5, 9, 13, 22, 32

    work page arXiv 2024

  28. [28]

    Marineinst: A foundation model for marine image analysis with instance visual description,

    Z. Zheng, Y. Chen, H. Zeng, T.-A. Vu, B.-S. Hua, and S.-K. Yeung, “Marineinst: A foundation model for marine image analysis with instance visual description,” in European Conference on Computer Vision. Springer, 2024, pp. 239–257. 5, 22, 32

    work page 2024

  29. [29]

    Robust fish recognition using foundation models toward automatic fish resource management,

    T. Hasegawa and D. Nakano, “Robust fish recognition using foundation models toward automatic fish resource management,” Journal of Marine Science and Engineering, vol. 12, no. 3, p. 488, 2024. 5, 22

    work page 2024

  30. [30]

    Fmrft: Fusion mamba and detr for query time sequence intersection fish tracking,

    M. Yao, Y. Huo, Q. Tian, J. Zhao, X. Liu, R. Wang, L. Xue, and H. Wang, “Fmrft: Fusion mamba and detr for query time sequence intersection fish tracking,” arXiv preprint arXiv:2409.01148, 2024. 5, 21

    work page arXiv 2024

  31. [31]

    Aquaticclip: A vision-language foundation model for underwater scene analysis,

    B. Alawode, I. I. Ganapathi, S. Javed, N. Werghi, M. Ben- namoun, and A. Mahmood, “Aquaticclip: A vision-language foundation model for underwater scene analysis,” arXiv preprint arXiv:2502.01785, 2025. 5, 22

    work page arXiv 2025

  32. [32]

    Classification of animals using mobilenet with svm classifier,

    M. Sowmya, M. Balasubramanian, and K. Vaidehi, “Classification of animals using mobilenet with svm classifier,” in Computa- tional Methods and Data Engineering: Proceedings of ICCMDE 2021 . Springer, 2022, pp. 347–358. 5, 27

    work page 2021

  33. [33]

    Cattle classifications system using fuzzy k-nearest neighbor classifier,

    H. A. Mahmoud, H. M. El Hadad, F. A. Mousa, and A. E. Hassanien, “Cattle classifications system using fuzzy k-nearest neighbor classifier,” in 2015 International Conference on Informatics, Electronics & Vision (ICIEV). IEEE, 2015, pp. 1–5. 5, 27

    work page 2015

  34. [34]

    A comparison of logistic regression and classification tree to assess brucellosis associated risk factors in dairy cattle,

    A. Megahed, S. Kandeel, D. S. Alshaya, K. A. Attia, M. D. AlKa- htani, F. M. Albohairy, and A. Selim, “A comparison of logistic regression and classification tree to assess brucellosis associated risk factors in dairy cattle,” Preventive Veterinary Medicine, vol. 203, p. 105664, 2022. 5, 28

    work page 2022

  35. [35]

    On-barn cattle facial recognition using deep transfer learning and data augmentation,

    A. Ruchay, V . Kolpakov, H. Guo, and A. Pezzuolo, “On-barn cattle facial recognition using deep transfer learning and data augmentation,” Computers and electronics in agriculture, vol. 225, p. 109306, 2024. 5

    work page 2024

  36. [36]

    Improved resnet- 50 deep learning algorithm for identifying chicken gender,

    D. Wu, Y. Ying, M. Zhou, J. Pan, and D. Cui, “Improved resnet- 50 deep learning algorithm for identifying chicken gender,” Computers and Electronics in Agriculture, vol. 205, p. 107622, 2023. 5, 29

    work page 2023

  37. [37]

    Sift-cnn pipeline in livestock management: A drone image stitching algorithm,

    H. R. Bouchekara, B. O. Sadiq, S. O Zakariyya, Y. A. Sha’aban, M. S. Shahriar, and M. M. Isah, “Sift-cnn pipeline in livestock management: A drone image stitching algorithm,” Drones, vol. 7, no. 1, p. 17, 2022. 5, 29

    work page 2022

  38. [38]

