A Deep Learning Model for Detection and Classification of Nutritional Deficiency in Coffee Plant

Year 2025, Volume: 31 Issue: 4, 960 - 980, 30.09.2025

Umarani Chellamanı , Baskaran Kaliaperumal

https://doi.org/10.15832/ankutbd.1568929

Abstract

Coffee is one of the most popular beverages consumed worldwide and is also an important economic driver in agricultural economies. However, nutritional deficiencies in coffee plants have a major effect on the quality and yield of the crop. Detection of these deficiencies early and accurately is critical for effective intervention and management. In this work, we introduce a novel deep-learning framework for detection and classification of nutritional deficiencies in coffee plants. DenseNet-201, AlexNet, and MobileNet-V2 are integrated to extract discriminative features from coffee leaf images, and an attention-based feature fusion mechanism is proposed using squeeze-and-excitation blocks to improve feature representation. A differential evolution algorithm is used to optimize a Kernel extreme learning machine for learning efficiency and generalization to classify the extracted features. A benchmark dataset is used for the evaluation of the proposed model and its performance is assessed against multiple performance metrics such as accuracy, precision, recall, specificity, F1-score, and the Matthews correlation coefficient. The proposed method is compared with existing deep learning models, and it is found that the proposed method outperforms the other models with a classification accuracy of 99.50%, precision of 99.22%, recall of 99.24%, specificity of 0.9947, F1-score of 0.9957, and M correlation coefficient of 0.9908. The model is able to identify nutritional deficiencies accurately, and these results confirm the model’s effectiveness as a practical and scalable solution for precision agriculture and sustainable coffee cultivation.

Keywords

Coffee plant , Nutritional deficiency detection , Deep learning , Differential evolution algorithm , Kernel extreme learning machine , Classification

