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Deep learning in agriculture: A survey

arXiv 1807.11809 ↗First posted 31 Jul 2018cs.LG cs.CV stat.MLcited by 160

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Abstract (arXiv)

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

Key facts. Cited by 160 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Jul 2018.

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Citing papers per year: 2021: 41; 2022: 27; 2023: 24; 2024: 14; 2025: 30; 2026: 24.

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  5. 2021A Survey of Deep Learning Techniques for Weed Detection from Imagescited by 23
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  7. 2023Label-Efficient Learning in Agriculture: A Comprehensive Reviewcited by 16
  8. 2021Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classificationcited by 15
  9. 2024AD4RL: Autonomous Driving Benchmarks for Offline Reinforcement Learning with Value-based Datasetcited by 15
  10. 2021Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networkscited by 12
  11. 2022A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Taskscited by 12
  12. 2023GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Modelscited by 12
38 more
  1. 2021A Robust Illumination-Invariant Camera System for Agricultural Applicationscited by 11
  2. 2021Context-self contrastive pretraining for crop type semantic segmentationcited by 11
  3. 2022Position-Agnostic Autonomous Navigation in Vineyards with Deep Reinforcement Learningcited by 11
  4. 2025AgriGPT-VL: Agricultural Vision-Language Understanding Suitecited by 11
  5. 2022e-CLIP: Large-Scale Vision-Language Representation Learning in E-commercecited by 10
  6. 2021Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rowscited by 9
  7. 2021A review on vision-based analysis for automatic dietary assessmentcited by 9
  8. 2021A fast accurate fine-grain object detection model based on YOLOv4 deep neural networkcited by 9
  9. 2022GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grainscited by 9
  10. 2023Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Reviewcited by 8
  11. 2025AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Applicationcited by 8
  12. 2021Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detectioncited by 7
  13. 2021Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculturecited by 7
  14. 2022Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMscited by 7
  15. 2021Geometry-Aware Fruit Grasping Estimation for Robotic Harvesting in Orchardscited by 6
  16. 2021Monitoring crop phenology with street-level imagery using computer visioncited by 6
  17. 2022Spatial Monitoring and Insect Behavioural Analysis Using Computer Vision for Precision Pollinationcited by 6
  18. 2022Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Featurescited by 6
  19. 2021IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Controlcited by 5
  20. 2021Vision Transformers For Weeds and Crops Classification Of High Resolution UAV Imagescited by 5
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  22. 2023Forward-Forward Algorithm for Hyperspectral Image Classification: A Preliminary Studycited by 5
  23. 2025A survey of datasets for computer vision in agriculturecited by 5
  24. 2021What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Reviewcited by 4
  25. 2022Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Visioncited by 4
  26. 2022Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clusteringcited by 4
  27. 2023An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classificationcited by 4
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  30. 2024Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentationcited by 4
  31. 2024MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Modelscited by 4
  32. 2025MangoLeafViT: Leveraging Lightweight Vision Transformer with Runtime Augmentation for Efficient Mango Leaf Disease Classificationcited by 4
  33. 2025WeedSense: Multi-Task Learning for Weed Segmentation, Height Estimation, and Growth Stage Classificationcited by 4
  34. 2026Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinementcited by 4
  35. 2021Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Datasetcited by 3
  36. 2021In-field high throughput grapevine phenotyping with a consumer-grade depth cameracited by 3
  37. 2021Intelligent Monitoring of Stress Induced by Water Deficiency in Plants using Deep Learningcited by 3
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