{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T04:02:18Z","timestamp":1761537738264,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819538072","type":"print"},{"value":"9789819538089","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-3808-9_11","type":"book-chapter","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T03:58:04Z","timestamp":1761537484000},"page":"145-155","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RetinaNet vs. SSDMobileNetV2: A Comparative Study on Smartphone-Based Classification of Indonesian Rice Varieties"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1288-4267","authenticated-orcid":false,"given":"Hadi","family":"Santoso","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9459-0405","authenticated-orcid":false,"given":"Bagus","family":"Priambodo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7389-9967","authenticated-orcid":false,"given":"Bambang","family":"Jokonowo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3897-0873","authenticated-orcid":false,"given":"Rabiah Abdul","family":"Kadir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riza","family":"Sulaiman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","unstructured":"Bhattacharya, A.: A novel deep learning based model for classification of rice leaf diseases. In: 2021 Swedish Workshop on Data Science (SweDS), pp. 1\u20136 (2021). https:\/\/doi.org\/10.1109\/SweDS53855.2021.9638278","DOI":"10.1109\/SweDS53855.2021.9638278"},{"issue":"5","key":"11_CR2","doi-asserted-by":"publisher","first-page":"206","DOI":"10.25165\/j.ijabe.20211405.5902","volume":"14","author":"Y Qian","year":"2021","unstructured":"Qian, Y., et al.: Classification of rice seed variety using point cloud data combined with deep learning. Int. J. Agric. Biol. Eng. 14(5), 206\u2013212 (2021). https:\/\/doi.org\/10.25165\/j.ijabe.20211405.5902","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"11_CR3","doi-asserted-by":"publisher","unstructured":"Teja, K.U.V.R., Reddy, B.P.V., Kesara, L.R., Kowshik, K.D.P., Panchaparvala, L.A.: Transfer learning based rice leaf disease classification with inception-V3. In: 2021 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), pp. 1\u20136 (2021). https:\/\/doi.org\/10.1109\/SMARTGENCON51891.2021.9645888","DOI":"10.1109\/SMARTGENCON51891.2021.9645888"},{"key":"11_CR4","doi-asserted-by":"publisher","unstructured":"Taner, A., \u00d6ztekin, Y.B., Duran, H.: Performance analysis of deep learning CNN models for variety classification in Hazelnut. Sustainability (Switzerland) (2021). https:\/\/doi.org\/10.3390\/su13126527","DOI":"10.3390\/su13126527"},{"key":"11_CR5","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph19159639","author":"B Li","year":"2022","unstructured":"Li, B., Zhuo, N., Ji, C., Zhu, Q.: Influence of smartphone-based digital extension service on farmers\u2019 sustainable agricultural technology adoption in China. Int. J. Environ. Res. Public Health (2022). https:\/\/doi.org\/10.3390\/ijerph19159639","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"11_CR6","doi-asserted-by":"publisher","unstructured":"Erazo-Mesa, E., Echeverri-S\u00e1nchez, A., Ram\u00edrez-Gil J.G.: Advances in Hass avocado irrigation scheduling under digital agriculture approach. Revista Colombiana de Ciencias Horticolas (2022). https:\/\/doi.org\/10.17584\/rcch.2022v16i1.13456","DOI":"10.17584\/rcch.2022v16i1.13456"},{"key":"11_CR7","unstructured":"Ahmed, N., Muhammad Shahzad Asif, H., Saleem, G., Usman Younus, M., Professor, A.: Image quality assessment for foliar disease identification (agropath) (2021). www.jar.com.pk"},{"key":"11_CR8","doi-asserted-by":"publisher","unstructured":"Gupta, K., Garg, A., Kukreja, V., Gupta, D.: Rice diseases multi-classification: an image resizing deep learning approach. In: 2021 International Conference on Decision Aid Sciences and Application (DASA), pp. 170\u2013175 (2021). https:\/\/doi.org\/10.1109\/DASA53625.2021.9682298","DOI":"10.1109\/DASA53625.2021.9682298"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Xu, Q., et al.: Classification of rice seed variety using point cloud data combined with. Int. J. Agric. Biol. Eng. 14, 206\u2013212 (2021). https:\/\/api.semanticscholar.org\/CorpusID:244582234","DOI":"10.25165\/j.ijabe.20211405.5902"},{"issue":"4","key":"11_CR10","doi-asserted-by":"publisher","first-page":"446","DOI":"10.62411\/jcta.10459","volume":"1","author":"RK Rachman","year":"2024","unstructured":"Rachman, R.K., Setiadi, D.R.I.M., Susanto, A., Nugroho, K., Islam, H.M.M.: Enhanced vision transformer and transfer learning approach to improve rice disease recognition. J. Comput. Theor. Appl. 1(4), 446\u2013460 (2024). https:\/\/doi.org\/10.62411\/jcta.10459","journal-title":"J. Comput. Theor. Appl."