Ethical Statement

Not Applicable

Project Number

No Funding

References

• Abuhayi B M & Mossa A A (2023). Coffee disease classification using Convolutional Neural Network based on feature concatenation. Informatics in Medicine Unlocked, 39: 101245. doi.org/10.1016/j.imu.2023.101245
• Adelaja O & Pranggono B (2025). Leveraging Deep Learning for Real-Time Coffee Leaf Disease Identification. AgriEngineering 2025, 7, 13. doi.org/10.3390/agriengineering7010013
• Ali A M, Słowik A, Hezam I M & Abdel-Basset M (2024). Sustainable smart system for vegetables plant disease detection: Four vegetable case studies. Computers and Electronics in Agriculture, 227, 109672. doi.org/10.1016/j.compag.2024.109672
• Apraez L S C, Pino A F S, Ossa A, Vasquez C I, Solarte J D, Cabrera E V R, & Ruiz S E (2025). Application of Spectral Imaging and Vegetation Index in Latin American Coffee Production: A Systematic Mapping. Land Degradation & Development, 36(2), 337-349. doi.org/10.1002/ldr.5373
• Baiju B V, Kirupanithi N, Srinivasan S, Kapoor A, Mathivanan S K, & Shah M A (2025). Robust CRW crops leaf disease detection and classification in agriculture using hybrid deep learning models. Plant Methods, 21, 18. doi.org/10.1186/s13007-025-01332-5
• Barman U, Sarma P, Rahman M, Deka V, Lahkar S, Sharma V, & Saikia M J (2024). Vit-SmartAgri: vision transformer and smartphone-based plant disease detection for smart agriculture. Agronomy, 14(2), 327. doi.org/10.3390/agronomy14020327 Belciug S (2022). Learning deep neural networks' architectures using differential evolution. Case study: medical imaging processing. Computers in biology and medicine 146: 105623. doi.org/10.1016/j.compbiomed.2022.105623
• Bera A, Bhattacharjee D & Krejcar O (2024). PND-Net: plant nutrition deficiency and disease classification using graph convolutional network. Scientific Reports, 14(1), 15537. doi.org/10.1038/s41598-024-66543-7
• Bhagat M & Kumar D (2023). Efficient feature selection using BoWs and SURF method for leaf disease identification. Multimedia Tools and Applications 82: 28187–28211. doi.org/10.1007/s11042-023-14625-5
• Bidarakundi P M & Kumar B M (2024). Coffee-Net: Deep Mobile Patch Generation Network for Coffee Leaf Disease Classification. IEEE Sensors Journal. doi.org/10.1109/jsen.2024.3498050
• Chug, A, Bhatia A, Singh A P & Singh D (2023). A novel framework for image-based plant disease detection using hybrid deep learning approach. Soft Computing, 27(18), 13613-13638. doi.org/10.1007/s00500-022-07177-7 de Oliveira Aparecido L E, Lorençone P A, Lorençone J A, Torsoni G B, de Lima R F, Padilha F & de Souza Rolim G (2024). Addressing coffee crop diseases: forecasting Phoma leaf spot with machine learning. Theoretical and Applied Climatology 155(3): 2261-2282. doi.org/10.1007/s00704-023-04739-z
• de Souza Dias J, Sorte L X B, Fambrini F & Saito J H (2025, February). Coffee plant disease detection using JSEG segmentation and near sets clustering. In Fifth Symposium on Pattern Recognition and Applications (SPRA 2024) (Vol. 13540, pp. 40-52). SPIE. doi.org/10.1117/12.3056434
• Demilie W B (2024). Plant disease detection and classification techniques: a comparative study of the performances. Journal of Big Data 11(1): 5. doi.org/10.1186/s40537-023-00863-9
• Dosset A, Dang L M, Alharbi F, Habib S, Alam N, Park H Y & Moon H (2025). Cassava disease detection using a lightweight modified soft attention network. Pest Management Science, 81(2), 607-617. doi.org/10.1002/ps.8456
• Duhan S, Gulia P, Gill N S & Narwal E (2025). RTR_Lite_MobileNetV2: A Lightweight and Efficient Model for Plant Disease Detection and Classification. Current Plant Biology, 100459. doi.org/10.1016/j.cpb.2025.100459
• Faisal M, Leu J S & Darmawan J T (2023). Model selection of hybrid feature fusion for coffee leaf disease classification. IEEE Access, 11: 62281-62291. doi.org/10.1109/access.2023.3286935
• Haridasan A, Thomas J, & Raj E D (2023). Deep learning system for paddy plant disease detection and classification. Environmental monitoring and assessment, 195(1), 120. doi.org/10.1007/s10661-022-10656-x
