[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121536-en":3,"doc-seo-121536-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121536,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","ADVANCED MACHINE LEARNING-BASED ECO-INTEGRATED MODEL FOR PREDICTING LATE BLIGHT IN MULTIPLE CULTIVATION SYSTEMS","This paper develops a machine learning–driven framework to analyze and predict potato late blight caused by Phytophthora infestans across two cultivation systems—ecological and integrated—using six potato varieties. Statistical analysis with two-factor ANOVA and Tukey’s HSD evaluates effects of system, variety, and year. An Eco-Integrated Model is proposed to improve prediction accuracy and interpretability by combining SMOTE for class imbalance, SHAP for feature importance, and a CatBoost classifier. Data collected from 2018–2020 support scalable, explainable forecasting that informs sustainable disease management.","[https://doi.org/10.2298/FUEE2601219B](https://doi.org/10.2298/FUEE2601219B)  \nOriginal scientific paper  \nADVANCED MACHINE LEARNING-BASED ECO-INTEGRATED MODEL FOR PREDICTING LATE BLIGHT IN MULTIPLE CULTIVATION SYSTEMS  \nParama Bagchi1, Barbara Sawicka2, Zoran Stamenkovic3,4, Piotr Barbaś5, Piotr Pszczółkowski6, Dusan Markovic7, Debotosh Bhattacharjee8  \n1Department ofCSE, RCC Institute of Information Technology, Kolkata, India 2Department of Plant Production Technology and Commodities Science, University of Life Sciences in Lublin, 20-950 Lublin, Poland 3Institute of Computer Science, University of Potsdam, 14476 Potsdam, Germany 4IHP, Leibniz-Institut für Innovative Mikroelektronik, 15236 Frankfurt (Oder), Germany 5Department of Potato Agronomy, Plant Breeding and Acclimatization Institute-National Research Institute, Branch of Jadwisin, Jadwisin, 05-140 Serock, Poland 6Research Centre for Cultivar Testing, Słupia Wielka 34, 63-022 Słupia Wielka, Poland 7Faculty of Technical Sciences Čačak, University of Kragujevac, Serbia  \n8Department ofCSE, Jadavpur University, Kolkata, India  \nORCID ID: Parama Bagchi  [https://orcid.org/0000-0002-9725-9582](https://orcid.org/0000-0002-9725-9582)  \nBarbara Sawicka  [https://orcid.org/0000-0002-8183-7624](https://orcid.org/0000-0002-8183-7624)  \nZoran Stamenkovic  [https://orcid.org/0000-0002-6078-413X](https://orcid.org/0000-0002-6078-413X)[ ](https://orcid.org/0000-0002-6078-413X)Piotr Barbaś  [https://orcid.org/0000-0001-7830-0116](https://orcid.org/0000-0001-7830-0116)[ ](https://orcid.org/0000-0001-7830-0116)Piotr Pszczółkowski  [https://orcid.org/0000-0002-5907-1984](https://orcid.org/0000-0002-5907-1984)  \nDusan Markovic  [https://orcid.org/0000-0002-7270-6702](https://orcid.org/0000-0002-7270-6702)  \nDebotosh Bhattacharjee  [https://orcid.org/0000-0002-1163-6413](https://orcid.org/0000-0002-1163-6413)  \nAbstract. This paper presents a machine learning–driven framework for analyzing and predicting potato late blight (caused by Phytophthora infestans) across two distinct cultivation systems—ecological and integrated—using six potato varieties. Traditional statistical methods, including a two-factor Analysis of Variance (ANOVA) and Tukey’s Honest Significant Difference (HSD) test, were applied to assess the effects of cultivation systems, potato varieties, and year. To enhance predictive accuracy and model interpretability, an advanced machine learning pipeline, termed the Eco-Integrated Model, was developed. This model integrates SMOTE (Synthetic Minority Oversampling Technique) for handling class imbalance, SHAP (SHapley Additive xPlanations) for interpretability and feature importance analysis, and the CatBoost classifier for robust, high-performance prediction. The dataset, collected over three years (2018–2020), includes multi-varietal and system-specific records of late blight incidence for both ecological integrated-based data, serving as inputfor model training and evaluation. The  \nReceived June 04, 2025; revised September 17, 2025; accepted October 07, 2025 Corresponding author: Parama Bagchi  \nDepartment ofCSE, RCC Institute of Information Technology, Kolkata, India [E-mail: paramabagchi@gmail.com](E-mail: paramabagchi@gmail.com)  \nproposed Eco-Integrated Model demonstrated high predictive capability, revealing that integrated cultivation systems are generally more effective at suppressing disease progression. Moreover, substantial varietal differences were identified in late blight susceptibility, as highlighted by both statistical and machine learning analyses. These findings underline the value of incorporating explainable, data-driven approaches into plant disease forecasting. The Eco-Integrated Model offers a scalable, interpretable, and accurate predictive solution, contributing to precision agriculture practices and supporting evidence-based decision-making for sustainable potato production and disease management strategies.  \nKey words: Potato Late Blight, Cultivati","cbCaipfhBE6tlfb5","https://ap.wps.com/l/cbCaipfhBE6tlfb5","pdf",1273950,1,38,"English","en",105,"# Introduction\n## Agriculture systems\n## Potato cultivation management\n# Methods\n## Machine learning framework and components\n## Statistical analysis approach\n# Results\n## Predictive performance and interpretability\n## Effects of cultivation systems and varieties\n# Conclusion","[{\"question\":\"What problem does the Eco-Integrated Model address?\",\"answer\":\"It targets prediction of potato late blight caused by Phytophthora infestans across ecological and integrated cultivation systems using multi-varietal data.\"},{\"question\":\"How do statistical methods contribute to the study?\",\"answer\":\"Two-factor ANOVA and Tukey’s HSD test evaluate the effects of cultivation systems, potato varieties, and year on late blight incidence.\"},{\"question\":\"Which machine learning techniques improve accuracy and explainability?\",\"answer\":\"The pipeline uses SMOTE to handle class imbalance, SHAP for interpretability and feature importance analysis, and a CatBoost classifier for robust prediction.\"}]","ADVANCED MACHINE LEARNING-BASED ECO-INTEGRATED MODEL FOR PREDICTING LATE BLIGHT IN MULTIPLE CULTIVATION SYSTEMS | 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