{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:29:14Z","timestamp":1762514954430,"version":"build-2065373602"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"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":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s13042-025-02755-1","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T08:16:32Z","timestamp":1756368992000},"page":"9295-9321","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A comprehensive cross-attention and fuzzy segmentation approach for rice plant disease detection"],"prefix":"10.1007","volume":"16","author":[{"given":"R.","family":"Prabavathi","sequence":"first","affiliation":[]},{"given":"Balika J.","family":"Chelliah","sequence":"additional","affiliation":[]},{"given":"R.","family":"Sampath","sequence":"additional","affiliation":[]},{"given":"S.","family":"Vinodh Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"2755_CR1","doi-asserted-by":"publisher","unstructured":"Abd Algani YM, Caro OJM, Bravo LMR, Kaur C, Al Ansari MS, Bala BK (2023) Leaf disease identification and classification using optimized deep learning.\u00a0Measur Sensors 25:100643. https:\/\/doi.org\/10.1016\/j.measen.2022.100643","DOI":"10.1016\/j.measen.2022.100643"},{"key":"2755_CR2","doi-asserted-by":"publisher","first-page":"109477","DOI":"10.1109\/ACCESS.2023.3322587","volume":"11","author":"N Bharanidharan","year":"2023","unstructured":"Bharanidharan N, Chakravarthy SS, Rajaguru H, Kumar VV, Mahesh TR, Guluwadi S (2023) Multiclass paddy disease detection using filter based feature transformation technique. IEEE Access 11:109477\u2013109487. https:\/\/doi.org\/10.1109\/ACCESS.2023.3322587","journal-title":"IEEE Access"},{"issue":"1","key":"2755_CR3","doi-asserted-by":"publisher","first-page":"39","DOI":"10.3103\/S1060992X2301006X","volume":"32","author":"DJ Chaudhari","year":"2023","unstructured":"Chaudhari DJ, Malathi K (2023) Detection and prediction of rice leaf disease using a hybrid CNN-SVM model. Optical Memory Neural Netw 32(1):39\u201357. https:\/\/doi.org\/10.3103\/S1060992X2301006X","journal-title":"Optical Memory Neural Netw"},{"key":"2755_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-981-99-8684-2_1","volume":"4","author":"SS Chouhan","year":"2024","unstructured":"Chouhan SS, Singh UP, Jain S (2024) Introduction to computer vision and drone technology. Appl Comput Vis Drone Technol Agric 4:1\u20135. https:\/\/doi.org\/10.1007\/978-981-99-8684-2_1","journal-title":"Appl Comput Vis Drone Technol Agric"},{"key":"2755_CR5","doi-asserted-by":"publisher","unstructured":"Chouhan SS, Singh UP, Saxena A, Jain S (2024) Assessing the importance and need of artificial intelligence for precision agriculture. In: Artificial intelligence techniques in smart agriculture 1\u20136. https:\/\/doi.org\/10.1007\/978-981-97-5878-4_1","DOI":"10.1007\/978-981-97-5878-4_1"},{"issue":"4","key":"2755_CR6","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1007\/s11277-024-11374-y","volume":"136","author":"SS Chouhan","year":"2024","unstructured":"Chouhan SS, Singh UP, Sharma U, Jain S (2024) Classification of different plant species using deep learning and machine learning algorithms. Wirel Pers Commun 136(4):2275\u20132298. https:\/\/doi.org\/10.1007\/s11277-024-11374-y","journal-title":"Wirel Pers Commun"},{"key":"2755_CR7","doi-asserted-by":"publisher","unstructured":"Daniya T, Vigneshwari S (2023) Rice plant leaf disease detection and classification using optimization enabled deep learning. J Environ Inform 42(1). https:\/\/doi.org\/10.3808\/jei.202300492","DOI":"10.3808\/jei.202300492"},{"issue":"1","key":"2755_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s40537-023-00863-9","volume":"11","author":"WB