{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T12:19:15Z","timestamp":1781266755446,"version":"3.54.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:00:00Z","timestamp":1776470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T00:00:00Z","timestamp":1776470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Plant diseases pose a major threat to global food security, particularly in agricultural regions such as Egypt. This study presents an integrated framework that combines deep learning (DL) with Geographic Information Systems (GIS) for the automated diagnosis and spatial monitoring of tomato and potato diseases. Three deep learning models were developed and evaluated: DenseNet169, MobileNetV2, and a Custom-CNN baseline. Comparative experiments on the test dataset showed that DenseNet169 achieved the highest evaluation accuracy of 98.24%, followed by MobileNetV2 with 94.66%, while the Custom-CNN obtained a lower accuracy of 82.60%, reflecting the limitations of shallow architectures trained from scratch. Owing to its superior performance, DenseNet169 was selected for integration with the GIS module. The GIS component geotags disease predictions and visualizes outbreaks through interactive crop-specific layers, heatmaps, and temporal tracking, enabling early detection and targeted interventions. This spatial\u2013temporal integration allows stakeholders to monitor disease progression, assess risk under varying environmental conditions, and optimize resource allocation for precision agriculture. The final system also incorporates a bilingual web interface (Arabic\/English) enhanced with Grad-CAM visualizations, which improve interpretability and build trust among local farming communities. Field validation conducted in Beheira Governorate, Egypt, confirmed the accuracy and practical utility of the system in real-world agricultural settings. By integrating AI-based diagnostics with geospatial intelligence, the proposed framework provides a scalable and sustainable decision-support tool, offering a valuable pathway toward data-driven plant disease management in developing regions.<\/jats:p>","DOI":"10.1007\/s44163-025-00811-x","type":"journal-article","created":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T07:28:54Z","timestamp":1776497334000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep learning and GIS integration for plant disease diagnosis and management"],"prefix":"10.1007","volume":"6","author":[{"given":"Waleed","family":"Maged","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdelrahman","family":"Elsayed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rehab","family":"Mahmoud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mostafa","family":"Thabet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,18]]},"reference":[{"key":"811_CR1","unstructured":"Food and Agriculture Organization of the United Nations (FAO). Plant health and food security. FAO, Rome. 2017. Available at: https:\/\/www.fao.org\/3\/I7829EN\/i7829en.pdf"},{"issue":"3","key":"811_CR2","first-page":"75","volume":"18","author":"A Mohamed","year":"2020","unstructured":"Mohamed A, Abdel-Gaber S, Nasr M, Hazman M. An intelligent approach to mitigate the effects of climate change and insects on crops. Int J Comput Sci Inf Secur. 2020;18(3):75\u20139.","journal-title":"Int J Comput Sci Inf Secur"},{"key":"811_CR3","doi-asserted-by":"publisher","unstructured":"Venkatesh N, Nagaraju Y, Sahana TS, Swetha S, Hegde SU. Transfer learning-based convolutional neural network model for classification of mango leaves infected by anthracnose. In: 2020 IEEE Int Conf Innov Technol (INOCON), pp. 1\u20137. IEEE. 2020. https:\/\/doi.org\/10.1109\/INOCON50539.2020.9298269","DOI":"10.1109\/INOCON50539.2020.9298269"},{"key":"811_CR4","doi-asserted-by":"publisher","unstructured":"Peyal HI, Shahriar SM, Sultana A, Jahan I, Mondol MH. Detection of tomato leaf diseases using transfer learning architectures: A comparative analysis. In: 2021 Int Conf Autom Control Mechatron Ind 4.0 (ACMI), pp. 1\u20136. IEEE. 2021. https:\/\/doi.org\/10.1109\/ACMI53878.2021.9528199","DOI":"10.1109\/ACMI53878.2021.9528199"},{"key":"811_CR5","first-page":"509","volume-title":"Precision agriculture and sustainable crop production","author":"R Ranjan","year":"2020","unstructured":"Ranjan R, Vinayak S. Application of remote sensing and GIS in plant disease management. In: Chourasia HK, Acharya K, Singh VK, editors. Precision agriculture and sustainable crop production. New Delhi: Today & Tomorrow\u2019s Printers and; 2020. pp. 509\u201322."