DELG Net: A Dual-Stream Cross-Attention Framework for Automated Nutrient Deficiency Detection in Mulberry Leaves
Received: 13 January 2026 | Revised: 2 February 2026 | Accepted: 14 February 2026 | Online: 4 April 2026
Corresponding author: S. Raghavendrachar
Abstract
Nutrient deficiencies in mulberry leaves play a decisive role in sericulture, since they directly affect the growth of silkworms and the production of silk. Traditional visual observation by professionals is subjective, laborious, and cannot be used in massive monitoring. Although generic Convolutional Neural Network (CNN) models are frequently applied to this task, they often struggle to capture the fine-grained spatial and textural details essential for identifying specific nutrient stress. To address these drawbacks, this paper presents DELG Net, a biologically-related two-stream convolutional model that conjoins global and regional feature processing. Macroscopic color and contour features are captured by the global stream, and vein and texture patterns are highlighted with high-pass filtering by the local stream. A cross-attention fusion model dynamically increases the contribution of the two streams according to the prominence of the symptoms, and feature weighting can be adjusted adaptively and contextually. Experiments on a curated dataset of mulberry plant leaves show that DELG Net achieves better precision and interpretability than state-of-the-art CNNs, with a total classification accuracy of 95.4% and consistent results in all three classes of nitrogen, potassium, and phosphorus deficiencies. The proposed model can be used to monitor nutrient stress in real-time and on a large scale to increase the efficiency of silk production.
Keywords:
deep learning, cross-attention fusion, dual-stream architecture, plant health monitoring, precision agricultureDownloads
References
E. A. Aldakheel, M. Zakariah, and A. H. Alabdalall, ''Detection and identification of plant leaf diseases using YOLOv4,'' Frontiers in Plant Science, vol. 15, Apr. 2024, Art. no. 1355941. DOI: https://doi.org/10.3389/fpls.2024.1355941
K. Venkatesh and K. J. Naik, ''Nutrient deficiency identification and yield-loss prediction in leaf images of groundnut crop using transfer learning,'' Signal, Image and Video Processing, vol. 18, no. 5, pp. 4553–4568, July 2024. DOI: https://doi.org/10.1007/s11760-024-03094-4
M. H. Bijoy et al., ''Towards Sustainable Agriculture: A Novel Approach for Rice Leaf Disease Detection Using dCNN and Enhanced Dataset,'' IEEE Access, vol. 12, pp. 34174–34191, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3371511
J. Fuentes-Pacheco et al., ''A Curriculum Learning Approach to Classify Nitrogen Concentration in Greenhouse Basil Plants Using a Very Small Dataset and Low-Cost RGB Images,'' IEEE Access, vol. 12, pp. 27411–27425, 2024. DOI: https://doi.org/10.1109/ACCESS.2024.3367614
M. Majdalawieh, S. Khan, and T. Islam, ''Using Deep Learning Model to Identify Iron Chlorosis in Plants,'' IEEE Access, vol. 11, pp. 46949–46955, 2023. DOI: https://doi.org/10.1109/ACCESS.2023.3273607
M. Shoaib et al., ''An advanced deep learning models-based plant disease detection: A review of recent research,'' Frontiers in Plant Science, vol. 14, Mar. 2023, Art. no. 1158933. DOI: https://doi.org/10.3389/fpls.2023.1158933
A. Bera, D. Bhattacharjee, and O. Krejcar, ''PND-Net: plant nutrition deficiency and disease classification using graph convolutional network,'' Scientific Reports, vol. 14, no. 1, July 2024, Art. no. 15537. DOI: https://doi.org/10.1038/s41598-024-66543-7
K. Wei et al., ''Explainable Deep Learning Study for Leaf Disease Classification,'' Agronomy, vol. 12, no. 5, Apr. 2022, Art. no. 1035. DOI: https://doi.org/10.3390/agronomy12051035
H. K. Kondaveeti and C. G. Simhadri, ''Evaluation of deep learning models using explainable AI with qualitative and quantitative analysis for rice leaf disease detection,'' Scientific Reports, vol. 15, no. 1, Aug. 2025, Art. no. 31850. DOI: https://doi.org/10.1038/s41598-025-14306-3
S. Timilsina et al., ''CNDD-Net: A Lightweight Attention-Based Convolutional Neural Network for Classifying Corn Nutritional Deficiencies and Leaf Diseases,'' Electronics, vol. 14, no. 7, Apr. 2025, Art. no. 1482. DOI: https://doi.org/10.3390/electronics14071482