},{"key":"11_CR11","doi-asserted-by":"publisher","unstructured":"Mohnish Kumaar, D., Palani, S.: ResNet50 Integrated vision transformer for enhanced plant disease classification. In: 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT), pp. 1\u20136 (2024). https:\/\/doi.org\/10.1109\/AIIoT58432.2024.10574771","DOI":"10.1109\/AIIoT58432.2024.10574771"},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"73786","DOI":"10.1109\/ACCESS.2022.3188649","volume":"10","author":"S Condran","year":"2022","unstructured":"Condran, S., Bewong, M., Islam, M.Z., Maphosa, L., Zheng, L.: Machine learning in precision agriculture: a survey on trends, applications and evaluations over two decades. IEEE Access 10, 73786\u201373803 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3188649","journal-title":"IEEE Access"},{"key":"11_CR13","doi-asserted-by":"publisher","unstructured":"Su, Z., Adam, A., Faidzul Nasrudin, M.: Adaptive focal loss for keypoint-based deep learning detectors addressing class imbalance. IEEE Access 13, 31842\u201331856 (2025). https:\/\/doi.org\/10.1109\/ACCESS.2025.3538917","DOI":"10.1109\/ACCESS.2025.3538917"},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"146610","DOI":"10.1109\/ACCESS.2024.3473536","volume":"12","author":"S Zhao","year":"2024","unstructured":"Zhao, S., Wang, H., Liu, T., Huang, S.: Integrating multiscale linear attention and focal loss for robust pest classification. IEEE Access 12, 146610\u2013146619 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3473536","journal-title":"IEEE Access"},{"key":"11_CR15","doi-asserted-by":"publisher","DOI":"10.3390\/app15073986","author":"J Kong","year":"2025","unstructured":"Kong, J., Tang, S., Feng, J., Mo, L., Jin, X.: AASNet: A Novel image instance segmentation framework for fine-grained fish recognition via linear correlation attention and dynamic adaptive focal loss. Appl. Sci. (Switzerland) (2025). https:\/\/doi.org\/10.3390\/app15073986","journal-title":"Appl. Sci. (Switzerland)"},{"key":"11_CR16","doi-asserted-by":"publisher","unstructured":"Zambre, Y.V., Rajkitkul, E., Mohan, A., Peeples, J.: Spatial transformer network YOLO model for agricultural object detection. In: 2024 International Conference on Machine Learning and Applications (ICMLA), pp. 115\u2013121 (2024). https:\/\/doi.org\/10.1109\/ICMLA61862.2024.00022","DOI":"10.1109\/ICMLA61862.2024.00022"},{"key":"11_CR17","doi-asserted-by":"publisher","unstructured":"Sunitha, G., Sudeepthi, A., Sreedhar, B., Shaik, A.B., Farooq, C.: RetinaNet and vision transformer-based model for wheat head detection. In: 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 151\u2013156 (2023). https:\/\/doi.org\/10.1109\/ICIRCA57980.2023.10220614","DOI":"10.1109\/ICIRCA57980.2023.10220614"},{"key":"11_CR18","doi-asserted-by":"publisher","unstructured":"Bhuria, R.: Smart agriculture: leveraging ResNet50 for precise detection and classification of banana leaf diseases. In: 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), pp. 1\u20136 (2025). https:\/\/doi.org\/10.1109\/IITCEE64140.2025.10915412","DOI":"10.1109\/IITCEE64140.2025.10915412"},{"key":"11_CR19","doi-asserted-by":"publisher","DOI":"10.3390\/app12178467","author":"N Khasawneh","year":"2022","unstructured":"Khasawneh, N., Faouri, E., Fraiwan, M.: Automatic detection of tomato diseases using deep transfer learning. Appl. Sci. (Switzerland) (2022). https:\/\/doi.org\/10.3390\/app12178467","journal-title":"Appl. Sci. (Switzerland)"},{"key":"11_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10775-6","author":"L Lei","year":"2024","unstructured":"Lei, L., Yang, Q., Yang, L., Shen, T., Wang, R., Fu, C.: Deep learning implementation of image segmentation in agricultural applications: a comprehensive review. Artif. Intell. Rev. (2024). https:\/\/doi.org\/10.1007\/s10462-024-10775-6","journal-title":"Artif. Intell. Rev."