{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T19:06:08Z","timestamp":1764961568216,"version":"3.46.0"},"reference-count":53,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Soybean Center at the Southern Illinois University Carbondale","award":["202196"],"award-info":[{"award-number":["202196"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The accurate identification of soybean growth stages is critical for optimizing agricultural interventions, where mistimed treatments can result in yield losses ranging from 2.5% to 40%. Existing deep learning approaches remain limited in scope, targeting isolated developmental phases rather than providing comprehensive phenological coverage. This paper presents a novel object detection architecture DELTA-SoyStage, combining an EfficientNet backbone with a lightweight ChannelMapper neck and a newly proposed DELTA (Denoising Enhanced Lightweight Task Alignment) detection head for soybean growth stage classification. We introduce a dataset of 17,204 labeled RGB images spanning nine growth stages from emergence (VE) through full maturity (R8), collected under controlled greenhouse conditions with diverse imaging angles and lighting variations. DELTA-SoyStage achieves 73.9% average precision with only 24.4 GFLOPs computational cost, demonstrating 4.2\u00d7 fewer FLOPs than the best-performing baseline (DINO-Swin: 74.7% AP, 102.5 GFLOPs) with only 0.8% accuracy difference. The lightweight DELTA head combined with the efficient ChannelMapper neck requires only 8.3 M parameters\u2014a 43.5% reduction compared to standard architectures\u2014while maintaining competitive accuracy. Extensive ablation studies validate key design choices including task alignment mechanisms, multi-scale feature extraction strategies, and encoder\u2013decoder depth configurations. The proposed model\u2019s computational efficiency makes it suitable for deployment on resource-constrained edge devices in precision agriculture applications, enabling timely decision-making without reliance on cloud infrastructure.<\/jats:p>","DOI":"10.3390\/s25237303","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:42:02Z","timestamp":1764960122000},"page":"7303","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DELTA-SoyStage: A Lightweight Detection Architecture for Full-Cycle Soybean Growth Stage Monitoring"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8978-3917","authenticated-orcid":false,"given":"Abdellah","family":"Lakhssassi","sequence":"first","affiliation":[{"name":"School of Computing, Southern Illinois University, Carbondale, IL 62901, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9196-1468","authenticated-orcid":false,"given":"Yasser","family":"Salhi","sequence":"additional","affiliation":[{"name":"Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, IL 62901, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8255-9419","authenticated-orcid":false,"given":"Naoufal","family":"Lakhssassi","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, School of Science, Hampton University, Hampton, VA 23668, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9469-9718","authenticated-orcid":false,"given":"Khalid","family":"Meksem","sequence":"additional","affiliation":[{"name":"Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, IL 62901, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3707-4316","authenticated-orcid":false,"given":"Khaled","family":"Ahmed","sequence":"additional","affiliation":[{"name":"School of Computing, Southern Illinois University, Carbondale, IL 62901, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Zhao, W., Lai, Y., Zhang, B., and Zhang, D. (2020). Edible plant oil: Global status, health issues, and perspectives. Front. Plant Sci., 11.","DOI":"10.3389\/fpls.2020.01315"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s11746-013-2407-9","article-title":"Current knowledge in soybean composition","volume":"91","author":"Medic","year":"2014","journal-title":"J. Am. Oil Chem. Soc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s00253-012-4330-7","article-title":"Soyfoods and soybean products: From traditional use to modern applications","volume":"96","author":"Chen","year":"2012","journal-title":"Appl. Microbiol. Biotechnol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Toomer, O.T., Oviedo-Rond\u00f3n, E.O., Ali, M., Joseph, M., Vu, T., Fallen, B., and Mian, R. (2024). Full-Fat Soybean Meals as an Alternative Poultry Feed Ingredient\u2014Feed Processing Methods and Utilization\u2014Review and Perspective. Animals, 14.","DOI":"10.3390\/ani14162366"},{"key":"ref_5","unstructured":"American Soybean Association (2025, June 15). SoyStats 2024 Edition. PDF Report, 2024. Available online: https:\/\/soygrowers.com\/wp-content\/uploads\/2024\/06\/24ASA-001-Soy-Stats-Web.pdf."},{"key":"ref_6","unstructured":"USDA National Agricultural Statistics Service (2025, June 15). US Corn Production Up While Soybean Production Down from September. News Release, 2024. Available online: https:\/\/www.world-grain.com\/articles\/20369-record-us-soybean-crop-forecast-in-2024."