Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Chittaragi, Amoghavarsha; Patil, Balanagouda; Manjesh, M.; Sridhar, S.; Hurakadli, Manjunath S.; Praveenakumar, R.; Dharamveer, S. Duhan; Kumar, Anil; Kumar, Rakesh; Mohan, Man; Kasniya, Pawan Kumar Title A name given to the resource SARIMA-Random Forest Framework for Forecasting Anthracnose Severity in Bottle Gourd Under Variable Transplanting Dates Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Plant Pathology;Volume;75;Issue;1;Article No.;e70093; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1111/ppa.70093" target="_blank" rel="noreferrer noopener">https://doi.org/10.1111/ppa.70093</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021523092?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021523092?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chittaragi A., Department of Plant Pathology, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India, ICAR-KVK, Chintamani, University of Agricultural Sciences, Karnataka, Bangalore, India; Patil B., Department of Plant Pathology, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Karnataka, Shivamogga, India; Manjesh M., Department of Life Sciences, Christ University, Bengaluru, India; Sridhar S., Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Karnataka, Shivamogga, India; Hurakadli M.S., Department of Plant Pathology, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India; Praveenakumar R., ICAR-KVK, Chintamani, University of Agricultural Sciences, Karnataka, Bangalore, India; Dharamveer S.D., Department of Vegetable Sciences, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India; Kumar A., Department of Nematology, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India; Kumar R., Department of Plant Pathology, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India; Mohan M., Department of Plant Pathology, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India; Kasniya P.K., Department of Plant Pathology, College of Agriculture, Chaudhary Charan Singh Haryana Agricultural University, Haryana, Hisar, India Description An account of the resource Anthracnose, caused by Colletotrichum lagenarium, is an economically important disease affecting bottle gourd. This study aimed to evaluate the influence of transplanting time and weather parameters on anthracnose progression and to develop a forecasting framework using statistical and machine-learning models. Field experiments were conducted during the monsoon seasons of 2023 and 2024, with four transplanting dates: 1 June, 15 June, 1 July and 15 July. Disease severity was assessed weekly on leaves and fruits along with concurrent recording of weather data. Correlation and regression analyses revealed minimum temperature as the most influential weather variables, particularly during early transplanting dates. The regression models yielded the highest explanatory power for 1 June fruits (R2 = 0.675), while later transplanting dates showed reduced disease pressure and lower model accuracy. To capture seasonal trends and short-term predictability, Seasonal Autoregressive Integrated Moving Average (SARIMA) models with configuration (1,1,1) (1,1,1) [15] were applied. These models effectively forecasted disease progression, especially for July transplanting with lower mean squared errors (MSE &lt; 200). Time series decomposition showed strong seasonal and trend components in early sowings, while cross-correlation analysis confirmed a 13-week lag between weather triggers and disease expression. This study emphasises the importance of transplanting time in disease development and demonstrates the potential of combining SARIMA and random forest models for developing weather-based early warning systems. These findings contribute to climate-resilient crop protection strategies and can aid in timely decision-making for anthracnose management in bottle gourd and related cucurbits. 2025 British Society for Plant Pathology. Subject The topic of the resource artificial intelligence; climate-resilient disease management; disease forecasting; epidemiological modelling; machine learning; time series modelling Publisher An entity responsible for making the resource available John Wiley and Sons Inc Relation A related resource ISSN: 320862; CODEN: PLPAA Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Format The file format, physical medium, or dimensions of the resource online