    Cattle segmentation and contour extraction based on mask r-cnn for precision livestock farming,

    Y. Qiao, M. Truman, and S. Sukkarieh, “Cattle segmentation and contour extraction based on mask r-cnn for precision livestock farming,” Computers and Electronics in Agriculture , vol. 165, p. 104958, 2019. 5, 29

    work page 2019

  39. [39]

    Automatic segmentation of cattle rib-eye area in ultrasound images using the unet++ deep neural network,

    M. J. de Melo, D. N. Gon c ¸alves, M. d. N. B. Gomes, G. Faria, J. de Andrade Silva, A. P . M. Ramos, L. P . Osco, M. T. G. Furuya, J. M. Junior, and W. N. Gonc ¸alves, “Automatic segmentation of cattle rib-eye area in ultrasound images using the unet++ deep neural network,” Computers and Electronics in Agriculture, vol. 195, p. 106818, 2022. 5

    work page 2022

  40. [40]

    Detrs beat yolos on real-time object detection,

    Y. Zhao, W. Lv, S. Xu, J. Wei, G. Wang, Q. Dang, Y. Liu, and J. Chen, “Detrs beat yolos on real-time object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2024, pp. 16 965–16 974. 5, 30

    work page 2024

  41. [41]

    Automatic sheep counting by multi-object tracking,

    J. Xu, L. Yu, J. Zhang, and Q. Wu, “Automatic sheep counting by multi-object tracking,” in 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE, 2020, pp. 257–257. 5

    work page 2020

  42. [42]

    Wizard: Unsu- pervised goats tracking algorithm,

    J.-A. Vayssade, X. Godard, and M. Bonneau, “Wizard: Unsu- pervised goats tracking algorithm,” Computers and Electronics in Agriculture, vol. 209, p. 107831, 2023. 5

    work page 2023

  43. [43]

    Animalformer: Multimodal vision framework for behavior-based precision livestock farming,

    A. Qazi, T. Razzaq, and A. Iqbal, “Animalformer: Multimodal vision framework for behavior-based precision livestock farming,”

    work page

  44. [44]

    Available: https://arxiv.org/abs/2406.09711 5, 31

    [Online]. Available: https://arxiv.org/abs/2406.09711 5, 31

    work page arXiv

  45. [45]

    Wildclip: Scene and animal attribute retrieval from camera trap data with domain- adapted vision-language models,

    V . Gabeff, M. Rußwurm, D. Tuia, and A. Mathis, “Wildclip: Scene and animal attribute retrieval from camera trap data with domain- adapted vision-language models,” International Journal of Computer Vision, vol. 132, no. 9, pp. 3770–3786, 2024. 5, 31

    work page 2024

  46. [46]

    From vision to vocabulary: A multimodal approach to detect and track black cattle behaviors,

    S. M. Noe, T. T. Zin, P . Tin, and I. Kobayashi, “From vision to vocabulary: A multimodal approach to detect and track black cattle behaviors,” in International Conference on Genetic and Evolutionary Computing. Springer, 2024, pp. 171–179. 5

    work page 2024

  47. [47]

    Custombottleneck-vggnet: Advanced tomato leaf disease identi- fication for sustainable agriculture,

    M. Zarboubi, A. Bellout, S. Chabaa, and A. Dliou, “Custombottleneck-vggnet: Advanced tomato leaf disease identi- fication for sustainable agriculture,” Computers and Electronics in Agriculture, vol. 232, p. 110066, 2025. 5

    work page 2025

  48. [48]

    Leafconvnext: Enhancing 34 plant disease classification for the future of unmanned farming,

    F. Lu, H. Shangguan, Y. Yuan, Z. Yan, T. Yuan, Y. Yang, H. Wang, W. Xie, G. Zhang, Z. Wang et al. , “Leafconvnext: Enhancing 34 plant disease classification for the future of unmanned farming,” Computers and Electronics in Agriculture, vol. 233, p. 110165, 2025. 5

    work page 2025

  49. [49]

    Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields,

    X. Ma, X. Deng, L. Qi, Y. Jiang, H. Li, Y. Wang, and X. Xing, “Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields,” PloS one , vol. 14, no. 4, p. e0215676, 2019. 5

    work page 2019

  50. [50]

    Computer-vision classification of corn seed varieties using deep convolutional neural network,

    S. Javanmardi, S.-H. M. Ashtiani, F. J. Verbeek, and A. Martynenko, “Computer-vision classification of corn seed varieties using deep convolutional neural network,” Journal of Stored Products Research, vol. 92, p. 101800, 2021. 5

    work page 2021

  51. [51]

    Real-time recognition system of soybean seed full-surface defects based on deep learning,

    G. Zhao, L. Quan, H. Li, H. Feng, S. Li, S. Zhang, and R. Liu, “Real-time recognition system of soybean seed full-surface defects based on deep learning,” Computers and Electronics in Agriculture, vol. 187, p. 106230, 2021. 5

    work page 2021

  52. [52]

    Effective plant disease diagnosis using vision transformer trained with leafy-generative adversarial network-generated images,

    A. K. Singh, A. Rao, P . Chattopadhyay, R. Maurya, and L. Singh, “Effective plant disease diagnosis using vision transformer trained with leafy-generative adversarial network-generated images,” Expert Systems with Applications, vol. 254, p. 124387, 2024. 5

    work page 2024

  53. [53]

    Visual intelligence in precision agriculture: Exploring plant disease detection via efficient vision transformers,

    S. Parez, N. Dilshad, N. S. Alghamdi, T. M. Alanazi, and J. W. Lee, “Visual intelligence in precision agriculture: Exploring plant disease detection via efficient vision transformers,” Sensors, vol. 23, no. 15, p. 6949, 2023. 5

    work page 2023

  54. [54]

    Insect-yolo: A new method of crop insect detection,

    N. Wang, S. Fu, Q. Rao, G. Zhang, and M. Ding, “Insect-yolo: A new method of crop insect detection,” Computers and Electronics in Agriculture, vol. 232, p. 110085, 2025. 5

    work page 2025

  55. [55]

    Potato disease detection and prevention using multimodal ai and large language model,

    H. Zhu, W. Shi, X. Guo, S. Lyu, R. Yang, and Z. Han, “Potato disease detection and prevention using multimodal ai and large language model,” Computers and Electronics in Agriculture, vol. 229, p. 109824, 2025. 5

    work page 2025

  56. [56]

    Lvf: A language and vision fusion framework for tomato diseases segmentation,

    Y. Hu, J. Zhu, G. Zhou, M. He, M. Lv, J. Wang, A. Chen, J. Deng, and Y. Jiang, “Lvf: A language and vision fusion framework for tomato diseases segmentation,” Computers and Electronics in Agriculture, vol. 227, p. 109484, 2024. 5

    work page 2024

  57. [57]

    Visual large language model for wheat disease diagnosis in the wild,

    K. Zhang, L. Ma, B. Cui, X. Li, B. Zhang, and N. Xie, “Visual large language model for wheat disease diagnosis in the wild,” Computers and Electronics in Agriculture, vol. 227, p. 109587, 2024. 5

    work page 2024

  58. [58]

    Leaf disease identification and classification using optimized deep learning,

    Y. M. Abd Algani, O. J. M. Caro, L. M. R. Bravo, C. Kaur, M. S. Al Ansari, and B. K. Bala, “Leaf disease identification and classification using optimized deep learning,” Measurement: Sensors, vol. 25, p. 100643, 2023. 4

    work page 2023

  59. [59]

    Progress in the application of cnn-based image classification and recognition in whole crop growth cycles,

    F. Yu, Q. Zhang, J. Xiao, Y. Ma, M. Wang, R. Luan, X. Liu, Y. Ping, Y. Nie, Z. Tao et al. , “Progress in the application of cnn-based image classification and recognition in whole crop growth cycles,” Remote Sensing, vol. 15, no. 12, p. 2988, 2023. 4

    work page 2023

  60. [60]

