Jung, M. et al. Construction of deep learning-based disease detection model in plants. Sci. Rep. 13, 7331 (2023).
Google Scholar
Aiswarya, J., Mariammal, K. & Veerappan, K. Plant nutrient deficiency detection and classification-a review. In 2023 5th International Conference Inventive Research in Computing Applications (ICIRCA). 796–802 (IEEE, 2023).
Yan, Q., Lin, X., Gong, W., Wu, C. & Chen, Y. Nutrient deficiency diagnosis of plants based on transfer learning and lightweight convolutional neural networks Mobilenetv3-large. In Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition. 26–33 (2022).
Sudhakar, M. & Priya, R. Computer vision based machine learning and deep learning approaches for identification of nutrient deficiency in crops: A survey. Nat. Environ. Pollut. Technol. 22 (2023).
Noon, S. K., Amjad, M., Qureshi, M. A. & Mannan, A. Use of deep learning techniques for identification of plant leaf stresses: A review. Sustain. Comput. Inform. Syst. 28, 100443 (2020).
Waheed, H. et al. Deep learning based disease, pest pattern and nutritional deficiency detection system for “Zingiberaceae’’ crop. Agriculture 12, 742 (2022).
Google Scholar
Barbedo, J. G. A. Detection of nutrition deficiencies in plants using proximal images and machine learning: A review. Comput. Electron. Agric. 162, 482–492 (2019).
Google Scholar
Shadrach, F. D., Kandasamy, G., Neelakandan, S. & Lingaiah, T. B. Optimal transfer learning based nutrient deficiency classification model in ridge gourd (Luffa acutangula). Sci. Rep. 13, 14108 (2023).
Google Scholar
Sathyavani, R., JaganMohan, K. & Kalaavathi, B. Classification of nutrient deficiencies in rice crop using DenseNet-BC. Mater. Today Proc. 56, 1783–1789 (2022).
Google Scholar
Haris, S., Sai, K. S., Rani, N. S. et al. Nutrient deficiency detection in mobile captured guava plants using light weight deep convolutional neural networks. In 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC). 1190–1193 (IEEE, 2023).
Munir, S., Seminar, K. B., Sukoco, H. et al. The application of smart and precision agriculture (SPA) for measuring leaf nitrogen content of oil palm in peat soil areas. In 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE). 650–655 (IEEE, 2023).
Lu, J., Peng, K., Wang, Q. & Sun, C. Lettuce plant trace-element-deficiency symptom identification via machine vision methods. Agriculture 13, 1614 (2023).
Google Scholar
Omer, S. M., Ghafoor, K. Z. & Askar, S. K. Lightweight improved YOLOv5 model for cucumber leaf disease and pest detection based on deep learning. In Signal, Image and Video Processing. 1–14 (2023).
Kumar, A. & Bhowmik, B. Automated rice leaf disease diagnosis using CNNs. In 2023 IEEE Region 10 Symposium (TENSYMP). 1–6 (IEEE, 2023).
Senjaliya, H. et al. A comparative study on the modern deep learning architectures for predicting nutritional deficiency in rice plants. In 2023 IEEE IAS Global Conference on Emerging Technologies (GlobConET). 1–6 (IEEE, 2023).
Ennaji, O., Vergutz, L. & El Allali, A. Machine learning in nutrient management: A review. Artif. Intell. Agric. (2023).
Rathnayake, D., Kumarasinghe, K., Rajapaksha, R. & Katuwawala, N. Green insight: A novel approach to detecting and classifying macro nutrient deficiencies in paddy leaves. In 2023 8th International Conference Information Technology Research (ICITR). 1–6 (IEEE, 2023).
Asaari, M. S. M., Shamsudin, S. & Wen, L. J. Detection of plant stress condition with deep learning based detection models. In 2023 International Conference on Energy, Power, Environment, Control, and Computing (ICEPECC). 1–5 (IEEE, 2023).