},{"key":"ref_7","unstructured":"World Grain (2025, June 15). Record US Soybean Crop Forecast in 2024. World Grain 2024, Available online: https:\/\/www.nass.usda.gov\/Newsroom\/2024\/10-11-2024.php."},{"key":"ref_8","first-page":"1","article-title":"Soybean growth and development","volume":"197","author":"Purcell","year":"2014","journal-title":"Ark. Soybean Prod. Handb."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1017\/wet.2022.36","article-title":"Soybean yield loss from delayed postemergence herbicide application based on weed height, days after emergence, accumulated crop heat units, and soybean growth stage","volume":"36","author":"Soltani","year":"2022","journal-title":"Weed Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1146\/annurev-ento-120709-144755","article-title":"Ecology and management of the soybean aphid in North America","volume":"56","author":"Ragsdale","year":"2011","journal-title":"Annu. Rev. Entomol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"165","DOI":"10.25100\/socolen.v44i2.7312","article-title":"Action thresholds for the soybean stink bug complex: Phytotechnical and physiological parameters and egg parasitism","volume":"44","author":"Eduardo","year":"2018","journal-title":"Rev. Colomb. Entomol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zahra, S., Ruiz, H., Jung, J., and Adams, T. (2024). UAV-Based Phenotyping: A Non-Destructive Approach to Studying Wheat Growth Patterns for Crop Improvement and Breeding Programs. Remote Sens., 16.","DOI":"10.3390\/rs16193710"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"21219","DOI":"10.1109\/ACCESS.2022.3152544","article-title":"IoT-equipped and AI-enabled next generation smart agriculture: A critical review, current challenges and future trends","volume":"10","author":"Qazi","year":"2022","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107938","DOI":"10.1016\/j.agrformet.2020.107938","article-title":"A near real-time deep learning approach for detecting rice phenology based on UAV images","volume":"287","author":"Yang","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Agrawal, J., and Arafat, M.Y. (2024). Transforming Farming: A Review of AI-Powered UAV Technologies in Precision Agriculture. Drones, 8.","DOI":"10.3390\/drones8110664"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhao, J., Gao, A., Wang, B., Wen, J., Duan, Y., Wang, G., and Li, Z. (2025). Modeling and Visualization of Nitrogen and Chlorophyll in Greenhouse Solanum lycopersicum L. Leaves with Hyperspectral Imaging for Nitrogen Stress Diagnosis. Plants, 14.","DOI":"10.3390\/plants14213276"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, L., Niu, Y., Zhang, H., Han, W., Li, G., Tang, J., and Peng, X. (2019). Maize canopy temperature extracted from UAV thermal and RGB imagery and its application in water stress monitoring. Front. Plant Sci., 10.","DOI":"10.3389\/fpls.2019.01270"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Almeida, T.A., Montenegro, A.A., Da Silva, R.A., De Lima, J.L., Carvalho, A.A.d., and da Silva, J.R. (2024). Evaluating Daily Water Stress Index (DWSI) Using Thermal Imaging of Neem Tree Canopies under Bare Soil and Mulching Conditions. Remote Sens., 16.","DOI":"10.3390\/rs16152782"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lloret, J., Sendra, S., Garcia, L., and Jimenez, J.M. (2021). A Wireless Sensor Network Deployment for Soil Moisture Monitoring in Precision Agriculture. Sensors, 21.","DOI":"10.3390\/s21217243"},{"key":"ref_20","first-page":"193","article-title":"Wireless sensor network for monitoring soil moisture and weather conditions","volume":"31","author":"Sui","year":"2015","journal-title":"Appl. Eng. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mansoor, S., Iqbal, S., Popescu, S.M., Kim, S.L., Chung, Y.S., and Baek, J.H. (2025). Integration of smart sensors and IOT in precision agriculture: Trends, challenges and future prospectives. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1587869"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Almalky, A.M., and Ahmed, K.R. (2023). Deep learning for detecting and classifying the growth stages of consolida regalis weeds on fields. Agronomy, 13.","DOI":"10.3390\/agronomy13030934"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Islam, T., Sarker, T.T., Ahmed, K.R., Rankrape, C.B., and Gage, K. (2025). WeedSwin hierarchical vision transformer with SAM-2 for multi-stage weed detection and classification. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-05092-z"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106694","DOI":"10.1016\/j.compag.2022.106694","article-title":"Real-time growth stage detection model for high degree of occultation using DenseNet-fused YOLOv4","volume":"193","author":"Roy","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cai, W., Lu, K., Fan, M., Liu, C., Huang, W., Chen, J., Wu, Z., Xu, C., Ma, X., and Tan, S. (2024). Rice growth-stage recognition based on improved yolov8 with uav imagery. Agronomy, 14.","DOI":"10.3390\/agronomy14122751"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, J., Gao, G., Lei, Y., Zhao, C., Wang, Y., Bai, H., Liu, Y., Guo, X., and Li, Q. (2024). SGSNet: A lightweight deep learning model for strawberry growth stage