    Flowerphenonet: Automated flower detection from multi-view image sequences using deep neural networks for temporal plant phenotyping analysis,

    S. Das Choudhury, S. Guha, A. Das, A. K. Das, A. Samal, and T. Awada, “Flowerphenonet: Automated flower detection from multi-view image sequences using deep neural networks for temporal plant phenotyping analysis,” Remote Sensing, vol. 14, no. 24, p. 6252, 2022. 4

    work page 2022

  61. [61]

    Recognition of sunflower growth period based on deep learning from uav remote sensing images,

    Z. Song, P . Wang, Z. Zhang, S. Yang, and J. Ning, “Recognition of sunflower growth period based on deep learning from uav remote sensing images,” Precision Agriculture, vol. 24, no. 4, pp. 1417–1438, 2023. 4

    work page 2023

  62. [62]

    Defect detection in fruit and vegetables by using machine vision systems and image processing,

    M. Soltani Firouz and H. Sardari, “Defect detection in fruit and vegetables by using machine vision systems and image processing,” Food Engineering Reviews, vol. 14, no. 3, pp. 353–379,

    work page

  63. [63]

    Review of weed detection methods based on computer vision,

    Z. Wu, Y. Chen, B. Zhao, X. Kang, and Y. Ding, “Review of weed detection methods based on computer vision,” Sensors, vol. 21, no. 11, p. 3647, 2021. 4

    work page 2021

  64. [64]

    Early weed detection using image processing and machine learning techniques in an australian chilli farm,

    N. Islam, M. M. Rashid, S. Wibowo, C.-Y. Xu, A. Morshed, S. A. Wasimi, S. Moore, and S. M. Rahman, “Early weed detection using image processing and machine learning techniques in an australian chilli farm,” Agriculture, vol. 11, no. 5, p. 387, 2021. 4

    work page 2021

  65. [65]

    Advanced drone-based weed detection us- ing feature-enriched deep learning approach,

    M. U. Rehman, H. Eesaar, Z. Abbas, L. Seneviratne, I. Hussain, and K. T. Chong, “Advanced drone-based weed detection us- ing feature-enriched deep learning approach,” Knowledge-Based Systems, vol. 305, p. 112655, 2024. 4

    work page 2024

  66. [66]

    A relational approach to pesticide use: Farmers, herbicides, nutsedge, and the weedy path to pesticide use reduction objectives,

    L. Arg¨uelles and H. March, “A relational approach to pesticide use: Farmers, herbicides, nutsedge, and the weedy path to pesticide use reduction objectives,” Journal of Rural Studies , vol. 101, p. 103046, 2023. 4

    work page 2023

  67. [67]

    Automated estimation of crop yield using artificial intelligence and remote sensing technologies,

    Q. M. Ilyas, M. Ahmad, and A. Mehmood, “Automated estimation of crop yield using artificial intelligence and remote sensing technologies,” Bioengineering, vol. 10, no. 2, p. 125, 2023. 4

    work page 2023

  68. [68]

    Recognition of bloom/yield in crop images using deep learning models for smart agriculture: A review,

    B. Darwin, P . Dharmaraj, S. Prince, D. E. Popescu, and D. J. Hemanth, “Recognition of bloom/yield in crop images using deep learning models for smart agriculture: A review,” Agronomy, vol. 11, no. 4, p. 646, 2021. 4

    work page 2021

  69. [69]

    A smartphone-based application for scale pest detection using multiple-object detection methods,

    J.-W. Chen, W.-J. Lin, H.-J. Cheng, C.-L. Hung, C.-Y. Lin, and S.-P . Chen, “A smartphone-based application for scale pest detection using multiple-object detection methods,” Electronics, vol. 10, no. 4, p. 372, 2021. 4

    work page 2021

  70. [70]

    Pesticide spraying robot for precision agriculture: A categorical literature review and future trends,

