Artificial Intelligence-Based Crop Disease Identification Technology: Applications, Challenges, and Future Prospects
Abstract
Crop diseases pose a critical threat to global food security. Traditional diagnostic methods are inefficient and fail to meet the demands of modern precision agriculture. In recent years, artificial intelligence (AI) technologies centered on deep learning have revolutionized the rapid and precise identification of crop diseases. This paper systematically outlines key AI techniques for crop disease recognition, including computer vision-based image recognition, multimodal data fusion, and edge computing for field deployment. By analyzing representative domestic and international application cases, this paper highlights the significant advantages of this technology in terms of accuracy and efficiency. Simultaneously, it delves into current technical bottlenecks and deployment barriers, such as the few-shot learning problem, environmental interference, and low farmer trust. The paper concludes by outlining future directions, including self-supervised learning, digital twins, and industry integration, to advance the deep application and implementation of AI technology in smart agriculture.
References
Jameer K, Ramgopal K, Shafi P, 2023, Agricultural Plant Diseases Identification: From Traditional Approach to Deep Learning. Materials Today: Proceedings, 80(P1): 344–356.
Sood V, 2025, Towards Automated Crop Protection: Fusion of Densenet121-Resner50 Model for Disease Detection and Pest Recognition. International Journal of System Assurance Engineering and Management, 2025: 1–17.
Hassan S, Nath K, Jasinski M, et al., 2025, AI-Driven Plant Disease Detection with Tailored Convolutional Neural Network. Network (Bristol, England), 2025: 1–26.
Zeeshan M, Baghini M, Pandey A, 2025, EdgePlantNet: Lightweight Edge-Aware Cyber-Physical System for Plant Disease Detection using Enhanced Attention CNNs. Pervasive and Mobile Computing, 2025(110): 102059–102059.
Katona T, Tóth G, Petró M, et al., 2024, Developing New Fully Connected Layers for Convolutional Neural Networks with Hyperparameter Optimization for Improved Multi-Label Image Classification. Mathematics, 12(6): 806.
Singh C, Wibowo S, Grandhi S, 2025, A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO. Applied Sciences, 15(13): 7075.
Unal Y, 2025, Integrating CBAM and Squeeze-and-Excitation Networks for Accurate Grapevine Leaf Disease Diagnosis. Food Science & Nutrition, 13(6): e70377.
Parag B, Kumar S, 2023, Res4net-CBAM: A Deep CNN with Convolution Block Attention Module for Tea Leaf Disease Diagnosis. Multimedia Tools and Applications, 83(16): 48925–48947.
Xiong S, Wang L, Zhang Y, et al., 2025, Boosting Crop Disease Recognition via Automated Image Description Generation and Multimodal Fusion. Computers and Electronics in Agriculture, 2025(239): 111082.
Zhong T, Iqbal M, Zhang X, 2025, Confidence-Aware Multi-Model Image Classification for Early Disease Detection in Plants. Acta Technologica Agriculturae, 28(3): 159–168.
Yu H, Li X, Yu Y, et al., 2025, A Dual Branch Multimodal Model for Early Detection of Rice Sheath Blight: Fusing Spectral and Physiological Signatures. Computers and Electronics in Agriculture, 2025(231): 110031.
Singh D, Kumar A, 2024, A Deep Recurrent Neural Network for Plant Disease Classification. SN Computer Science, 5(8): 1053.
Kumar B, Rao P, 2025, ViT-CapsNet: Implementing a Smart Agricultural Management Framework using Region ViT-Based Residual Adaptive CapsNet. Knowledge-Based Systems, 2025(330): 114654.
Islam M, Niva M, Chowdhury A, et al., 2025, A Two-Stage Model for Enhanced Mango Leaf Disease Detection using an Innovative Handcrafted Spatial Feature Extraction Method and Knowledge Distillation Process. Ecological Informatics, 2025(91): 103365.
Mourad A, Ibrahim H, Lück S, et al., 2025, Unlocking the Genetic Control of Early Seedling Resistance to Wheat Powdery Mildew through Microphenomics. Pest Management Science.
Liu X, Sui Q, Chen Z, 2025, Real Time Weed Identification with Enhanced Mobilevit Model for Mobile Devices. Scientific Reports, 15(1): 27323.
Yang S, Feng Q, Zhang J, et al., 2024, From Laboratory to Field: Cross-Domain Few-Shot Learning for Crop Disease Identification in the Field. Frontiers in Plant Science, 2024(15): 1434222.
Li J, Yin Z, Li D, et al., 2025, An Efficient Non-Parametric Feature Calibration Method for Few-Shot Plant Disease Classification. Frontiers in Plant Science, 2025(16): 1541982.
Wadhwa D, Malik K, 2024, A Generalizable and Interpretable Model for Early Warning of Pest-Induced Crop Diseases using Environmental Data. Computers and Electronics in Agriculture, 227(P1): 109472.
Lu J, Shi R, Tong J, et al., 2023, Lightweight Method for Plant Disease Identification Using Deep Learning. Intelligent Automation & Soft Computing, 37(1): 525–544.
