Research Progress of Deep Learning-Based Artificial Intelligence Technology in Pest and Disease Detection and Control
Abstract
1. Introduction
1.1. Research Background and Significance
1.2. Evolution of Agricultural Pest and Disease Detection and Control Technologies
1.2.1. Conventional Approaches to Pest and Disease Detection and Control
1.2.2. Emerging AI-Based Approaches for Pest and Disease Detection and Control
2. Applications of Artificial Intelligence in Agricultural Pest and Disease Identification and Detection
2.1. Deep Learning and Image Recognition Models
2.1.1. Mainstream Deep Learning Architectures
2.1.2. Image Recognition and Detection Technology
2.2. Remote Sensing and UAV Image Analysis Technology
2.3. Internet of Things Sensors and Smart Monitoring Technologies
2.4. Rapid Detection and Mobile Application Technologies
2.5. Multimodal Fusion and Data Analysis Technologies
3. Application of Artificial Intelligence Technologies in Agricultural Pest and Disease Control
3.1. Precision Application and Intelligent Spraying Equipment
3.2. Smart Early-Warning and Pest Control Models
3.3. AI-Assisted Biological Control and Ecological Regulation
3.4. Green Intelligent Control Strategies for Disease-Resistant Varieties
4. Conclusions and Outlook
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
| CNN | convolutional neural network |
| RNN | recurrent neural network |
| CNN-LSTM | convolutional neural network–long short-term memory network model |
| SE-ResNet50 | squeeze-and-excitation residual network 50-layer model |
| SugarcaneGAN | sugarcane generative adversarial network |
| MobileNet-V2 | mobileNet version 2 |
| GNN | graph neural network |
| TRL-GAN | transformer-reinforced learning generative adversarial network |
| DDMA-YOLO | dual-dimensional mixed attention YOLO |
| DTL-SE-ResNet50 | dual transfer learning squeeze-and-excitation residual network 50-layer model |
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| Model Name | Detection Technology | Disease Type | Research Advantage |
|---|---|---|---|
| Convolutional Neural Network [8] | Hyperspectral Imaging | Powdery Mildew | Early warning, low-cost monitoring |
| TARI/TANI + Unsupervised classification + Adaptive thresholding [9] | Hyperspectral imaging | Tea anthracnose | Robust against background noise, effective for automated tea disease detection |
| Deep Convolutional Neural Network [10] | Multispectral images | Rice Diseases | High recognition accuracy |
| CNN + Transformer [11] | Multispectral Images | Pine Wilt Disease | Lightweight implementation for real-time monitoring |
| DATS-ASSFormer [12] | Multispectral Images | Wheat rust, wheat scab, wheat yellow dwarf | Reduces dependency on manual labeling |
| Deep Convolutional Neural Network [13] | RGB images | Anthracnose, Downy Mildew, Powdery Mildew | High computational efficiency |
| Global Pooling Dilated Convolutional Neural Network [14] | RGB Images | Cucumber Leaf Diseases | Improved feature extraction ability and computational performance |
| Recurrent Neural Network [15] | RGB Images | Finger Millet Leaf Diseases | Applicable to small datasets or changing environments |
| Spatial Convolutional Self-attention Transformer [16] | RGB Images | Strawberry Diseases | Enhanced robustness and accuracy under noisy conditions |
| Transformer [17] | RGB Images | Kiwi Diseases | Strong feature extraction ability |
| TRL-GAN [18] | RGB Images | Citrus Greening | Small sample learning, early detection |
| DDMA-YOLO [19] | UAV Remote Sensing | Leaf Blight Disease | Precise positioning, efficient detection |
| Decision tree + MTMF + NDVI [20] | UAV multispectral remote sensing | Wheat powdery mildew, wheat leaf rust | Enables spatiotemporal monitoring of disease progression |
| RustQNet [21] | RGB Images + Hyperspectral Resolution Multispectral | Stripe Rust Disease | Quantitative evaluation, precision spraying |
| Model Name | Detection Technology | Research Object | Research Advantage |
|---|---|---|---|
| Decision Tree–Based Model and Confusion Matrix Method [56] | Microscopic Imaging | Rice Pyricularia Disease | Integration of multiple features to minimize misclassification rate |
| Classification Model [57] | Thermal Infrared Imaging | Tea Anthracnose | Contact-free detection offering rapid response |
| Logistic Regression and Random Forest Classification Model [58] | Diffraction Imaging | Tomato Botrytis cinerea Spores | Sensitivity enhancement via integrated microfluidic enrichment |
| Support Vector Machine Classification Model [59] | Diffraction Imaging | Magnaporthe oryzae Spores | High-specificity detection capability |
| Support Vector Machine Classification Model [60] | Hyperspectral Imaging | Tea White Spot Disease and Anthracnose | Enables non-destructive leaf analysis |
| 3D Convolutional Neural Network [61] | Hyperspectral Imaging | Wheat Stripe Rust | Efficient wide-area monitoring |
| Faster Region-Based Convolutional Neural Network [62] | Visible Light Imaging | Weeds in Cotton Field | Rapid identification and precise weed categorization |
| DTL-SE-ResNet50 [63] | Visible Light Imaging | Tomato Early Blight, Cucumber Downy Mildew, and Pepper Anthracnose | Accurate performance with reduced reliance on large datasets |
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Wu, Y.; Chen, L.; Yang, N.; Sun, Z. Research Progress of Deep Learning-Based Artificial Intelligence Technology in Pest and Disease Detection and Control. Agriculture 2025, 15, 2077. https://doi.org/10.3390/agriculture15192077
Wu Y, Chen L, Yang N, Sun Z. Research Progress of Deep Learning-Based Artificial Intelligence Technology in Pest and Disease Detection and Control. Agriculture. 2025; 15(19):2077. https://doi.org/10.3390/agriculture15192077
Chicago/Turabian StyleWu, Yu, Li Chen, Ning Yang, and Zongbao Sun. 2025. "Research Progress of Deep Learning-Based Artificial Intelligence Technology in Pest and Disease Detection and Control" Agriculture 15, no. 19: 2077. https://doi.org/10.3390/agriculture15192077
APA StyleWu, Y., Chen, L., Yang, N., & Sun, Z. (2025). Research Progress of Deep Learning-Based Artificial Intelligence Technology in Pest and Disease Detection and Control. Agriculture, 15(19), 2077. https://doi.org/10.3390/agriculture15192077