Tavanapong, W. et al. Artificial intelligence for colonoscopy: Past, present, and future. IEEE J. Biomed. Health Inform. 26, 3950–3965 (2022).
Google Scholar
Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (2017).
Zhang, S., Tong, H., Xu, J. & Maciejewski, R. Graph convolutional networks: A comprehensive review. Comput. Soc. Netw. 6, 1–23 (2019).
Google Scholar
Bera, A., Wharton, Z., Liu, Y., Bessis, N. & Behera, A. SR-GNN: Spatial relation-aware graph neural network for fine-grained image categorization. IEEE Trans. Image Process. 31, 6017–6031 (2022).
Google Scholar
Qu, Z., Yao, T., Liu, X. & Wang, G. A graph convolutional network based on univariate neurodegeneration biomarker for Alzheimer’s disease diagnosis. IEEE J. Transl. Eng. Health Med. (2023).
Khlifi, M. K., Boulila, W. & Farah, I. R. Graph-based deep learning techniques for remote sensing applications: Techniques, taxonomy, and applications—A comprehensive review. Comput. Sci. Rev. 50, 100596 (2023).
Google Scholar
Sunitha, P., Uma, B., Channakeshava, S. & Babu, S. A fully labelled image dataset of banana leaves deficient in nutrients. Data Brief 48, 109155 (2023).
Google Scholar
Tuesta-Monteza, V. A., Mejia-Cabrera, H. I. & Arcila-Diaz, J. CoLeaf-DB: Peruvian coffee leaf images dataset for coffee leaf nutritional deficiencies detection and classification. Data Brief 48, 109226 (2023).
Google Scholar
Chungcharoen, T. et al. Machine learning-based prediction of nutritional status in oil palm leaves using proximal multispectral images. Comput. Electron. Agric. 198, 107019 (2022).
Google Scholar
Bhavya, T., Seggam, R. & Jatoth, R. K. Fertilizer recommendation for rice crop based on NPK nutrient deficiency using deep neural networks and random forest algorithm. In 2023 3rd International Conference on Artificial Intelligence and Signal Processing (AISP). 1–5 (IEEE, 2023).
Dey, B., Haque, M. M. U., Khatun, R. & Ahmed, R. Comparative performance of four CNN-based deep learning variants in detecting Hispa pest, two fungal diseases, and npk deficiency symptoms of rice (Oryza sativa). Comput. Electron. Agric. 202, 107340 (2022).
Google Scholar
Cevallos, C., Ponce, H., Moya-Albor, E. & Brieva, J. Vision-based analysis on leaves of tomato crops for classifying nutrient deficiency using convolutional neural networks. In 2020 International Joint Conference on Neural Networks (IJCNN). 1–7 (IEEE, 2020).
Espejo-Garcia, B., Malounas, I., Mylonas, N., Kasimati, A. & Fountas, S. Using Efficientnet and transfer learning for image-based diagnosis of nutrient deficiencies. Comput. Electron. Agric. 196, 106868 (2022).
Google Scholar
Wang, C., Ye, Y., Tian, Y. & Yu, Z. Classification of nutrient deficiency in rice based on cnn model with reinforcement learning augmentation. In 2021 International Symposium on Artificial Intelligence and its Application on Media (ISAIAM). 107–111 (IEEE, 2021).
Bahtiar, A. R., Santoso, A. J., Juhariah, J. et al. Deep learning detected nutrient deficiency in chili plant. In 2020 8th International Conference on Information and Communication Technology (ICoICT). 1–4 (IEEE, 2020).
Rahadiyan, D., Hartati, S., Nugroho, A. P. et al. Feature aggregation for nutrient deficiency identification in chili based on machine learning. Artif. Intell. Agric. (2023).
Aishwarya, M. & Reddy, P. Ensemble of CNN models for classification of groundnut plant leaf disease detection. Smart Agric. Technol. 100362 (2023).