Demilie","year":"2024","unstructured":"Demilie WB (2024) Plant disease detection and classification techniques: a comparative study of the performances. J Big Data 11(1):5. https:\/\/doi.org\/10.1186\/s40537-023-00863-9","journal-title":"J Big Data"},{"issue":"4","key":"2755_CR9","doi-asserted-by":"publisher","first-page":"3679","DOI":"10.1080\/03772063.2023.2195842","volume":"70","author":"RK Dubey","year":"2024","unstructured":"Dubey RK, Choubey DK (2024) Efficient prediction of blast disease in paddy plant using optimized support vector machine. IETE J Res 70(4):3679\u20133689. https:\/\/doi.org\/10.1080\/03772063.2023.2195842","journal-title":"IETE J Res"},{"issue":"3","key":"2755_CR10","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1002\/agj2.21449","volume":"116","author":"RK Dubey","year":"2024","unstructured":"Dubey RK, Choubey DK (2024) Reliable detection of blast disease in rice plant using optimized artificial neural network. Agron J 116(3):1099\u20131111. https:\/\/doi.org\/10.1002\/agj2.21449","journal-title":"Agron J"},{"key":"2755_CR11","doi-asserted-by":"publisher","unstructured":"Farooqui NA, Haleem M, Khan W, Ishrat M (2024) Precision agriculture and predictive analytics: enhancing agricultural efficiency and yield. Intell Techn Predictive Data Analy: 171\u2013188. https:\/\/doi.org\/10.1002\/9781394227990.ch9","DOI":"10.1002\/9781394227990.ch9"},{"issue":"2","key":"2755_CR12","doi-asserted-by":"publisher","first-page":"407","DOI":"10.3233\/IDT-210182","volume":"16","author":"NA Farooqui","year":"2022","unstructured":"Farooqui NA, Mishra AK, Mehra R (2022) Automatic crop disease recognition by improved abnormality segmentation along with heuristic-based concatenated deep learning model. Intell Decis Technol 16(2):407\u2013429. https:\/\/doi.org\/10.3233\/IDT-210182","journal-title":"Intell Decis Technol"},{"issue":"3","key":"2755_CR13","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1007\/s41315-022-00258-8","volume":"7","author":"NA Farooqui","year":"2023","unstructured":"Farooqui NA, Mishra AK, Mehra R (2023) Concatenated deep features with modified LSTM for enhanced crop disease classification. Int J Intell Robot Appl 7(3):510\u2013534. https:\/\/doi.org\/10.1007\/s41315-022-00258-8","journal-title":"Int J Intell Robot Appl"},{"key":"2755_CR14","doi-asserted-by":"publisher","unstructured":"Farooqui NA, Mishra AK, Ray K, Mallik S (2023) Leaf disease segmentation using Uunet++ architecture. In: International conference on trends in electronics and health informatics, 769\u2013780. https:\/\/doi.org\/10.1007\/978-981-97-3937-0_52","DOI":"10.1007\/978-981-97-3937-0_52"},{"issue":"1","key":"2755_CR15","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/s10661-022-10656-x","volume":"195","author":"A Haridasan","year":"2023","unstructured":"Haridasan A, Thomas J, Raj ED (2023) Deep learning system for paddy plant disease detection and classification. Environ Monit Assess 195(1):120. https:\/\/doi.org\/10.1007\/s10661-022-10656-x","journal-title":"Environ Monit Assess"},{"issue":"5","key":"2755_CR16","doi-asserted-by":"publisher","first-page":"2225","DOI":"10.1007\/s10115-022-01818-x","volume":"65","author":"JG Jang","year":"2023","unstructured":"Jang JG, Quan C, Lee HD, Kang U (2023) Falcon: lightweight and accurate convolution based on depthwise separable convolution. Knowl Inf Syst 65(5):2225\u20132249. https:\/\/doi.org\/10.1007\/s10115-022-01818-x","journal-title":"Knowl Inf Syst"},{"issue":"3","key":"2755_CR17","doi-asserted-by":"publisher","first-page":"508","DOI":"10.3390\/electronics12030508","volume":"12","author":"M Jiang","year":"2023","unstructured":"Jiang M, Feng C, Fang X, Huang Q, Zhang C, Shi X (2023) Rice disease identification method based on attention mechanism and deep dense network. Electronics 12(3):508. https:\/\/doi.org\/10.3390\/electronics12030508","journal-title":"Electronics"},{"key":"2755_CR18","doi-asserted-by":"publisher","unstructured":"Joko S (2024) Utilization of artificial neural network in rice plant disease classification using leaf image.