• Hasan R I, Yusuf S M, Mohd Rahim M S & Alzubaidi L (2023). Automatic clustering and classification of coffee leaf diseases based on an extended kernel density estimation approach. Plants, 12(8), 1603. doi.org/10.3390/plants12081603
• He K, Zhang X, Ren S & Sun J (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). doi.org/10.1109/cvpr.2016.90
• Hitimana E, Sinayobye O J, Ufitinema J C, Mukamugema J, Rwibasira P, Murangira T & Ngabonziza J (2023). An intelligent system-based coffee plant leaf disease recognition using deep learning techniques on Rwandan Arabica dataset. Technologies, 11(5), 116. doi.org/10.3390/technologies11050116
• Iandola F N, Han S, Moskewicz M W, Ashraf K, Dally W J & Keutzer K (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360.
• Idress K A D, Gadalla O A A, Öztekin Y B & Baitu G P (2024). Machine Learning-based for Automatic Detection of Corn-Plant Diseases Using Image Processing. Journal of Agricultural Sciences (Tarim Bilimleri Dergisi), 30(3), 464-476. doi.org/10.15832/ankutbd.1288298
• Karia A J, Ally J S & Leonard S (2025). Enhancing Coffee Leaf Rust Detection Using DenseNet201: A Comprehensive Analysis of the Mbozi and Public Datasets in Songwe, Tanzania: English. African Journal of Empirical Research, 6(1), 171-188. doi.org/10.51867/ajernet.6.1.17
• Karthik R, Alfred J J & Kennedy J J (2023). Inception-based global context attention network for the classification of coffee leaf diseases. Ecological Informatics, 77, 102213. doi.org/10.1016/j.ecoinf.2023.102213
• Krizhevsky A, Sutskever I & Hinton G E (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.. doi.org/10.1145/3065386
• Ma N, Zhang X, Zheng H T & Sun J (2018). Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European conference on computer vision (ECCV) (pp. 116-131).
• Milke E B, Gebiremariam M T & Salau A O (2023). Development of a coffee wilt disease identification model using deep learning. Informatics in Medicine Unlocked, 42, 101344. doi.org/10.1016/j.imu.2023.101344
• Nawaz M, Nazir T, Javed A, Amin S T, Jeribi F & Tahir A (2024). CoffeeNet: A deep learning approach for coffee plant leaves diseases recognition. Expert Systems with Applications, 237, 121481. doi.org/10.1016/j.eswa.2023.121481
• Pham T C, Le C H, Packianather M & Hoang V D (2023, December). Artificial intelligence-based solutions for coffee leaf disease classification. In IOP Conference Series: Earth and Environmental Science (Vol. 1278, No. 1, p. 012004). IOP Publishing. doi.org/10.1088/1755-1315/1278/1/012004
• Pillaca‐Pullo O S, Moreni Lopes A, Bautista‐Cruz N & Estela‐Escalante W (2025). From coffee waste to nutritional gold: bioreactor cultivation of single‐cell protein from Candida sorboxylosa. Journal of Chemical Technology & Biotechnology, 100(2), 360-368. doi.org/10.1002/jctb.7778
• Preethi P, Swathika R, Kaliraj S, Premkumar R & Yogapriya J (2024). Deep Learning–Based Enhanced Optimization for Automated Rice Plant Disease Detection and Classification. Food and Energy Security, 13(5), e70001. doi.org/10.1002/fes3.70001
• Raghavendra B K (2023). An Efficient Approach for Coffee Leaf Disease Classification and Severity Prediction. International Journal of Intelligent Engineering & Systems, 16(5). doi.org/10.22266/ijies2023.1031.59
• Rahman K N, Banik S C, Islam R & Al Fahim A (2025). A real time monitoring system for accurate plant leaves disease detection using deep learning. Crop Design, 4(1), 100092. doi.org/10.1016/j.cropd.2024.100092
• Ramamurthy K, Thekkath R D, Batra S & Chattopadhyay S (2023). A novel deep learning architecture for disease classification in Arabica coffee plants. Concurrency and Computation: Practice and Experience, 35(8), e7625. doi.org/10.1002/cpe.7625
• Rezaei M, Diepeveen D, Laga H, Gupta S, Jones M G & Sohel F (2025). A transformer-based few-shot learning pipeline for barley disease detection from field-collected imagery. Computers and electronics in agriculture, 229, 109751. doi.org/10.1016/j.compag.2024.109751