},{"key":"811_CR6","unstructured":"Hammonds T. Use of GIS in agriculture. Cornell Small Farms Program, pp. 1\u201313. 2017. Available at: https:\/\/smallfarms.cornell.edu\/2017\/04\/use-of-gis\/"},{"key":"811_CR7","doi-asserted-by":"publisher","first-page":"e70384","DOI":"10.1002\/eng2.70384","volume":"7","author":"G Suresh","year":"2025","unstructured":"Suresh G, Narla VL, Tummalapalli G, Peddinti AS, Al-Mdallal QM. AgroDetect: smart plant pathology from leaf images. Eng Rep. 2025;7:e70384. https:\/\/doi.org\/10.1002\/eng2.70384.","journal-title":"Eng Rep"},{"key":"811_CR8","doi-asserted-by":"publisher","unstructured":"Nanda S, Yadav RP. Traditional CNN-based plant disease classification using the PlantVillage dataset. In: Nanda S, Yadav RP, editors. Data Science and Intelligent Computing Techniques. Computing & Intelligent Systems, SCRS, India; 2023. pp. 47\u201354. https:\/\/doi.org\/10.56155\/978-81-955020-2-8-5","DOI":"10.56155\/978-81-955020-2-8-5"},{"issue":"2","key":"811_CR9","doi-asserted-by":"publisher","first-page":"51","DOI":"10.35784\/acs-2022-13","volume":"18","author":"M Bakr","year":"2022","unstructured":"Bakr M, et al. Tomato disease detection model based on densenet and transfer learning. Appl Comput Sci. 2022;18(2):51\u201360.","journal-title":"Appl Comput Sci"},{"key":"811_CR10","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.compag.2018.03.032","volume":"161","author":"EC Too","year":"2019","unstructured":"Too EC, Yujian L, Njuki S, Yingchun L. A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric. 2019;161:272\u20139. https:\/\/doi.org\/10.1016\/j.compag.2018.03.032.","journal-title":"Comput Electron Agric"},{"issue":"2","key":"811_CR11","doi-asserted-by":"publisher","first-page":"345","DOI":"10.32604\/iasc.2023.025897","volume":"36","author":"S Poornima","year":"2023","unstructured":"Poornima S, et al. Hybrid convolutional neural network for plant diseases prediction. Intell Autom Soft Comput. 2023;36(2):345\u201352. https:\/\/doi.org\/10.32604\/iasc.2023.025897.","journal-title":"Intell Autom Soft Comput"},{"issue":"3","key":"811_CR12","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.inpa.2020.02.002","volume":"7","author":"M Ji","year":"2020","unstructured":"Ji M, Zhang L, Wu Q. Automatic grape leaf disease identification via unitedmodel based on multiple convolutional neural networks. Inf Process Agric. 2020;7(3):418\u201326. https:\/\/doi.org\/10.1016\/j.inpa.2020.02.002.","journal-title":"Inf Process Agric"},{"issue":"12","key":"811_CR13","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.3390\/electronics10121388","volume":"10","author":"SM Hassan","year":"2021","unstructured":"Hassan SM, et al. Identification of plant-leaf diseases using CNN and transfer-learning approach. Electronics. 2021;10(12):1388. https:\/\/doi.org\/10.3390\/electronics10121388.","journal-title":"Electronics"},{"key":"811_CR14","doi-asserted-by":"publisher","unstructured":"Hong H, Lin J, Huang F. Tomato disease detection and classification by deep learning. In: 2020 Int Conf Big Data Artif Intell IoT Eng (ICBAIE). IEEE. 2020. https:\/\/doi.org\/10.1109\/ICBAIE49996.2020.9339048","DOI":"10.1109\/ICBAIE49996.2020.9339048"},{"issue":"8","key":"811_CR15","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.3390\/agriculture12081192","volume":"12","author":"B Tugrul","year":"2022","unstructured":"Tugrul B, Elfatimi E, Eryigit R. Convolutional neural networks in detection of plant leaf diseases: a review. Agriculture. 2022;12(8):1192. https:\/\/doi.org\/10.3390\/agriculture12081192.","journal-title":"Agriculture"},{"issue":"6","key":"811_CR16","first-page":"2449","volume":"19","author":"WM Haggag","year":"2023","unstructured":"Haggag WM, Ali RR, Al-Ansary NA. Geographic information systems and remote sensing: innovative tools for plant health. Int J Agric Technol. 2023;19(6):2449\u201364.","journal-title":"Int J Agric Technol"},{"issue":"11","key":"811_CR17","first-page":"45","volume":"3","author":"GD Yashkumar","year":"2023","unstructured":"Yashkumar GD, Ayushi T, et al. Application of remote sensing and GIS in precision agriculture. J Agric Innov. 