mlsanityData to 7 Oct 2026

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

arXiv 2507.22101 ↗First posted 29 Jul 2025cs.CVcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 85 of its 352 references that are in the map. First posted on arXiv 29 Jul 2025.

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85 of 352 references.

References · 85

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2023GPT-4 Technical Reportcited by 18,132
  6. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  7. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  8. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  9. 2023Segment Anythingcited by 8,158
  10. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  11. 2020End-to-End Object Detection with Transformerscited by 7,384
  12. 2017Mask R-CNNcited by 7,259
38 more
  1. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  2. 2016Densely Connected Convolutional Networkscited by 6,691
  3. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  5. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  6. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  7. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  8. 2014Going Deeper with Convolutionscited by 4,231
  9. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  10. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
  11. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  12. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  13. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  14. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelscited by 2,689
  15. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Taskscited by 2,534
  16. 2021Learning to Prompt for Vision-Language Modelscited by 2,269
  17. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  18. 2022Visual Prompt Tuningcited by 1,674
  19. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,127
  20. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  21. 2024Grounded SAM: Assembling Open-World Models for Diverse Visual Taskscited by 914
  22. 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 908
  23. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 763
  24. 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 526
  25. 2022ViTPose: Simple Vision Transformer Baselines for Human Pose Estimationcited by 450
  26. 2019GhostNet: More Features from Cheap Operationscited by 410
  27. 2023Segment Anything in High Qualitycited by 373
  28. 2016Using Deep Learning for Image-Based Plant Disease Detectioncited by 216
  29. 2022Patches Are All You Need?cited by 201
  30. 2024MobileVLM V2: Faster and Stronger Baseline for Vision Language Modelcited by 172
  31. 2021UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagerycited by 166
  32. 2023BioCLIP: A Vision Foundation Model for the Tree of Lifecited by 159
  33. 2023DiffusionSat: A Generative Foundation Model for Satellite Imagerycited by 130
  34. 2023SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understandingcited by 111
  35. 2024MobileNetV4 -- Universal Models for the Mobile Ecosystemcited by 98
  36. 2018DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learningcited by 69
  37. 2020A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysiscited by 57
  38. 2022Transferability in Deep Learning: A Surveycited by 57

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