Nadafzadeh, M. et al. Design, fabrication and evaluation of a robot for plant nutrient monitoring in greenhouse (case study: iron nutrient in spinach). Comput. Electron. Agric. 217, 108579 (2024).
Google Scholar
Desiderio, J. M. H., Tenorio, A. J. F. & Manlises, C. O. Health classification system of romaine lettuce plants in hydroponic setup using convolutional neural networks (CNN). In 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). 1–6 (IEEE, 2022).
Costa, L., Kunwar, S., Ampatzidis, Y. & Albrecht, U. Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning. Precis. Agric. 1–22 (2022).
Lanjewar, M. G. & Parab, J. S. CNN and transfer learning methods with augmentation for citrus leaf diseases detection using PaaS cloud on mobile. Multimed. Tools Appl. 1–26 (2023).
Lanjewar, M. G., Morajkar, P. P. Modified transfer learning frameworks to identify potato leaf diseases. Multimed. Tools Appl. 1–23 (2023).
Dissanayake, A. et al. Detection of diseases and nutrition in bell pepper. In 2023 5th International Conference on Advancements in Computing (ICAC). 286–291 (IEEE, 2023).
Wu, Z., Jiang, F. & Cao, R. Research on recognition method of leaf diseases of woody fruit plants based on transfer learning. Sci. Rep. 12, 15385 (2022).
Google Scholar
Liu, H., Lv, H., Li, J., Liu, Y. & Deng, L. Research on maize disease identification methods in complex environments based on cascade networks and two-stage transfer learning. Sci. Rep. 12, 18914 (2022).
Google Scholar
Kukreja, V., Sharma, R., Vats, S. & Manwal, M. DeepLeaf: Revolutionizing rice disease detection and classification using convolutional neural networks and random forest hybrid model. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). 1–6 (IEEE, 2023).
Bezabih, Y. A., Salau, A. O., Abuhayi, B. M., Mussa, A. A. & Ayalew, A. M. CPD-CCNN: Classification of pepper disease using a concatenation of convolutional neural network models. Sci. Rep. 13, 15581 (2023).
Google Scholar
Kini, A. S., Prema, K. & Pai, S. N. Early stage black pepper leaf disease prediction based on transfer learning using convnets. Sci. Rep. 14, 1404 (2024).
Google Scholar
Wu, Q. et al. A classification method for soybean leaf diseases based on an improved convnext model. Sci. Rep. 13, 19141 (2023).
Google Scholar
Ma, X., Chen, W. & Xu, Y. ERCP-Net: A channel extension residual structure and adaptive channel attention mechanism for plant leaf disease classification network. Sci. Rep. 14, 4221 (2024).
Google Scholar
Babatunde, R. S. et al. A novel smartphone application for early detection of habanero disease. Sci. Rep. 14, 1423 (2024).
Google Scholar
Nagasubramanian, G. et al. Ensemble classification and IoT-based pattern recognition for crop disease monitoring system. IEEE Internet Things J. 8, 12847–12854 (2021).
Google Scholar
Nachtigall, L. G., Araujo, R. M. & Nachtigall, G. R. Classification of apple tree disorders using convolutional neural networks. In 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). 472–476 (IEEE, 2016).
Borhani, Y., Khoramdel, J. & Najafi, E. A deep learning based approach for automated plant disease classification using vision transformer. Sci. Rep. 12, 11554 (2022).
Google Scholar
Aishwarya, M. & Reddy, A. P. Dataset of groundnut plant leaf images for classification and detection. Data Brief 48, 109185 (2023).
Google Scholar
Shi, J. et al. Cervical cell classification with graph convolutional network. Comput. Methods Prog. Biomed. 198, 105807 (2021).
Google Scholar
Fahad, N. M., Azam, S., Montaha, S. & Mukta, M. S. H. Enhancing cervical cancer diagnosis with graph convolution network: AI-powered segmentation, feature analysis, and classification for early detection. Multimed. Tools Appl. 1–25 (2024).
