{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T00:06:05Z","timestamp":1759017965792,"version":"3.44.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031962271"},{"type":"electronic","value":"9783031962288"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-96228-8_12","type":"book-chapter","created":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T05:38:00Z","timestamp":1750743480000},"page":"156-170","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Agricultural Disease Management: An Application of\u00a0Deep Learning for\u00a0Fusarium Head Blight Detection in\u00a0Wheat Crops"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0120-3822","authenticated-orcid":false,"given":"Munza","family":"Kamukwamba","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5632-1220","authenticated-orcid":false,"given":"Dustin","family":"van der Haar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9040-3601","authenticated-orcid":false,"given":"Hima","family":"Vadapalli","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"12_CR1","first-page":"46","volume":"12","author":"PA Nazarov","year":"2020","unstructured":"Nazarov, P.A., Baleev, D.N., Ivanova, M.I., Sokolova, L.M., Karakozova, M.V.: Infectious plant diseases: etiology, current status, problems and prospects in plant protection. Nat. Libr. Med. 12, 46\u201359 (2020)","journal-title":"Nat. Libr. Med."},{"key":"12_CR2","doi-asserted-by":"publisher","unstructured":"Ristaino, J.B., et al.: The persistent threat of emerging plant disease pandemics to global food security (2021). https:\/\/doi.org\/10.1073\/pnas.2022239118","DOI":"10.1073\/pnas.2022239118"},{"key":"12_CR3","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1016\/S2095-3119(15)61300-4","volume":"15","author":"H Dun-Chun","year":"2016","unstructured":"Dun-Chun, H., Jia-sui, Z., Lian-hui, X.: Problems, challenges and future of plant disease management: from an ecological point of view. J. Integr. Agric. 15, 705\u2013715 (2016)","journal-title":"J. Integr. Agric."},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Bernardes, R.C., et al.: Deep-learning approach for fusarium head blight detection in wheat seeds using low-cost imaging technology. Agriculture 12 (2022). https:\/\/doi.org\/10.3390\/agriculture12111801","DOI":"10.3390\/agriculture12111801"},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"Dweba, C.C., et al.: Fusarium head blight of wheat: pathogenesis and control strategies. Crop Prot. 91, 114\u2013122 (2017). https:\/\/doi.org\/10.1016\/j.cropro.2016.10.002","DOI":"10.1016\/j.cropro.2016.10.002"},{"key":"12_CR6","doi-asserted-by":"publisher","unstructured":"Xu, M., Wang, Q., Wang, G., Zhang, X., Liu, H., Jiang, C.: Combatting Fusarium head blight: advances in molecular interactions between Fusarium Graminearum and wheat. Pyhtopathology Res. 4 (2022). https:\/\/doi.org\/10.1186\/s42483-022-00142-0","DOI":"10.1186\/s42483-022-00142-0"},{"key":"12_CR7","doi-asserted-by":"publisher","unstructured":"Golhani, K., Balasundram, S.K., Vadamalai, G., Pradhan, B.: A review of neural networks in plant disease detection using hyperspectral data. Inf. Process. Agric. 5, 354\u2013371 (2018). https:\/\/doi.org\/10.1016\/j.jnpa.2018.05.002","DOI":"10.1016\/j.jnpa.2018.05.002"},{"key":"12_CR8","doi-asserted-by":"publisher","unstructured":"Zhang, X., Qiao, Y., Meng, F., Fan, C., Zhang, M.: Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6, 30370\u201330377 (2017). https:\/\/doi.org\/10.1109\/ACCESS.2018.2844405","DOI":"10.1109\/ACCESS.2018.2844405"},{"key":"12_CR9","doi-asserted-by":"publisher","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436\u2013444 (2015). https:\/\/doi.org\/10.1038\/nature14539","DOI":"10.1038\/nature14539"},{"key":"12_CR10","doi-asserted-by":"publisher","unstructured":"Chohan, M., Khan, A., Chohan, R., Katpar, S.H., Mahar, M.S.: Plant disease detection using deep learning. Int. J. Recent Technol. Eng. 9, 2277\u20133878 (2020). https:\/\/doi.org\/10.35940\/iirte.A2139.059120","DOI":"10.35940\/iirte.A2139.059120"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sens. 11 (2019)","DOI":"10.3390\/rs11131554"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Feng, L., Wu, B., He, Y., Zhang, C.: Hyperspectral imaging combined with deep transfer learning for rice disease detection. Front. Plant Sci. 12 (2021)","DOI":"10.3389\/fpls.2021.693521"},{"key":"12_CR13","unstructured":"Gluon: SoftmaxCrossEntropyLoss vs. KLDivLoss (2024). https:\/\/discuss.mxnet.apache.org\/t\/softmaxcrossentropyloss-vs-kldivloss\/4807. Accessed 26 Aug 2024"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Moshou, D., Bravo, C., West, J., Wahlen, S., McCartney, A., Ramon, H.: Automatic detection of \u201cyellow rust\u201d in wheat using reflectance measurements and neural networks. Comput. Electron. Agric. 