[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122337-en":3,"doc-seo-122337-105":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},122337,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine-Learning Based Plant Health Monitoring - Thesis","Machine-learning based plant health monitoring uses image data and convolutional neural network methods to classify plant leaf conditions. The work builds and trains a classification model using datasets such as PlantVillage and a Taiwan tomato leaves collection, applying dataset preparation steps including shuffling, splitting, resizing, normalization, and data augmentation. A web application is designed to collect data, run predictions, and present results. Experiments test responsiveness, confidence, accuracy, and evaluation behavior across folders of different image sizes, supported by charts and visualizations.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nMACHINE-LEARNING BASED PLANT HEALTH  \nMONITORING  \nA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science  \nby  \nManoj Nizampatnam  \nDecember 2023  \nThe thesis of Manoj Nizampatnam is approved:  \n\n| Taehyung Wang, Ph.D. |\n| --- |\n| Robert McIlhenny, Ph.D. |\n\nXunfei Jiang, Ph.D., Chair  \n\n| Date |\n| --- |\n| Date |\n\nDate  \nCalifornia State University, Northridge  \nAcknowledgements  \nI would like to express my deepest gratitude to professor Xunfei Jiang for serving as my thesis chair and guiding me through every step of my project. I would also like to thank the computer science department for providing me with the opportunity of thesis. I would like to thank professor Robert McIlhenny and professor Taehyung George Wang for agreeing to serve as my defence committee members. Lastly I would also like to thank professor Bingbing Li for providing guidance throughout the project wherever necessary.  \nTable of Contents  \nSignature page ii  \nAcknowledgements iii  \nList of Figures vi Abstract viii 1 Introduction 1  \n2 Related Works 2  \n2.1 Related Works ................................. 2  \n2.2 Plant Monitoring ............................... 2  \n2.3 Object Detection ............................... 2  \n2.4 Plant Health Classification .......................... 3  \n2.5 Datasets for Plant Leavess .......................... 4  \n3 Classification model 6  \n3.1 Classification Models ............................. 6  \n3.2 Dataset .................................... 6  \n3.2.1 Plantvillage .............................. 6  \n3.2.2 Taiwan Tomato Leaves ........................ 6  \n3.3 Classification Algorithms ........................... 7  \n3.3.1 Import data into tensorflow dataset object .............. 7  \n3.3.2 Shuffle ................................ 7  \n3.3.3 Split Dataset ............................. 7  \n3.3.4 Cache and Prefetch the Dataset .................... 7  \n3.3.5 Resizing and Normalization ..................... 8  \n3.3.6 Data Augmentation .......................... 8  \n3.3.7 Build a CNN model from the training dataset ............ 8  \n3.4 Classification Results ............................. 8  \n4 Design 11  \n4.1 Data Collection ................................ 12  \n4.2 Data Preprocessing .............................. 12  \n4.3 Model building ................................ 13  \n4.4 Web Application ............................... 13  \n4.4.1 Home Page .............................. 14  \n4.4.2 Classification Result Page ...................... 14  \n4.4.3 Classification Report Page ...................... 16  \n5 Results 17  \n5.1 Responsiveness of web application ...................... 17  \n5.2 Dataset .................................... 17  \n5.3 Visualization ................................. 18  \n5.4 User interface ................................. 18  \n5.5 Confidence and accuracy ........................... 21  \n5.6 Evaluation of the System ........................... 22  \n5.6.1 Experiment 1 : Testing the machine learning model for a single image 23  \n5.6.2 Experiment 2 : Testing the machine learning model for a smaller folder of 5 images .......................... 23  \n5.6.3 Experiment 3 : Testing the machine learning model for a small folder of 10 images .......................... 27  \n5.6.4 Experiment 4 : Testing the machine learning model for a medium size folder of 50 images ....................... 27  \n5.6.5 Experiment 5 : Testing the machine learning model for a medium folder of 100 images ......................... 27  \n5.6.6 Experiment 6 : Testing the machine learning model for a large size folder of 500 images ......................... 