M. A. Hasan et al., ''Mulberry leaf disease detection by CNN-ViT with XAI integration,'' PLOS One, vol. 20, no. 6, June 2025, Art. no. e0325188. DOI: https://doi.org/10.1371/journal.pone.0325188
J. G. A. Barbedo, ''Detection of nutrition deficiencies in plants using proximal images and machine learning: A review,'' Computers and Electronics in Agriculture, vol. 162, pp. 482–492, July 2019. DOI: https://doi.org/10.1016/j.compag.2019.04.035
S. Madhu and V. RaviSankar, ''Comprehensive Analysis of a YOLO-based Deep Learning Model for Cotton Plant Leaf Disease Detection,'' Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19947–19952, Feb. 2025. DOI: https://doi.org/10.48084/etasr.8944
M. F. Taha et al., ''Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants,'' Agronomy, vol. 14, no. 10, Oct. 2024, Art. no. 2290. DOI: https://doi.org/10.3390/agronomy14102290
A. M. Roy, R. Bose, and J. Bhaduri, ''A fast accurate fine-grain object detection model based on YOLOv4 deep neural network.'' arXiv, 2021. DOI: https://doi.org/10.1007/s00521-021-06651-x
T. Ilyas, J. Lee, O. Won, Y. Jeong, and H. Kim, ''Overcoming field variability: unsupervised domain adaptation for enhanced crop-weed recognition in diverse farmlands,'' Frontiers in Plant Science, vol. 14, Aug. 2023, Art. no. 1234616. DOI: https://doi.org/10.3389/fpls.2023.1234616
G. Ignesti, D. Moroni, and M. Martinelli, ''Towards the actual deployment of robust, adaptable, and maintainable AI models for sustainable agriculture,'' presented at the 19th Conference on Computer Science and Intelligence Systems, Belgrade, Serbia, Nov. 2024, pp. 33–39. DOI: https://doi.org/10.15439/2024F2991
N. Paul, G. C. Sunil, D. Horvath, and X. Sun, ''Deep learning for plant stress detection: A comprehensive review of technologies, challenges, and future directions,'' Computers and Electronics in Agriculture, vol. 229, Feb. 2025, Art. no. 109734. DOI: https://doi.org/10.1016/j.compag.2024.109734
J. Yi et al., ''Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images,'' Sensors, vol. 20, no. 20, Oct. 2020, Art. no. 5893. DOI: https://doi.org/10.3390/s20205893
Z. Xu et al., ''Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice,'' Computational Intelligence and Neuroscience, vol. 2020, pp. 1–12, Aug. 2020. DOI: https://doi.org/10.1155/2020/7307252
M. Urfan et al., ''The Deep Learning-Crop Platform (DL-CRoP): For Species-Level Identification and Nutrient Status of Agricultural Crops,'' Research, vol. 7, Jan. 2024, Art. no. 0491. DOI: https://doi.org/10.34133/research.0491
E. Elizar, M. A. Zulkifley, R. Muharar, M. H. M. Zaman, and S. M. Mustaza, ''A Review on Multiscale-Deep-Learning Applications,'' Sensors, vol. 22, no. 19, Sept. 2022, Art. no. 7384. DOI: https://doi.org/10.3390/s22197384
Y. Cheng et al., ''Multi-scale Feature Fusion and Transformer Network for urban green space segmentation from high-resolution remote sensing images,'' International Journal of Applied Earth Observation and Geoinformation, vol. 124, Nov. 2023, Art. no. 103514. DOI: https://doi.org/10.1016/j.jag.2023.103514
H. Lin, R. Tse, S. K. Tang, Z. Qiang, and G. Pau, ''Few-shot learning approach with multi-scale feature fusion and attention for plant disease recognition,'' Frontiers in Plant Science, vol. 13, Sept. 2022, Art. no. 907916. DOI: https://doi.org/10.3389/fpls.2022.907916
G. Zheng, Z. Jiang, X. Zhang, and D. Jiang, ''Multi-scale feature fusion-based semantic segmentation network for agricultural remote sensing images,'' Chemical and Biological Technologies in Agriculture, vol. 12, no. 1, Aug. 2025, Art. no. 126. DOI: https://doi.org/10.1186/s40538-025-00833-8
Z. X. Yang, Y. Li, R. F. Wang, P. Hu, and W. H. Su, ''Deep Learning in Multimodal Fusion for Sustainable Plant Care: A Comprehensive Review,'' Sustainability, vol. 17, no. 12, June 2025, Art. no. 5255. DOI: https://doi.org/10.3390/su17125255
T. Ali et al., ''Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops,'' Scientific Reports, vol. 14, no. 1, Dec. 2024, Art. no. 30062. DOI: https://doi.org/10.1038/s41598-024-74127-8
M. Bodaghi, M. Hosseini, and R. Gottumukkala, ''A Multimodal Intermediate Fusion Network with Manifold Learning for Stress Detection.'' arXiv, 2024. DOI: https://doi.org/10.1109/ICMI60790.2024.10586177
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Copyright (c) 2026 S. Raghavendrachar, Rekha B. Venkatapur, V. Karthik
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