\u00a0Int J Res Sci Eng (IJRISE) 4(02):1\u201310. https:\/\/doi.org\/10.55529\/ijrise.42.1.10","DOI":"10.55529\/ijrise.42.1.10"},{"issue":"4","key":"2755_CR19","doi-asserted-by":"publisher","first-page":"2213","DOI":"10.1002\/agj2.21070","volume":"114","author":"K Kishore Kumar","year":"2022","unstructured":"Kishore Kumar K, Kannan E (2022) Detection of rice plant disease using AdaBoostSVM classifier. Agron J 114(4):2213\u20132229. https:\/\/doi.org\/10.1002\/agj2.21070","journal-title":"Agron J"},{"key":"2755_CR20","doi-asserted-by":"publisher","unstructured":"Kulkarni P, Shastri S (2024) Rice leaf diseases detection using machine learning. J Sci Res Technol: 17\u201322. https:\/\/doi.org\/10.61808\/jsrt81","DOI":"10.61808\/jsrt81"},{"key":"2755_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3419906","author":"R Kumar","year":"2024","unstructured":"Kumar R, Kumar A, Bhatia K, Nisar KS, Chouhan SS, Maratha P, Tiwari AK (2024) Hybrid approach of cotton disease detection for enhanced crop health and yield. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2024.3419906","journal-title":"IEEE Access"},{"issue":"17","key":"2755_CR22","doi-asserted-by":"publisher","first-page":"2230","DOI":"10.3390\/plants11172230","volume":"11","author":"G Latif","year":"2022","unstructured":"Latif G, Abdelhamid SE, Mallouhy RE, Alghazo J, Kazimi ZA (2022) Deep learning utilization in agriculture: detection of rice plant diseases using an improved CNN model. Plants 11(17):2230. https:\/\/doi.org\/10.3390\/plants11172230","journal-title":"Plants"},{"key":"2755_CR23","doi-asserted-by":"publisher","first-page":"95197","DOI":"10.1109\/ACCESS.2022.3203813","volume":"10","author":"R Liu","year":"2022","unstructured":"Liu R, Wang T, Zhou J, Hao X, Xu Y, Qiu J (2022) Improved African vulture optimization algorithm based on quasi-oppositional differential evolution operator. IEEE Access 10:95197\u201395218. https:\/\/doi.org\/10.1109\/ACCESS.2022.3203813","journal-title":"IEEE Access"},{"key":"2755_CR24","doi-asserted-by":"publisher","first-page":"1067189","DOI":"10.3389\/fpls.2023.1067189","volume":"14","author":"N Mandal","year":"2023","unstructured":"Mandal N, Adak S, Das DK, Sahoo RN, Mukherjee J, Kumar A, Chinnusamy V, Das B, Mukhopadhyay A, Rajashekara H, Gakhar S (2023) Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models. Front Plant Sci 14:1067189. https:\/\/doi.org\/10.3389\/fpls.2023.1067189","journal-title":"Front Plant Sci"},{"key":"2755_CR25","doi-asserted-by":"publisher","first-page":"35398","DOI":"10.1109\/ACCESS.2023.3263042","volume":"11","author":"E Moupojou","year":"2023","unstructured":"Moupojou E, Tagne A, Retraint F, Tadonkemwa A, Wilfried D, Tapamo H, Nkenlifack M (2023) Fieldplant: a dataset of field plant images for plant disease detection and classification with deep learning. IEEE Access 11:35398\u201335410. https:\/\/doi.org\/10.1109\/ACCESS.2023.3263042","journal-title":"IEEE Access"},{"key":"2755_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109795","volume":"120","author":"RK Patel","year":"2024","unstructured":"Patel RK, Chaudhary A, Chouhan SS, Pandey KK (2024) Mango leaf disease diagnosis using total variation filter based variational mode decomposition. Comput Electr Eng 120:109795. https:\/\/doi.org\/10.1016\/j.compeleceng.2024.109795","journal-title":"Comput Electr