},{"key":"11_CR21","doi-asserted-by":"publisher","unstructured":"Suma, K.G., et al.: CETR: CenterNet-Vision transformer model for wheat head detection. J. Auton. Intell. (2024). https:\/\/doi.org\/10.32629\/jai.v7i3.1189","DOI":"10.32629\/jai.v7i3.1189"},{"key":"11_CR22","doi-asserted-by":"publisher","DOI":"10.3390\/molecules29030682","author":"Z Kang","year":"2024","unstructured":"Kang, Z., et al.: The rapid non-destructive differentiation of different varieties of rice by fluorescence hyperspectral technology combined with machine learning. Molecules (2024). https:\/\/doi.org\/10.3390\/molecules29030682","journal-title":"Molecules"},{"key":"11_CR23","unstructured":"Saddami, K., Nurdin, Y., Zahramita, M., Shahreeza Safiruz, M.: Advancing Green AI: Efficient and Accurate Lightweight CNNs for Rice Leaf Disease Identification (2024). Accessed 30 Jun 2025. https:\/\/arxiv.org\/abs\/2408.01752"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Kambo, R., Yerpude, A.: Classification of basmati rice grain variety using image processing and principal component analysis. Int. J. Comput. Trends Technol. 11(2), (2014). http:\/\/www.ijcttjournal.org","DOI":"10.14445\/22312803\/IJCTT-V11P117"},{"key":"11_CR25","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1595","author":"J Lu","year":"2023","unstructured":"Lu, J., Liu, X., Ma, X., Tong, J., Peng, J.: Improved MobileNetV2 crop disease identification model for intelligent agriculture. PeerJ Comput. Sci. (2023). https:\/\/doi.org\/10.7717\/peerj-cs.1595","journal-title":"PeerJ Comput. Sci."},{"key":"11_CR26","doi-asserted-by":"publisher","DOI":"10.1590\/1678-4324-2024220754","author":"G \u00c7\u0131narer","year":"2024","unstructured":"\u00c7\u0131narer, G., Erba\u015f, N., \u00d6cal, A.: Rice classification and quality detection success with artificial intelligence technologies. Braz. Arch. Biol. Technol. (2024). https:\/\/doi.org\/10.1590\/1678-4324-2024220754","journal-title":"Braz. Arch. Biol. Technol."},{"issue":"6","key":"11_CR27","doi-asserted-by":"publisher","first-page":"4735","DOI":"10.1021\/acsomega.1c04102","volume":"7","author":"B Jin","year":"2022","unstructured":"Jin, B., et al.: Identification of rice seed varieties based on near-infrared hyperspectral imaging technology combined with deep learning. ACS Omega 7(6), 4735\u20134749 (2022). https:\/\/doi.org\/10.1021\/acsomega.1c04102","journal-title":"ACS Omega"},{"key":"11_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/e26080645","author":"X Xie","year":"2024","unstructured":"Xie, X., et al.: Puppet dynasty recognition system based on MobileNetV2. Entropy (2024). https:\/\/doi.org\/10.3390\/e26080645","journal-title":"Entropy"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Xia, W., Peng, R., Chu, H., Zhu, X., Yang, Z., Wang, Y.: An Overall Real-Time Mechanism for Classification and Quality Evaluation of Rice (2025)","DOI":"10.2139\/ssrn.4760270"}],"container-title":["Lecture Notes in Computer Science","Advances in Visual Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3808-9_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T03:58:06Z","timestamp":1761537486000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3808-9_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,28]]},"ISBN":["9789819538072","9789819538089"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3808-9_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,28]]},"assertion":[{"value":"28 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IVIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Visual Informatics Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"12 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ivic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ukm.my\/ivic","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}