32  \n6 Conclusion 35  \nReferences 37  \nList of Figures  \n3.1 Sample images in Plant Village dataset .................... 6  \n3.2 Sample images in Taiwan dataset ....................... 7  \n3.3 Training and Validation Accuracy with various epochs for plant village dataset .....................","cbCaidnDmlil9HdA","https://ap.wps.com/l/cbCaidnDmlil9HdA","pdf",13257353,1,45,"English","en",105,"# 1 Introduction\n# 2 Related Works\n## 2.1 Related Works\n## 2.2 Plant Monitoring\n## 2.3 Object Detection\n## 2.4 Plant Health Classification\n## 2.5 Datasets for Plant Leavess\n# 3 Classification model\n## 3.1 Classification Models\n## 3.2 Dataset\n### 3.2.1 Plantvillage\n### 3.2.2 Taiwan Tomato Leaves\n## 3.3 Classification Algorithms\n### 3.3.1 Import data into tensorflow dataset object\n### 3.3.2 Shuffle\n### 3.3.3 Split Dataset\n### 3.3.4 Cache and Prefetch the Dataset\n### 3.3.5 Resizing and Normalization\n### 3.3.6 Data Augmentation\n### 3.3.7 Build a CNN model from the training dataset\n## 3.4 Classification Results\n# 4 Design\n## 4.1 Data Collection\n## 4.2 Data Preprocessing\n## 4.3 Model building\n## 4.4 Web Application\n### 4.4.1 Home Page\n### 4.4.2 Classification Result Page\n### 4.4.3 Classification Report Page\n# 5 Results\n## 5.1 Responsiveness of web application\n## 5.2 Dataset\n## 5.3 Visualization\n## 5.4 User interface\n## 5.5 Confidence and accuracy\n## 5.6 Evaluation of the System\n### 5.6.1 Experiment 1 : Testing the machine learning model for a single image\n### 5.6.2 Experiment 2 : Testing the machine learning model for a smaller folder of 5 images\n### 5.6.3 Experiment 3 : Testing the machine learning model for a small folder of 10 images\n### 5.6.4 Experiment 4 : Testing the machine learning model for a medium size folder of 50 images\n### 5.6.5 Experiment 5 : Testing the machine learning model for a medium folder of 100 images\n### 5.6.6 Experiment 6 : Testing the machine learning model for a large size folder of 500 images\n# 6 Conclusion\n# References","[{\"question\":\"What datasets are used for the plant health classification model?\",\"answer\":\"The model uses the PlantVillage dataset and a Taiwan tomato leaves dataset. These datasets support training and evaluation of leaf condition classification.\"},{\"question\":\"What preprocessing and training steps are applied before building the CNN model?\",\"answer\":\"The pipeline includes importing data into a TensorFlow dataset object, shuffling, splitting the dataset, caching and prefetching, resizing and normalization, and applying data augmentation. Then a CNN model is built from the prepared training dataset.\"},{\"question\":\"How is the system evaluated in terms of performance and accuracy?\",\"answer\":\"Evaluation includes testing the web application responsiveness and measuring confidence and accuracy. Experiments compare predictions for different image folder sizes, ranging from a single image to folders of 500 images, and results are visualized using charts and tables.\"}]","Machine-Learning Based Plant Health Monitoring - Thesis | PDF",1785810092,113,{"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},"machine-learning-based-plant-health-monitoring-thesis","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-plant-health-monitoring-thesis/122337/",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","2026-08-04",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},"What datasets are used for the plant health classification model?","Question",{"text":75,"@type":76},"The model uses the PlantVillage dataset and a Taiwan tomato leaves dataset. These datasets support training and evaluation of leaf condition classification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What preprocessing and training steps are applied before building the CNN model?",{"text":80,"@type":76},"The pipeline includes importing data into a TensorFlow dataset object, shuffling, splitting the dataset, caching and prefetching, resizing and normalization, and applying data augmentation. Then a CNN model is built from the prepared training dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the system evaluated in terms of performance and accuracy?",{"text":84,"@type":76},"Evaluation includes testing the web application responsiveness and measuring confidence and accuracy. Experiments compare predictions for different image folder sizes, ranging from a single image to folders of 500 images, and results are visualized using charts and tables.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},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"]