{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:32:04Z","timestamp":1778365924939,"version":"3.51.4"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031882227","type":"print"},{"value":"9783031882234","type":"electronic"}],"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\/978-3-031-88223-4_8","type":"book-chapter","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T03:41:49Z","timestamp":1745466109000},"page":"104-118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Review on Image Processing Based Plant Disease Analysis Using Thermal Infrared Spectroscopy"],"prefix":"10.1007","author":[{"given":"Rama Kant","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prerana","family":"Mukherjee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Monika","family":"Agrawal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brejesh","family":"Lall","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Chen, J., et al.: Digital camera imaging system simulation. IEEE Trans. Electron Dev. 56(11), 2496\u20132505 (2009)","DOI":"10.1109\/TED.2009.2030995"},{"issue":"2","key":"8_CR2","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1094\/PDIS-03-15-0340-FE","volume":"100","author":"AK Mahlein","year":"2016","unstructured":"Mahlein, A.K.: Plant disease detection by imaging sensors\u2014parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 100(2), 241\u2013251 (2016)","journal-title":"Plant Dis."},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Kennelly, M., et al.: Introduction to abiotic disorders in plants. Plant Health Instructor\u00a010.1094, 10\u201320 (2012)","DOI":"10.1094\/PHI-I-2012-10-29-01"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Kaur, P., Gautam, V.: Plant biotic disease identification and classification based on leaf image: a review. In Proceedings of 3rd International Conference on Computing Informatics and Networks: ICCIN 2020, pp. 597\u2013610 (2021)","DOI":"10.1007\/978-981-15-9712-1_51"},{"issue":"52","key":"8_CR5","doi-asserted-by":"publisher","first-page":"2106475","DOI":"10.1002\/adfm.202106475","volume":"31","author":"G Lee","year":"2021","unstructured":"Lee, G., Wei, Q., Zhu, Y.: Emerging wearable sensors for plant health monitoring. Adv. Func. Mater. 31(52), 2106475 (2021)","journal-title":"Adv. Func. Mater."},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"Vesk, M., Possingham, J.V., Mercer, F.V.: The effect of mineral nutrient deficiencies on the structure of the leaf cells of tomato, spinach, and maize.\u00a0Aust. J. Botany\u00a014(1), 1\u201318 (1966)","DOI":"10.1071\/BT9660001"},{"key":"8_CR7","doi-asserted-by":"publisher","first-page":"2948","DOI":"10.3390\/molecules25122948","volume":"25","author":"KB Be\u0107","year":"2020","unstructured":"Be\u0107, K.B., Grabska, J., Huck, C.W.: Near-infrared spectroscopy in bio-applications. Molecules 25, 2948 (2020). https:\/\/doi.org\/10.3390\/molecules25122948","journal-title":"Molecules"},{"key":"8_CR8","doi-asserted-by":"publisher","unstructured":"Be\u0107, K.B., Huck, C.W.: Breakthrough potential in near-infrared spectroscopy: spectra simulation. a review of recent developments. Front Chem. 22(7), 48 (2019). https:\/\/doi.org\/10.3389\/fchem.2019.00048. PMID: 30854368; PMCID: PMC639607","DOI":"10.3389\/fchem.2019.00048"},{"key":"8_CR9","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1002\/cem.1079","volume":"21","author":"L Munck","year":"2007","unstructured":"Munck, L.: A new holistic exploratory approach to systems biology by near infrared spectroscopy evaluated by chemometrics and data inspection. J. Chemometrics 21, 406\u2013426 (2007)","journal-title":"J. Chemometrics"},{"key":"8_CR10","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.plantsci.2019.01.011","volume":"282","author":"T Roitsch","year":"2019","unstructured":"Roitsch, T., et al.: Review: new sensors and data-driven approaches path to next generation phenomics. Plant Sci. 282, 2\u201310 (2019)","journal-title":"Plant Sci."},{"key":"8_CR11","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1002\/cem.1278","volume":"24","author":"L Munck","year":"2010","unstructured":"Munck, L., et al.: A physiochemical theory on the applicability of soft mathematical models \u2013 experimentally interpreted. J. Chemo. 24, 481\u2013495 (2010)","journal-title":"J. Chemo."},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"De Silva, M., Brown, D.: Plant disease detection using multispectral imaging. In: Garg, D., Narayana, V.A., Suganthan, P.N., Anguera, J., Koppula, V.K., Gupta, S.K. (eds.) Advanced Computing. IACC 2022. Communications in Computer and Information Science, vol. 1781. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-35641-4_24","DOI":"10.1007\/978-3-031-35641-4_24"},{"key":"8_CR13","doi-asserted-by":"publisher","unstructured":"De Silva, M., Brown, D.: Plant disease detection using vision transformers on multispectral natural environment images. In: 2023 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, pp. 1\u20136. Durban, South Africa (2023). https:\/\/doi.org\/10.1109\/icABCD59051.2023.10220517","DOI":"10.1109\/icABCD59051.2023.10220517"},{"key":"8_CR14","doi-asserted-by":"publisher","unstructured":"Francisco, M.A., Reyes, R.A., Alvaran, E.J., Macalanda, K.R., Montances, M.: Severity classification of philippine downy mildew in corn using infrared thermography and deep learning. 1\u20135 (2024). https:\/\/doi.org\/10.1109\/EEAE60309.2024.10600541","DOI":"10.1109\/EEAE60309.2024.10600541"},{"key":"8_CR15","doi-asserted-by":"publisher","unstructured":"Oerke, E.C., Fr\u00f6hling, P., Steiner, U.: Thermographic assessment of scab disease on apple leaves. Precision Agric. 12, 699\u2013715 (2011). https:\/\/doi.org\/10.1007\/s11119-010-9212-3","DOI":"10.1007\/s11119-010-9212-3"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Wen, D., et al.: Use of thermal imaging and Fourier transform infrared spectroscopy for the pre-symptomatic detection of cucumber downy mildew. Euro. J. Plant Pathol. 1\u201312 (2019)","DOI":"10.1007\/s10658-019-01775-2"},{"key":"8_CR17","doi-asserted-by":"publisher","unstructured":"de Melo, L.L., et al.: Deep learning for identification of water deficits in sugarcane based on thermal images. Agric. Water Manage. 272, 107820 (2022). ISSN 0378-3774https:\/\/doi.org\/10.1016\/j.agwat.2022.107820","DOI":"10.1016\/j.agwat.2022.107820"},{"issue":"4","key":"8_CR18","doi-asserted-by":"publisher","first-page":"e0123262","DOI":"10.1371\/journal.pone.0123262.PMID:25861025;PMCID:PMC4393321","volume":"10","author":"SE Raza","year":"2015","unstructured":"Raza, S.E., Prince, G., Clarkson, J.P., Rajpoot, N.M.: Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS ONE 10(4), e0123262 (2015). https:\/\/doi.org\/10.1371\/journal.pone.0123262.PMID:25861025;PMCID:PMC4393321","journal-title":"PLoS ONE"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Zhu, W., Chen, H., Ciechanowska, I., Spaner, D.: Application of infrared thermal imaging for the rapid diagnosis of crop disease, IFAC-PapersOnLine, 51(17), 424\u2013430 (2018). ISSN 2405\u20138963","DOI":"10.1016\/j.ifacol.2018.08.184"},{"issue":"7","key":"8_CR20","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1093\/pcp\/pch097","volume":"45","author":"L Chaerle","year":"2004","unstructured":"Chaerle, L., Hagenbeek, D., De Bruyne, E., Valcke, R., Van Der Straeten, D.: Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage. Plant Cell Physiol. 45(7), 887\u201396 (2004). https:\/\/doi.org\/10.1093\/pcp\/pch097. PMID: 15295072","journal-title":"Plant Cell Physiol."},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Bompilwar, R., Singh Rathor, S.P., Das, D.: tCrop: thermal imaging based plant stress identification using on-edge deep learning. In: 2022 IEEE Region 10 Symposium (TENSYMP), pp. 1\u20136. Mumbai, India (2022)","DOI":"10.1109\/TENSYMP54529.2022.9864547"},{"key":"8_CR22","doi-asserted-by":"publisher","unstructured":"Raza, S.A.: Registration of thermal and visible light images of diseased plants using silhouette extraction in the wavelet domain. Pattern Recogn. 48 (2015). https:\/\/doi.org\/10.1016\/j.patcog.2015.01.027","DOI":"10.1016\/j.patcog.2015.01.027"},{"issue":"10","key":"8_CR23","doi-asserted-by":"publisher","first-page":"10474","DOI":"10.1016\/j.jksuci.2022.11.003","volume":"34","author":"G Batchuluun","year":"2022","unstructured":"Batchuluun, G., Nam, S.H., Park, K.R.: Deep learning-based plant classification and crop disease classification by thermal camera. J. King Saud Univ. Comput. Inf. Sci. 34(10), 10474\u20131048 (2022)","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Attallah, O.: Deep learning-based model for paddy diseases classification by thermal infrared sensor: an application for precision agriculture. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume X-1\/W1\u20132023, pp.779\u2013784 (2023)","DOI":"10.5194\/isprs-annals-X-1-W1-2023-779-2023"},{"issue":"9","key":"8_CR25","doi-asserted-by":"publisher","first-page":"1682","DOI":"10.3390\/agronomy11091682","volume":"11","author":"I Vagelas","year":"2021","unstructured":"Vagelas, I., Papadimos, A., Lykas, C.: Pre-symptomatic disease detection in the vine, chrysanthemum, and rose leaves with a low-cost infrared sensor. Agronomy 11(9), 1682 (2021). https:\/\/doi.org\/10.3390\/agronomy11091682","journal-title":"Agronomy"},{"key":"8_CR26","unstructured":"Ahmed, A.: Application of thermal imaging sensor to early detect powdery mildew disease in wheat. Middle East J. (2016)"},{"key":"8_CR27","doi-asserted-by":"publisher","unstructured":"De Silva, M., Brown, D.: Early plant disease detection using infrared and mobile photographs in natural environment (2023).https:\/\/doi.org\/10.1007\/978-3-031-37717-4_21","DOI":"10.1007\/978-3-031-37717-4_21"},{"key":"8_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11119-022-09927-x","volume":"24","author":"I Bhakta","year":"2022","unstructured":"Bhakta, I., Phadikar, S., Majumder, K., Mukherjee, H., Sau, A.: A novel plant disease prediction model based on thermal images using modified deep convolutional neural network. Precision Agric. 24, 1\u201317 (2022). https:\/\/doi.org\/10.1007\/s11119-022-09927-x","journal-title":"Precision Agric."},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Anasta, N., Setyawan, F.X., Fitriawan, H.: Disease detection in banana trees using an image processing-based thermal camera, IOP Conf. Ser. Earth Environ. Sci. 739 (2021)","DOI":"10.1088\/1755-1315\/739\/1\/012088"},{"issue":"3","key":"8_CR30","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1094\/PHYTO-95-0233","volume":"95","author":"M Lindenthal","year":"2005","unstructured":"Lindenthal, M., Steiner, U., Dehne, H.W., Oerke, E.C.: Effect of downy mildew development on transpiration of cucumber leaves visualized by digital infrared thermography. Phytopathology 95(3), 233\u201340 (2005). https:\/\/doi.org\/10.1094\/PHYTO-95-0233. PMID: 1894311","journal-title":"Phytopathology"},{"key":"8_CR31","doi-asserted-by":"crossref","unstructured":"Yuan, L., et al. :Tokens-to-token ViT: training vision transformers from scratch on ImageNet. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 538\u2013547. Montreal, QC, Canada (2021)","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Dutta, A.: Chapter 4 - fourier transform infrared spectroscopy. In: Thomas, S., Thomas, R., Zachariah, A.K., Mishra, R.K., (eds.) Micro and Nano Technologies, Spectroscopic Methods for Nanomaterials Characterization, Elsevier, pp. 73\u201393 (2017)","DOI":"10.1016\/B978-0-323-46140-5.00004-2"},{"key":"8_CR33","first-page":"176","volume-title":"Face Attribute Detection with MobileNetV2 and NasNet-Mobile, 11th International Symposium on Image and Signal Processing and Analysis (ISPA)","author":"F Saxen","year":"2019","unstructured":"Saxen, F., Werner, P., Handrich, S., Othman, E., Dinges, L., Al-Hamadi, A.: Face Attribute Detection with MobileNetV2 and NasNet-Mobile, 11th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 176\u2013180. Dubrovnik, Croatia (2019)"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al. :Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"8_CR35","unstructured":"Targ, S., Almeida, D., Lyman, K.: Resnet in resnet: Generalizing residual architectures, arXiv preprint arXiv:1603.08029\u00a0(2016)"},{"key":"8_CR36","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Newsam, S.: Densenet for dense flow. In: 2017 IEEE International Conference on Image Processing (ICIP). IEEE (2017)","DOI":"10.1109\/ICIP.2017.8296389"},{"key":"8_CR37","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3389\/fnins.2019.00095","volume":"13","author":"A Sengupta","year":"2019","unstructured":"Sengupta, A., et al.: Going deeper in spiking neural networks: VGG and residual architectures. Front. Neurosci. 13, 95 (2019)","journal-title":"Front. Neurosci."},{"key":"8_CR38","doi-asserted-by":"crossref","unstructured":"Chollet, F :Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"8_CR39","doi-asserted-by":"crossref","unstructured":"Koonce, B., Koonce, B.: EfficientNet. :Convolutional neural networks with swift for Tensorflow: image recognition and dataset categorization,\u00a0109\u2013123 (2021)","DOI":"10.1007\/978-1-4842-6168-2_10"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition. ICPR 2024 International Workshops and Challenges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-88223-4_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T03:42:14Z","timestamp":1745466134000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-88223-4_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031882227","9783031882234"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-88223-4_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}