[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124836-en":3,"doc-seo-124836-105":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},124836,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",6,"Technology","Cotton Crop Leaf Disease Detection System Using Machine Learning Approaches to Improve Efficiency","India’s agriculture depends heavily on accurate, timely diagnosis of crop diseases that can rapidly reduce yield and cause major financial losses for farmers. Traditional visual inspection requires highly experienced specialists and involves time-consuming procedures, often leading to misdiagnosis and inappropriate pesticide use. The study evaluates classical machine learning models (SVM, random forest) against CNN approaches (Inceptionv3, VGG16, ResNet50) using transfer learning and data augmentation, measuring accuracy, precision, recall, and training time.","Cotton Crop Leaf Disease Detection System Using Machine Learning Approaches to Improve  \nEfficiency”  \nKuldeep Pal1, Ankur Rana 2  \n1 Department of Computer Application, Quantum University, Roorkee  \n2Assistant Professor, Department of Computer Application, Quantum University, Roorkee  \nAbstract  \nIndia's economy heavily depends on agriculture. Over 70% of people in India make their living from agriculture. Accurate and prompt diagnosis of illnesses that harm crops is one of the biggest challenges facing the agriculture sector. Diseases affect crop quality and have the power to destroy whole hectares of agricultural output, costing farmers a lot of money.  \nCurrent diagnosis methods need the presence of highly experienced professionals and take a lot of time to examine the damaged crop, understand the symptoms, identify the illness, and provide effective remedies. Due to the limitations of these methodologies, researchers are now looking for other approaches to early illness detection and classification. Addressing food security may be facilitated by smart farming and adequate infrastructure.  \nIn recent years, machine learning has demonstrated tremendous potential in identifying and categorising trends in linked academic disciplines. The goal of the current study is to evaluate the accuracy, precision, recall, and training time of traditional machine learning techniques like the Support Vector Machine (SVM) and random forest against the performance of convolutional neural network (CNN) methods and architectures like Inceptionv3, VGG16, and RasNet50 with data augmentation and transfer learning. The models were trained with the use of a manually gathered database from a farm and a government organisation, which had four distinct classes of photos, including healthy plants. The highest performing model was the Inceptionv3 architecture of CNN with transfer learning, which achieved an overall accuracy of 94 percent and met the demand for a more reliable and effective classification model. Additionally, when the quantity of training data rose, it was shown that the performance of the developed models increased.  \nThe outcomes obtained using transfer learning algorithms on CNN architectures are extremely encouraging, and they may be further refined to create a thorough leaf disease diagnosis system that can function in a real-world environment. As a result, it may enable the agricultural community to recognise problems and start prompt treatment without the intervention of qualified specialists.  \nKeywords: Transfer Learning, CNN, SVM, Random Forest, Machine Learning  \nIntroduction  \nMany South Asian countries, including Bangladesh, China, and India, rely heavily on agriculture (Patil & Burkpalli, 2021; Nadiruzzaman et al., 2021) . The escalation of crop diseases in specific regions, attributed to climate change and global warming, has resulted in a notable decrease in agricultural output. One crop that is particularly important to the economy is cotton, also known as the \"king of fibres\" or\"white gold\" (Khairnar & Goje, 2020) . Global trade is currently valued at 40 billion US dollars, but by the end of 2030, that value is predicted to climb to 60 billion US dollars (Meyer et al., 2023) . Bangladesh, as the second-largest provider of ready-made clothing globally, has recently exported garments worth 20 billion US dollars worldwide (Mohiuddin, 2008) . Notably, cotton plays a crucial role as a raw material in various industrial sectors.  \nSpinning mills are essential to the manufacture of yarn, which is a vital component utilised in our apparel  \nfactories. Bangladesh is endowed with favourable weather, lots of water resources, and rich soil, which makes it possible to use about 500,000 hectares of land for cotton production. Even though cotton is one of the most valuable crops in the world, it is constantly threatened by a variety of pests, illnesses, and climate variations like floods, droughts, and extreme temperatures. Yarn is a ","cbCaih2GGmZNcAAu","https://ap.wps.com/l/cbCaih2GGmZNcAAu","pdf",715407,1,13,"English","en",105,"# Abstract\n# Introduction\n## Background and motivation\n## Research questions and challenges\n## Automated detection workflow\n## Feature engineering and image processing","[{\"question\":\"Why is early cotton leaf disease diagnosis important for agriculture?\",\"answer\":\"Crop diseases reduce quality and can destroy agricultural output, creating significant losses. Early and reliable diagnosis supports better treatment decisions and improves efficiency in farming.\"},{\"question\":\"What machine learning and deep learning models are compared in the study?\",\"answer\":\"The study compares traditional methods such as SVM and random forest with CNN architectures including Inceptionv3, VGG16, and ResNet50 using transfer learning and data augmentation.\"},{\"question\":\"How is model performance evaluated and what is the best result reported?\",\"answer\":\"Performance is assessed using accuracy, precision, recall, and training time. The top-performing model is Inceptionv3 with transfer learning, achieving about 94% overall accuracy.\"}]","Cotton Crop Leaf Disease Detection System Using Machine Learning Approaches to Improve Efficiency | PDF",1785894898,33,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"cotton-crop-leaf-disease-detection-system-using-machine-learning-approaches-to-improve-efficiency","",{"@graph":36,"@context":85},[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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/cotton-crop-leaf-disease-detection-system-using-machine-learning-approaches-to-improve-efficiency/124836/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early cotton leaf disease diagnosis important for agriculture?","Question",{"text":75,"@type":76},"Crop diseases reduce quality and can destroy agricultural output, creating significant losses. Early and reliable diagnosis supports better treatment decisions and improves efficiency in farming.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning and deep learning models are compared in the study?",{"text":80,"@type":76},"The study compares traditional methods such as SVM and random forest with CNN architectures including Inceptionv3, VGG16, and ResNet50 using transfer learning and data augmentation.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated and what is the best result reported?",{"text":84,"@type":76},"Performance is assessed using accuracy, precision, recall, and training time. The top-performing model is Inceptionv3 with transfer learning, achieving about 94% overall accuracy.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},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":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]