[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121326-en":3,"doc-seo-121326-105":30,"detail-sidebar-cat-0-en-105":95},{"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},121326,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Analysis of Banana Plant Health Using Machine Learning Techniques","Banana production faces severe losses from diseases such as Black Sigatoka, Panama wilt, and Mosaic, making early detection on banana leaves a priority for protecting yield and farmers’ livelihoods. The study evaluates machine learning and deep learning pipelines for disease prediction and detection, covering image acquisition, preprocessing, segmentation, feature extraction, feature selection, and classification. It reviews limitations of standard models, especially sensitivity to scale and rotation, and proposes two fused-feature approaches using ANN with SIFT and ANN with HOG-LBP for improved performance.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nAnalysis of banana plant health using machine learning techniques  \nJoshva Devadas Thiagarajan1*, Siddharaj Vitthal Kulkarni1, Shreyas Anil Jadhav1, AyushAshish Waghe1, S. P. Raja1, Sivakumar Rajagopal2, Harshit Poddar2 & Shamala Subramaniam3  \nThe Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Thoughthe Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG)  \ncombined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presentsa promising avenue for research.  \nKeywords Banana industry, Disease detection, Agricultural farming, Machine learning algorithms, Banana leaf diseases, Automated systems  \nAgriculture functions as a fundamental pillar of any nation’s economy, and within this sector, the cultivation of bananas holds notable significance. In the fiscal year 2019, bananas made a substantial contribution of 346 billion Indian rupees to India’s economy, thereby solidifying its position as a critical food crop. Nevertheless, the industry encounters formidable challenges presented by diseases such as Black Sigatoka, Panama wilt, and Mosaic, resulting in reduced yields and impacting both farmers and the national economy. This paper underscores the imperative necessity for innovative methods in banana production to mitigate losses caused by diseases, with particular emphasis on early detection through the integration of cutting-edge technologies. The research highlights the role of machine learning and deep learning algorithms in the identification and classification of banana diseases. The process encompasses a series of steps, including image acquisition, pre-processing, segmentation, feature extraction, selection, and leaf classification. Multiple algorithms, including Convolutional Neural Network (CNN) for feature extra","cbCaimb5uAPyr625","https://ap.wps.com/l/cbCaimb5uAPyr625","pdf",3355026,1,23,"English","en",105,"# Introduction\n## Motivation and problem background\n## Disease impact and need for early detection\n# Methodology\n## Image processing pipeline\n## Feature extraction and classification approaches\n# Proposed approaches\n## ANN with SIFT\n## HOG-LBP with ANN\n# Discussion and future directions\n## Scale/rotation robustness challenges\n## Video dataset opportunities","[{\"question\":\"Why is early detection of banana leaf diseases emphasized?\",\"answer\":\"Diseases primarily affect banana plant leaves and can substantially reduce yields. Early identification helps safeguard production and mitigate losses for farmers and the economy.\"},{\"question\":\"What modeling pipeline is used for disease identification?\",\"answer\":\"The work outlines steps including image acquisition, preprocessing, segmentation, feature extraction, feature selection, and leaf classification, using different ML and deep learning components.\"},{\"question\":\"What limitation do existing models face according to the study?\",\"answer\":\"Existing models have difficulty with invariance to rotation and scale. The paper notes that CNN-based approaches may achieve accuracy but lack robustness to these transformations and are hard to combine with some feature-extraction methods.\"},{\"question\":\"How do the proposed models improve disease detection?\",\"answer\":\"Two alternative fused-feature approaches are suggested: ANN with SIFT to process extracted features via activation functions, and an ANN approach combining HOG and LBP features to represent local patterns and gradients for more robust identification.\"}]","Analysis of Banana Plant Health Using Machine Learning Techniques | PDF",1785735086,58,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"analysis-of-banana-plant-health-using-machine-learning-techniques","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/analysis-of-banana-plant-health-using-machine-learning-techniques/121326/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early detection of banana leaf diseases emphasized?","Question",{"text":75,"@type":76},"Diseases primarily affect banana plant leaves and can substantially reduce yields. Early identification helps safeguard production and mitigate losses for farmers and the economy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What modeling pipeline is used for disease identification?",{"text":80,"@type":76},"The work outlines steps including image acquisition, preprocessing, segmentation, feature extraction, feature selection, and leaf classification, using different ML and deep learning components.",{"name":82,"@type":73,"acceptedAnswer":83},"What limitation do existing models face according to the study?",{"text":84,"@type":76},"Existing models have difficulty with invariance to rotation and scale. The paper notes that CNN-based approaches may achieve accuracy but lack robustness to these transformations and are hard to combine with some feature-extraction methods.",{"name":86,"@type":73,"acceptedAnswer":87},"How do the proposed models improve disease detection?",{"text":88,"@type":76},"Two alternative fused-feature approaches are suggested: ANN with SIFT to process extracted features via activation functions, and an ANN approach combining HOG and LBP features to represent local patterns and gradients for more robust identification.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]