    A. T. Meshram, A. V . Vanalkar, K. B. Kalambe, and A. M. Badar, “Pesticide spraying robot for precision agriculture: A categorical literature review and future trends,” Journal of Field Robotics , vol. 39, no. 2, pp. 153–171, 2022. 4

    work page 2022

  71. [71]

    Deep learning-based weed–crop recog- nition for smart agricultural equipment: A review,

    H.-R. Qu and W.-H. Su, “Deep learning-based weed–crop recog- nition for smart agricultural equipment: A review,” Agronomy, vol. 14, no. 2, p. 363, 2024. 4

    work page 2024

  72. [72]

    Towards the character- ization of crop and weeds at leaf scale: A large comparison of shape, spatial and textural features,

    J.-A. Vayssade, G. Jones, and J.-N. Paoli, “Towards the character- ization of crop and weeds at leaf scale: A large comparison of shape, spatial and textural features,” Smart Agricultural Technology, vol. 5, p. 100245, 2023. 4

    work page 2023

  73. [73]

    Comparison of aerial and ground 3d point clouds for canopy size assessment in precision viticulture,

    A. Pagliai, M. Ammoniaci, D. Sarri, R. Lisci, R. Perria, M. Vieri, M. E. M. D’Arcangelo, P . Storchi, and S.-P . Kartsiotis, “Comparison of aerial and ground 3d point clouds for canopy size assessment in precision viticulture,” Remote Sensing, vol. 14, no. 5, p. 1145,

    work page

  74. [74]

    Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model,

    S. Islam, M. N. Reza, S. Ahmed, Samsuzzaman, K.-H. Lee, Y. J. Cho, D. H. Noh, and S.-O. Chung, “Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model,” Agriculture, vol. 14, no. 8, p. 1390, 2024. 5

    work page 2024

  75. [75]

    Using deep convolutional neural network for image-based diagnosis of nutrient deficiencies in plants grown in aquaponics,

    M. F. Taha, A. Abdalla, G. ElMasry, M. Gouda, L. Zhou, N. Zhao, N. Liang, Z. Niu, A. Hassanein, S. Al-Rejaie et al. , “Using deep convolutional neural network for image-based diagnosis of nutrient deficiencies in plants grown in aquaponics,”Chemosensors, vol. 10, no. 2, p. 45, 2022. 5

    work page 2022

  76. [76]

    Plant disease detection by imaging sensors– parallels and specific demands for precision agriculture and plant phenotyping,

    A.-K. Mahlein, “Plant disease detection by imaging sensors– parallels and specific demands for precision agriculture and plant phenotyping,” Plant disease, vol. 100, no. 2, pp. 241–251, 2016. 5

    work page 2016

  77. [77]

    Soil sensors and plant wearables for smart and precision agriculture,

    H. Yin, Y. Cao, B. Marelli, X. Zeng, A. J. Mason, and C. Cao, “Soil sensors and plant wearables for smart and precision agriculture,” Advanced Materials, vol. 33, no. 20, p. 2007764, 2021. 5

    work page 2021

  78. [78]

    Machine learning for pest and disease detection in crops,

    D. V . R. T. Amulothu, R. R. Rodge, W. Hasan, and S. Gupta, “Machine learning for pest and disease detection in crops,” in Agriculture 4.0. CRC Press, 2024, pp. 111–132. 6

    work page 2024

  79. [79]

    Anomaly detection on data streams for smart agriculture,

    J. C. Moso, S. Cormier, C. de Runz, H. Fouchal, and J. M. Wan- deto, “Anomaly detection on data streams for smart agriculture,” Agriculture, vol. 11, no. 11, p. 1083, 2021. 6

    work page 2021

  80. [80]

    Understanding the potential applications of artificial intelligence in agriculture sector,

    M. Javaid, A. Haleem, I. H. Khan, and R. Suman, “Understanding the potential applications of artificial intelligence in agriculture sector,” Advanced Agrochem, vol. 2, no. 1, pp. 15–30, 2023. 6

    work page 2023

Showing first 80 references.