[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125751-en":3,"doc-seo-125751-105":30,"detail-sidebar-cat-0-en-105":90},{"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":20,"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},125751,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Plant Stem Disease Detection Using Machine Learning Approaches","Rapid identification of plant stem diseases enables timely intervention and reduces crop loss. This work presents an automated stem disease detection and classification approach that combines digital image processing with machine learning. A dataset of 3,789 images covering five categories—stem rot, gummy blight, blackleg, didymella, and healthy—is used with an 80/20 training-testing split. Experiments run on Google Colab, Jupyter Notebook, and OpenCV, comparing SVM, Random Forest, KNN, and Impact Learning via accuracy, precision, recall, and F1.","Plant stem disease detection using machine learning approaches  \nJalal Uddin Md Akbar, Syafiq Fauzi Kamarulzaman, Ekramul Haque Tusher Faculty of Computing, Universiti Malaysia Pahang, Pahang-26600, Malaysia  \nABSTRACT  \nThe rapid identification of plant stem diseases is crucial for implementing timely intervention and minimizing crop loss. While previous research has primarily focused on leaf-based disease detection, this paper proposes an automated stem disease detection and classification model using digital image processing and machine learning techniques. A dataset comprising 3789 images of diseased and healthy stems, categorized into five classes (stem rot, gummy blight, blackleg, didymella, and healthy), was split into training (80%) and testing (20%) sets. Our experiments were conducted on multiple platforms, including Google Colab, Jupyter Notebook, and OpenCV, and compared the performance of four classification techniques: Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), and Impact Learning. Various performance metrics, such as accuracy, precision, recall, and F1 score, were employed to evaluate the classifiers. Our findings reveal that SVM outperformed the other classifiers, achieving an average accuracy of 87%, followed by Random Forest (79%), KNN (75%), and Impact Learning (70%) . This research offers valuable insights for farmers and the agricultural industry, paving the way for future studies exploring disease detection in other plant parts using similar techniques.  \nKEYWORDS  \nPlant stem diseases; Disease detection; Digital image processing; Machine learning; Support vector machine; Random forest; K-Nearest neighbor; Impact Learning; Classification techniques; Accuracy; Precision; Recall; F1 score; Agricultural applications  \nACKNOWLEDGMENT  \nThis research is supported by Universiti Malaysia Pahang Product Development Grant UIC220803 .","cbCaif6T2ncRdJCc","https://ap.wps.com/l/cbCaif6T2ncRdJCc","pdf",142439,1,2,"English","en",105,"# Abstract\n# Dataset and Methodology\n## Classes and Train/Test Split\n## Platforms and Classification Techniques\n# Evaluation Metrics\n# Results and Conclusion","[{\"question\":\"Why is automated plant stem disease detection important?\",\"answer\":\"Rapid detection supports timely intervention and helps minimize crop loss.\"},{\"question\":\"What dataset and classification categories are used in this study?\",\"answer\":\"The study uses 3,789 images and five classes: stem rot, gummy blight, blackleg, didymella, and healthy stems.\"},{\"question\":\"Which classification technique achieved the best performance?\",\"answer\":\"Support Vector Machine (SVM) performed best with an average accuracy of 87%, outperforming Random Forest, KNN, and Impact Learning.\"}]","Plant Stem Disease Detection Using Machine Learning Approaches | PDF",1785901019,5,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"plant-stem-disease-detection-using-machine-learning-approaches","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/plant-stem-disease-detection-using-machine-learning-approaches/125751/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is automated plant stem disease detection important?","Question",{"text":74,"@type":75},"Rapid detection supports timely intervention and helps minimize crop loss.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What dataset and classification categories are used in this study?",{"text":79,"@type":75},"The study uses 3,789 images and five classes: stem rot, gummy blight, blackleg, didymella, and healthy stems.",{"name":81,"@type":72,"acceptedAnswer":82},"Which classification technique achieved the best performance?",{"text":83,"@type":75},"Support Vector Machine (SVM) performed best with an average accuracy of 87%, outperforming Random Forest, KNN, and Impact Learning.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":46,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":29,"slug":136},19,"General","general"]