2023;3(11):45\u201354.","journal-title":"J Agric Innov"},{"issue":"2","key":"811_CR18","doi-asserted-by":"publisher","first-page":"2546","DOI":"10.46932\/sfjdv3n2-150","volume":"3","author":"DB Durango","year":"2022","unstructured":"Durango DB. Crops pests and diseases management system using android and GIS platform for the department of Agriculture, Province of Samar. South Fla J Dev. 2022;3(2):2546\u201353. https:\/\/doi.org\/10.46932\/sfjdv3n2-150.","journal-title":"South Fla J Dev"},{"issue":"1","key":"811_CR19","doi-asserted-by":"publisher","first-page":"012002","DOI":"10.1088\/1755-1315\/1051\/1\/012002","volume":"1051","author":"MSA Baharim","year":"2022","unstructured":"Baharim MSA, et al. A review: progression of remote sensing (RS) and geographical information system (GIS) applications in oil palm management and sustainability. IOP Conf Ser Earth Environ Sci. 2022;1051(1):012002. https:\/\/doi.org\/10.1088\/1755-1315\/1051\/1\/012002.","journal-title":"IOP Conf Ser Earth Environ Sci"},{"issue":"4","key":"811_CR20","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1007\/s42360-021-00334-2","volume":"74","author":"N Patel","year":"2021","unstructured":"Patel N, Singh SK, Mishra D. Precision agriculture and Geospatial techniques for sustainable disease control. Indian Phytopathol. 2021;74(4):875\u201384. https:\/\/doi.org\/10.1007\/s42360-021-00334-2.","journal-title":"Indian Phytopathol"},{"issue":"2","key":"811_CR21","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s42360-021-00311-9","volume":"74","author":"DP Roberts","year":"2021","unstructured":"Roberts DP, et al. Precision agriculture and Geospatial techniques for sustainable disease control. Indian Phytopathol. 2021;74(2):287\u2013305. https:\/\/doi.org\/10.1007\/s42360-021-00311-9.","journal-title":"Indian Phytopathol"},{"key":"811_CR22","first-page":"1","volume-title":"Plant disease: management strategies","author":"MU Charaya","year":"2021","unstructured":"Charaya MU, et al. Plant disease forecasting: past practices to emerging technologies. In: Nehra S, editor. Plant disease: management strategies. Rajasthan, India: Agrobios Research; 2021. pp. 1\u201330."},{"issue":"12","key":"811_CR23","first-page":"1","volume":"8","author":"A Anwer","year":"2019","unstructured":"Anwer A, Singh G. Geo-spatial technology for plant disease and insect pest management. Bull Environ Pharm Life Sci. 2019;8(12):1\u201312.","journal-title":"Bull Environ Pharm Life Sci"},{"issue":"1","key":"811_CR24","doi-asserted-by":"publisher","first-page":"012018","DOI":"10.1088\/1755-1315\/169\/1\/012018","volume":"169","author":"NM Sabtu","year":"2018","unstructured":"Sabtu NM, Idris NH, Ishak MHI. The role of Geospatial technology in plant pests and diseases: an overview. IOP Conf Ser Earth Environ Sci. 2018;169(1):012018. https:\/\/doi.org\/10.1088\/1755-1315\/169\/1\/012018.","journal-title":"IOP Conf Ser Earth Environ Sci"},{"issue":"9","key":"811_CR25","doi-asserted-by":"publisher","first-page":"2052","DOI":"10.3390\/agronomy14092052","volume":"14","author":"A Rana","year":"2024","unstructured":"Rana A, Sharma R, Kumar V, et al. Automated annotation and segmentation frameworks using transfer learning for multi-date aerial image analysis. Agronomy. 2024;14(9):2052. https:\/\/doi.org\/10.3390\/agronomy14092052.","journal-title":"Agronomy"},{"issue":"2","key":"811_CR26","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/BF02295996","volume":"12","author":"Q McNemar","year":"1947","unstructured":"McNemar Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika. 1947;12(2):153\u20137. https:\/\/doi.org\/10.1007\/BF02295996.","journal-title":"Psychometrika"},{"issue":"2","key":"811_CR27","doi-asserted-by":"publisher","first-page":"70384","DOI":"10.1007\/s10278-025-01518-2","volume":"38","author":"X Li","year":"2025","unstructured":"Li X. Enhancing statistical robustness in AI-based diagnostic model evaluation: practical standards for clinical imaging research. J Digit Imaging. 