mlsanityData to 7 Oct 2026

Using Deep Learning for Image-Based Plant Disease Detection

arXiv 1604.03169 ↗First posted 11 Apr 2016cs.CVcited by 216

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Abstract (arXiv)

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. When testing the model on a set of images collected from trusted online sources - i.e. taken under conditions different from the images used for training - the model still achieves an accuracy of 31.4%. While this accuracy is much higher than the one based on random selection (2.6%), a more diverse set of training data is needed to improve the general accuracy. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path towards smartphone-assisted crop disease diagnosis on a massive global scale.

Key facts. Cited by 216 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Apr 2016.

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Citing papers per year: 2021: 32; 2022: 30; 2023: 37; 2024: 25; 2025: 37; 2026: 55.

216 papers.

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  10. 2022Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficultycited by 37
  11. 2021Revisiting the Transferability of Supervised Pretraining: an MLP Perspectivecited by 36
  12. 2023PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domaincited by 31
38 more
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  13. 2022MangoLeafBD: A Comprehensive Image Dataset to Classify Diseased and Healthy Mango Leavescited by 13
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  25. 2021Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detectioncited by 7
  26. 2022Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Modelscited by 7
  27. 2022The Geometry of Self-supervised Learning Models and its Impact on Transfer Learningcited by 7
  28. 2023Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharingcited by 7
  29. 2024Cross-Domain Few-Shot Learning via Adaptive Transformer Networkscited by 7
  30. 2021Leaf Image-based Plant Disease Identification using Color and Texture Featurescited by 6
  31. 2021Monitoring crop phenology with street-level imagery using computer visioncited by 6
  32. 2022ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learningcited by 6
  33. 2022How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time Augmentationcited by 6
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  37. 2022On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classificationcited by 5
  38. 2022The Power of Transfer Learning in Agricultural Applications: AgriNetcited by 5

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