Lanjewar, M. G., Panchbhai, K. G. & Patle, L. B. Fusion of transfer learning models with LSTM for detection of breast cancer using ultrasound images. Comput. Biol. Med. 169, 107914 (2024).
Google Scholar
He, K., Zhang, X., Ren, S. & Sun, J. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1904–1916 (2015).
Google Scholar
Szegedy, C. et al. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1–9 (2015).
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2818–2826 (2016).
Chollet, F. Xception: Deep learning with depthwise separable convolutions. In IEEE Conference on Computer Vision Pattern Recognition. 1251–1258 (2017).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition. 770–778 (2016).
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.-C. MobileNetv2: Inverted residuals and linear bottlenecks. In Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition. 4510–4520 (2018).
Bera, A., Nasipuri, M., Krejcar, O. & Bhattacharjee, D. Fine-grained sports, yoga, and dance postures recognition: A benchmark analysis. IEEE Trans. Instrum. Meas. 72, 1–13 (2023).
Google Scholar
Bera, A., Wharton, Z., Liu, Y., Bessis, N. & Behera, A. Attend and guide (AG-Net): A keypoints-driven attention-based deep network for image recognition. IEEE Trans. Image Process. 30, 3691–3704 (2021).
Google Scholar
Singh, D. et al. PlantDoc: A dataset for visual plant disease detection. In Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. 249–253 (ACM, 2020).
Hameed, Z., Garcia-Zapirain, B., Aguirre, J. J. & Isaza-Ruget, M. A. Multiclass classification of breast cancer histopathology images using multilevel features of deep convolutional neural network. Sci. Rep. 12, 15600 (2022).
Google Scholar
Shabrina, N. H. et al. A novel dataset of potato leaf disease in uncontrolled environment. Data Brief 52, 109955 (2024).
Google Scholar
Spanhol, F. A., Oliveira, L. S., Petitjean, C. & Heutte, L. A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63, 1455–1462 (2015).
Google Scholar
Plissiti, M. E. et al. SIPAKMED: A new dataset for feature and image based classification of normal and pathological cervical cells in Pap smear images. In 2018 25th IEEE International Conf. Image Processing (ICIP). 3144–3148 (IEEE, 2018).
Van Der Maaten, L. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014).
Google Scholar
Selvaraju, R. R. et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In 2017 IEEE International Conference on Computer Vision (ICCV). 618–626 (2017).
Han, K. A. M., Maneerat, N., Sepsirisuk, K. & Hamamoto, K. Banana plant nutrient deficiencies identification using deep learning. In 2023 9th International Conference on Engineering, Applied Sciences, and Technology (ICEAST). 5–9 (IEEE, 2023).
Ahmad, A., El Gamal, A. & Saraswat, D. Toward generalization of deep learning-based plant disease identification under controlled and field conditions. IEEE Access 11, 9042–9057 (2023).
Google Scholar
Abdallah, N. et al. Enhancing histopathological image classification of invasive ductal carcinoma using hybrid harmonization techniques. Sci. Rep. 13, 20014 (2023).
Google Scholar
Han, Z. et al. Breast cancer multi-classification from histopathological images with structured deep learning model. Sci. Rep. 7, 4172 (2017).
Google Scholar
Basak, H., Kundu, R., Chakraborty, S. & Das, N. Cervical cytology classification using PCA and GWO enhanced deep features selection. SN Comput. Sci. 2, 369 (2021).
Google Scholar
Mohammed, M. A., Abdurahman, F. & Ayalew, Y. A. Single-cell conventional pap smear image classification using pre-trained deep neural network architectures. BMC Biomed. Eng. 3, 11 (2021).
Google Scholar
Yaman, O. & Tuncer, T. Exemplar pyramid deep feature extraction based cervical cancer image classification model using pap-smear images. Biomed. Signal Process. Control 73, 103428 (2022).
Google Scholar
Jiang, H. et al. Deep learning for computational cytology: A survey. Med. Image Anal. 84, 102691 (2023).
Google Scholar
link