Eng"},{"key":"2755_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.108492","volume":"105","author":"SR Reddy","year":"2023","unstructured":"Reddy SR, Varma GS, Davuluri RL (2023) Resnet-based modified red deer optimization with DLCNN classifier for plant disease identification and classification. Comput Electr Eng 105:108492. https:\/\/doi.org\/10.1016\/j.compeleceng.2022.108492","journal-title":"Comput Electr Eng"},{"key":"2755_CR28","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.aiia.2023.11.001","volume":"11","author":"PI Ritharson","year":"2024","unstructured":"Ritharson PI, Raimond K, Mary XA, Robert JE, Andrew J (2024) Deeprice: a deep learning and deep feature based classification of rice leaf disease subtypes. Artificial Intell Agric 11:34\u201349. https:\/\/doi.org\/10.1016\/j.aiia.2023.11.001","journal-title":"Artificial Intell Agric"},{"key":"2755_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eja.2023.126884","volume":"148","author":"AA Salamai","year":"2023","unstructured":"Salamai AA, Ajabnoor N, Khalid WE, Ali MM, Murayr AA (2023) Lesion-aware visual transformer network for paddy diseases detection in precision agriculture. Eur J Agron 148:126884. https:\/\/doi.org\/10.1016\/j.eja.2023.126884","journal-title":"Eur J Agron"},{"issue":"12","key":"2755_CR30","doi-asserted-by":"publisher","first-page":"14955","DOI":"10.1007\/s10462-023-10517-0","volume":"56","author":"CK Sunil","year":"2023","unstructured":"Sunil CK, Jaidhar CD, Patil N (2023) Systematic study on deep learning-based plant disease detection or classification. Artif Intell Rev 56(12):14955\u201315052. https:\/\/doi.org\/10.1007\/s10462-023-10517-0","journal-title":"Artif Intell Rev"},{"key":"2755_CR31","doi-asserted-by":"publisher","first-page":"45377","DOI":"10.1109\/ACCESS.2023.3273317","volume":"11","author":"A Tabbakh","year":"2023","unstructured":"Tabbakh A, Barpanda SS (2023) A deep features extraction model based on the transfer learning model and vision transformer \u201ctlmvit\u201d for plant disease classification. IEEE Access 11:45377\u201345392. https:\/\/doi.org\/10.1109\/ACCESS.2023.3273317","journal-title":"IEEE Access"},{"issue":"1","key":"2755_CR32","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s41870-021-00817-5","volume":"14","author":"SK Upadhyay","year":"2022","unstructured":"Upadhyay SK, Kumar A (2022) A novel approach for rice plant diseases classification with deep convolutional neural network. Int J Inf Technol 14(1):185\u2013199. https:\/\/doi.org\/10.1007\/s41870-021-00817-5","journal-title":"Int J Inf Technol"},{"key":"2755_CR33","doi-asserted-by":"publisher","first-page":"1755","DOI":"10.7717\/peerj-cs.1755","volume":"10","author":"W Zheng","year":"2024","unstructured":"Zheng W, Lu S, Yang Y, Yin Z, Yin L (2024) Lightweight transformer image feature extraction network. PeerJ Comput Sci 10:1755. https:\/\/doi.org\/10.7717\/peerj-cs.1755","journal-title":"PeerJ Comput Sci"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02755-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02755-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02755-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T11:24:27Z","timestamp":1762514667000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02755-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,28]]},"references-count":33,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["2755"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02755-1","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2025,8,28]]},"assertion":[{"value":"6 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No datasets were generated or analysed during the current study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Data availability"}}]}}