detection. Front. Plant Sci., 15.","DOI":"10.3389\/fpls.2024.1491706"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1007\/s00521-020-05064-6","article-title":"Crop growth stage estimation prior to canopy closure using deep learning algorithms","volume":"33","author":"Rasti","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, C., Song, X., Pan, W., Yu, H., Li, X., and Liu, P. (2025). Determination of Wheat Growth Stages Using Image Sequences and Deep Learning. Agronomy, 15.","DOI":"10.3390\/agronomy15010013"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ni, X., Wang, F., Huang, H., Wang, L., Wen, C., and Chen, D. (2024). A CNN-and Self-Attention-Based Maize Growth Stage Recognition Method and Platform from UAV Orthophoto Images. Remote Sens., 16.","DOI":"10.3390\/rs16142672"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wan, T., Rao, Y., Jin, X., Wang, F., Zhang, T., Shu, Y., and Li, S. (2023). Improved U-net for growth stage recognition of in-field maize. Agronomy, 13.","DOI":"10.3390\/agronomy13061523"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1186\/s13007-024-01278-0","article-title":"A deep learning approach for deriving wheat phenology from near-surface RGB image series using spatiotemporal fusion","volume":"20","author":"Cai","year":"2024","journal-title":"Plant Methods"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Darlan, D., Ajani, O.S., An, J.W., Bae, N.Y., Lee, B., Park, T., and Mallipeddi, R. (2025). SmartBerry for AI-based growth stage classification and precision nutrition management in strawberry cultivation. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-97168-z"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, J., Li, Q., Yu, C., He, Y., Qi, L., Shi, W., and Zhang, W. (2022). A model for identifying soybean growth periods based on multi-source sensors and improved convolutional neural network. Agronomy, 12.","DOI":"10.3390\/agronomy12122991"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, H., Ma, X., Guan, H., Wang, F., and Shen, P. (2023). Recognition of soybean pods and yield prediction based on improved deep learning model. Front. Plant Sci., 13.","DOI":"10.3389\/fpls.2022.1096619"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"P\u00e9rez, O., Diers, B., and Martin, N. (2024). Maturity Prediction in Soybean Breeding Using Aerial Images and the Random Forest Machine Learning Algorithm. Remote Sens., 16.","DOI":"10.3390\/rs16234343"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lakhssassi, A., Sarker, T.T., Ahmed, K., Lakhssassi, N., and Meksem, K. (2025, January 10\u201315). SoyStageNet: Balancing Accuracy and Efficiency for Real-Time Soybean Growth Stage Detection. Proceedings of the Computer Vision and Pattern Recognition Conference, Nashville, TV, USA.","DOI":"10.1109\/CVPRW67362.2025.00549"},{"key":"ref_37","unstructured":"Ravi, N., Gabeur, V., Hu, Y.T., Hu, R., Ryali, C., Ma, T., Khedr, H., R\u00e4dle, R., Rolland, C., and Gustafson, L. (2024). SAM 2: Segment Anything in Images and Videos. arXiv."},{"key":"ref_38","unstructured":"(2025, September 02). Tzutalin. LabelImg. GitHub Repository. Available online: https:\/\/github.com\/tzutalin\/labelImg."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kateb, F.A., Monowar, M.M., Hamid, M.A., Ohi, A.Q., and Mridha, M.F. (2021). FruitDet: Attentive Feature Aggregation for Real-Time Fruit Detection in Orchards. Agronomy, 11.","DOI":"10.3390\/agronomy11122440"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"9243","DOI":"10.1007\/s11042-022-13644-y","article-title":"Object detection using YOLO: Challenges, architectural successors, datasets and applications","volume":"82","author":"Diwan","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_45","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., and Shum, H.Y. (2022). DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhong, Y., Gao, Y., Scott, M.R., and Huang, W. (2021, January 11\u201317). TOOD: Task-aligned One-stage Object Detection. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"ref_47","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_48","unstructured":"Paszke, A. (2019). Pytorch: An imperative style, high-performance deep learning library. arXiv."},{"key":"ref_49","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open MMLab Detection Toolbox and Benchmark. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A.A. (2020). Albumentations: Fast and flexible image augmentations. Information, 11.","DOI":"10.3390\/info11020125"},{"key":"ref_51","first-page":"21002","article-title":"Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection","volume":"33","author":"Li","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_53","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/23\/7303\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:42:26Z","timestamp":1764960146000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/23\/7303"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,1]]},"references-count":53,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["s25237303"],"URL":"https:\/\/doi.org\/10.3390\/s25237303","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,1]]}}}