• Salamai A A (2024). Towards automated, efficient, and interpretable diagnosis coffee leaf disease: A dual-path visual transformer network. Expert Systems with Applications, 255, 124490. doi.org/10.1016/j.eswa.2024.124490 Sandhiya B & Raja S K S (2024). Deep learning and optimized learning machine for brain tumor classification. Biomedical Signal Processing and Control, 89, 105778. doi.org/10.1016/j.bspc.2023.105778
• Sandler M Howard A, Zhu M, Zhmoginov A & Chen L C (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18–22, 2018, 4510–4520 (Computer Vision Foundation/IEEE Computer Society, 2018). doi.org/10.1109/cvpr.2018.00474
• Sankareshwaran S P, Jayaraman G, Muthukumar P & Krishnan A (2023). Optimizing rice plant disease detection with crossover boosted artificial hummingbird algorithm based AX-RetinaNet. Environmental Monitoring and Assessment, 195(9), 1070. doi.org/10.1007/s10661-023-11612-z
• Saygılı A (2023). The efficiency of transfer learning and data augmentation in lemon leaf image classification. European Journal of Engineering and Applied Sciences 6(1): 32-40. doi.org/10.55581/ejeas.1321042
• Shovon M S H, Mozumder S J, Pal O K, Mridha M F, Asai N & Shin J (2023). PlantDet: A robust multi-model ensemble method based on deep learning for plant disease detection. IEEE Access, 11, 34846-34859. doi.org/10.1109/access.2023.3264835
• Shrma V S T & Rahiman M K (2024). A comparison of CNN and SVM algorithms for the prediction of growth defects in coffee plants for stable yield and fungal diseases. Interactions, 245(1), 322. doi.org/10.1007/s10751-024-02135-1 Singh M K & Kumar A (2024). Coffee Leaf Disease Classification by Using a Hybrid Deep Convolution Neural Network. SN Computer Science, 5(5), 618. doi.org/10.1007/s42979-024-02960-9
• Sofuoğlu C İ & Bırant D (2023). Using convolutional neural network for grape plant disease classification. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 28(3), 809-820. doi.org/10.17482/uumfd.1277418
• Sofuoğlu C İ & Bırant D (2024). Potato plant leaf disease detection using deep learning method. Journal of Agricultural Sciences (Tarim Bilimleri Dergisi), 30(1), 153-165. doi.org/10.15832/ankutbd.1276722
• Thakur D, Gera T, Aggarwal A, Verma M, Kaur M, Singh D & Amoon M (2024). SUNet: Coffee Leaf Disease Detection using Hybrid Deep Learning Model. IEEE Access. doi.org/10.1109/access.2024.3476211
• Tsigkou K, Demissie B A, Hashim S, Ghofrani-Isfahani P, Thomas R, Mapinga K F & Angelidaki I (2025). Coffee processing waste: Unlocking opportunities for sustainable development. Renewable and Sustainable Energy Reviews, 210, 115263. doi.org/10.1016/j.rser.2024.115263
• Tuesta-Monteza V A, Mejia-Cabrera H I & Arcila-Diaz J (2023). CoLeaf-DB: Peruvian coffee leaf images dataset for coffee leaf nutritional deficiencies detection and classification. Data in Brief, 48, 109226. doi.org/10.1016/j.dib.2023.109226 Upadhyay A, Chandel N S, Singh K P, Chakraborty S K, Nandede B M, Kumar M & Elbeltagi A (2025). Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture. Artificial Intelligence Review, 58(3), 1-64. doi.org/10.1007/s10462-024-11100-x
• Wang S H & Zhang Y D (2020). DenseNet-201-based deep neural network with composite learning factor and precomputation for multiple sclerosis classification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 16(2s), 1-19. doi.org/10.1145/3341095
• Wang X & Liu J (2025). TomatoGuard-YOLO: a novel efficient tomato disease detection method. Frontiers in Plant Science, 15, 1499278. doi.org/10.3389/fpls.2024.1499278
• Wang Y, Yu X & Zhang W (2025). An improved reinforcement learning-based differential evolution algorithm for combined economic and emission dispatch problems. Engineering Applications of Artificial Intelligence, 140, 109709. doi.org/10.1016/j.engappai.2024.109709
• Yamashita J V Y B & Leite J P R (2023). Coffee disease classification at the edge using deep learning. Smart Agricultural Technology, 4, 100183. doi.org/10.1016/j.atech.2023.100183
• Yazdani M, Kabirifar K & Haghani M (2024). Optimising post-disaster waste collection by a deep learning-enhanced differential evolution approach. Engineering Applications of Artificial Intelligence, 132, 107932. doi.org/10.1016/j.engappai.2024.107932