44, 173\u2013188 (2004)","DOI":"10.1016\/j.compag.2004.04.003"},{"key":"12_CR15","unstructured":"StellarNet: Glossary of Spectroscopy Terms: What is a Spectrograph?. https:\/\/www.stellarnet.us\/what-is-a-spectrograph\/. Accessed 27 Aug 2024"},{"key":"12_CR16","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1007\/s41348-024-00876-3","volume":"131","author":"E Saraswathi","year":"2024","unstructured":"Saraswathi, E., Banu, F.F.: A novel probabilistic intermittent neural network (PINN) and artificial jelly fish optimization (AJFO)-based plant leaf disease detection system. J. Plant Dis. Prot. 131, 587\u2013600 (2024)","journal-title":"J. Plant Dis. Prot."},{"key":"12_CR17","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.aej.2023.03.093","volume":"72","author":"N Omer","year":"2023","unstructured":"Omer, N., Samak, A.H., Taloba, A.I., Abd El-Aziz, R.M.: A novel optimised probabilistic neural network approach for intrusion detection and categorisation. Alex. Eng. J. 72, 351\u2013361 (2023)","journal-title":"Alex. Eng. J."},{"key":"12_CR18","unstructured":"Robeson: Beyond Visible Spectrum: AI for Agriculture (2024). https:\/\/www.kaggle.com\/competitions\/beyond-visible-spectrum-ai-for-agriculture-2024\/data. Accessed 25 Mar 2025"},{"key":"12_CR19","doi-asserted-by":"publisher","unstructured":"Migenda, N., M\u00f6ller, R., Schenck, W.: Adaptive dimensionality reduction for neural network-based online principal component analysis. PLoS ONE. 16 (2021). https:\/\/doi.org\/10.1371\/journal.pone.0248896","DOI":"10.1371\/journal.pone.0248896"},{"key":"12_CR20","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"12_CR21","unstructured":"Dilmegani, C.: What is Data Augmentation? Techniques & Examples in 2024. https:\/\/research.aimultiple.com\/data-augmentation\/. Accessed 25 Aug 2024"},{"key":"12_CR22","unstructured":"AssemblyAI: Batch normalization \u2014 What it is and how to implement it. YouTube (2021). https:\/\/www.youtube.com\/watch?v=yXOMHOpbon8"},{"key":"12_CR23","doi-asserted-by":"publisher","unstructured":"Lei, Z., Zhonglin, Z.: A improved pooling method for convolutional neural networks. Sci. Rep. 14 (2023). https:\/\/doi.org\/10.1038\/s41598-024-51258-6","DOI":"10.1038\/s41598-024-51258-6"},{"key":"12_CR24","volume-title":"Generative Deep Learning: Teaching Machines to Paint","author":"D Foster","year":"2023","unstructured":"Foster, D.: Generative Deep Learning: Teaching Machines to Paint. Compose and Play. O\u2019Reilly, Write (2023)"},{"key":"12_CR25","doi-asserted-by":"publisher","unstructured":"Sharma, N., Jain, V., Mishra, A.: An analysis of convolutional neural networks for image classification. In: International Conference on Computational Intelligence and Data Science (2018). https:\/\/doi.org\/10.1016\/j.procs.2018.05.198","DOI":"10.1016\/j.procs.2018.05.198"},{"key":"12_CR26","doi-asserted-by":"crossref","unstructured":"Fraiwan, M., Faouri, E., Khasawneh, N.: Classification of corn diseases from leaf images using deep transfer learning. Plants 11 (2022)","DOI":"10.3390\/plants11202668"},{"key":"12_CR27","doi-asserted-by":"crossref","unstructured":"Erickson, B.J., Kitamura, F.: Magician\u2019s corner: 9. performance metrics for machine learning models. Radiol. Artif. Intell. 3 (2021)","DOI":"10.1148\/ryai.2021200126"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-96228-8_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T11:07:38Z","timestamp":1758971258000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-96228-8_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031962271","9783031962288"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-96228-8_12","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"type":"print","value":"1868-4238"},{"type":"electronic","value":"1868-422X"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Limassol","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cyprus","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ifipaiai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}