2025;38(2):70384. https:\/\/doi.org\/10.1007\/s10278-025-01518-2.","journal-title":"J Digit Imaging"},{"key":"811_CR28","doi-asserted-by":"publisher","first-page":"12345","DOI":"10.1038\/s41598-024-66989-9","volume":"14","author":"S El-Naggar","year":"2024","unstructured":"El-Naggar S, El-Shafai W, El-Sayed H. Enhancing agriculture through real-time grape leaf disease classification via an edge device with a lightweight CNN architecture and Grad-CAM. Sci Rep. 2024;14:12345. https:\/\/doi.org\/10.1038\/s41598-024-66989-9.","journal-title":"Sci Rep"},{"key":"811_CR29","doi-asserted-by":"publisher","first-page":"108123","DOI":"10.1016\/j.compag.2025.108123","volume":"212","author":"K Murugesan","year":"2025","unstructured":"Murugesan K, et al. GradCAM-PestDetNet: a deep learning-based hybrid model with explainable AI for pest detection and classification. Comput Electron Agric. 2025;212:108123. https:\/\/doi.org\/10.1016\/j.compag.2025.108123.","journal-title":"Comput Electron Agric"},{"key":"811_CR30","volume-title":"The study of plant disease epidemics","author":"LV Madden","year":"2007","unstructured":"Madden LV, Hughes G, van den Bosch F. The study of plant disease epidemics. St. Paul, MN: American Phytopathological Society; 2007."},{"key":"811_CR31","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.agrformet.2017.06.008","volume":"246","author":"S Bregaglio","year":"2017","unstructured":"Bregaglio S, Donatelli M, Confalonieri R. Development and evaluation of a dynamic model for predicting downy mildew on grapevine. Agric Meteorol. 2017;246:139\u201350. https:\/\/doi.org\/10.1016\/j.agrformet.2017.06.008.","journal-title":"Agric Meteorol"},{"issue":"7","key":"811_CR32","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1094\/PHYTO-07-19-0268-R","volume":"110","author":"M Meyer","year":"2020","unstructured":"Meyer M, Cox JA, et al. Spatial scaling of plant disease spread. Phytopathology. 2020;110(7):1281\u201392. https:\/\/doi.org\/10.1094\/PHYTO-07-19-0268-R.","journal-title":"Phytopathology"},{"key":"811_CR33","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1146\/annurev.phyto.44.070505.143420","volume":"44","author":"KA Garrett","year":"2006","unstructured":"Garrett KA, Dendy SP, Frank EE, et al. Climate change effects on plant disease: genomes to ecosystems. Annu Rev Phytopathol. 2006;44:489\u2013509. https:\/\/doi.org\/10.1146\/annurev.phyto.44.070505.143420.","journal-title":"Annu Rev Phytopathol"},{"key":"811_CR34","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1146\/annurev.phyto.42.040803.140427","volume":"42","author":"MJ Jeger","year":"2004","unstructured":"Jeger MJ. Analysis of disease progress as a basis for evaluating disease management practices. Annu Rev Phytopathol. 2004;42:61\u201382. https:\/\/doi.org\/10.1146\/annurev.phyto.42.040803.140427.","journal-title":"Annu Rev Phytopathol"},{"key":"811_CR35","doi-asserted-by":"publisher","first-page":"70384","DOI":"10.1007\/s10278-025-01518-2","volume":"38","author":"X Li","year":"2025","unstructured":"Li X, Zhang Y, Chen J. Deep learning-based comparative evaluation of medical image classification models: statistical and graphical standards for reproducibility. J Digit Imaging. 2025;38:70384. https:\/\/doi.org\/10.1007\/s10278-025-01518-2.","journal-title":"J Digit Imaging"},{"issue":"1","key":"811_CR36","doi-asserted-by":"publisher","first-page":"012020","DOI":"10.1088\/1742-6596\/2200\/1\/012020","volume":"2200","author":"X Sun","year":"2022","unstructured":"Sun X, Li G, et al. Research on plant disease identification based on CNN. J Phys Conf Ser. 2022;2200(1):012020. https:\/\/doi.org\/10.1088\/1742-6596\/2200\/1\/012020.","journal-title":"J Phys Conf Ser"},{"issue":"12","key":"811_CR37","first-page":"2106","volume":"12","author":"S Kumar","year":"2021","unstructured":"Kumar S, Chaudhary V, Chandra SK. Plant disease detection using CNN. Turk J Comput Math Educ. 2021;12(12):2106\u201312.","journal-title":"Turk J Comput Math Educ"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00811-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00811-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00811-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T07:28:58Z","timestamp":1776497338000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00811-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,18]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["811"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00811-x","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,18]]},"assertion":[{"value":"26 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"344"}}