[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122783-en":3,"doc-seo-122783-105":30,"detail-sidebar-cat-0-en-105":92},{"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},122783,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis","This study proposes an IoT-based smart irrigation management system to improve water-resource utilization in smart agriculture. Ground parameters sensed from automated monitoring devices—soil moisture, light intensity, temperature, and humidity—feed unsupervised learning clustering to predict irrigation needs. Feature extraction computes statistics such as maximum, minimum, mean, and standard deviation from multiple soil moisture sensors, then lag features improve classification accuracy. A dataset with 108 features is processed in Orange GUI using k-means clustering. Model evaluation on one month of data shows strong performance with Random Forest, Neural Network, and kNN reaching 100%, 99.9%, and 99.8% accuracy.","Unsupervised Fertigation and Machine Learning for Crop Vegetation  \nParameter Analysis  \nMohd Izzat Mohd Rahman1, Mohd Azraai Mohd Razman*1, Anwar PP Abdul Majeed2, Muhammad Nur  \nAiman Shapiee1, Muhammad Amirul Abdullah1, Rabiu Muazu Musa3  \nSubmitted: 28/04/2023 Revised: 29/06/2023 Accepted: 09/07/2023  \nAbstract: This study proposes an IoT-based smart irrigation management system that can optimize water-resource utilization in a smart agricultural system. The system uses unsupervised learning-based clustering to predict the irrigation needs of a field based on the ground parameters sensed by automated monitoring devices. These parameters include soil moisture, light intensity, temperature, and humidity. The system extracts feature such as the maximum, minimum, mean, and standard deviation of four soil moisture sensors from the primary dataset of plants. Then, it applies lag features to enhance the accuracy of the classification model. The system uploads the dataset of 108 features to the Orange GUI and performs k-means clustering to assign cluster labels to the data as meta-attributes in a new dataset. The study evaluates the system using a month’s worth of data and demonstrates its functionality and effectiveness. The system employs machine learning techniques such as Random Forest, Neural Network, and kNN, which achieve 100%, 99.9%, and 99.8% accuracy respectively.  \nKeywords: Machine Learning; Feature Extraction; Classification; Fertigation System; Chili Plant  \n1. Introduction  \nPlant care is a strategy and activity for maintaining the health and appearance of plants. Water and soil continue tobe vital sources of life for all plants, assisting the photosynthetic process, particularly in diverse plants. Living systems of fertilization depend on nutrients from the water and soil. A fertigation system is a process of using fertilizer clarifications with irrigation water, commonly through a micro-sprinkler or a drip system [1] . Insufficient irrigation or excessive watering have been detrimental to production and nature, making automation of the rejuvenating greenery system important.  \nJordan's Hashemite Kingdom is studying the Mediterranean region's climate via research. Snowfalls occur at short intervals on the majority of the kingdom's mountain highlands in the north, center, and south and are quite heavy and sometimes collected [2] . Whereas the Equatorial area surrounding Malaysia has a climate defined by year-round high average temperatures and high monthly precipitation. Malaysia's climate has a year-round high flat temperature line (above 25°C) and rainfall of more than 350mm in December, but less than 350mm for the remainder of the month as shown in Fig. 1. The irregularity of rainfall  \n1 Manufacturing and Mechatronic Engineering Technology (FTKPM), Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia  \n2 School of Robotics, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, 215127, Taicang, PR China  \n3 Center for Fundamental and Continuing Education, Department of Credited Co-curriculum, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Terengganu, Malaysia  \n* [Corresponding Author Email: mohdazraai@ump.edu.my](Corresponding Author Email: mohdazraai@ump.edu.my)  \ndistribution in Malaysia throughout the year, the quality of the soil, the amount of water available in the region, and technological advancements in all spheres of our lives provide us with a sense of assisting farmers in watering the plants without time or effort by maintaining excellent plant production and providing irrigation water amounts [3] .  \nFig. 1. Monthly Climatology of Min-Temperature, MeanTemperature, Max-Temperature & Precipitation 1991- 2020 Malaysia  \nAs we recognize that the highest quality plants need, the first issue in this project is how to gather data from the set of plants that we want to apply. The sensors are adequate for them to interpret the data correctly, allowing us to go on to  \nthe","cbCainJG7Ycug68V","https://ap.wps.com/l/cbCainJG7Ycug68V","pdf",815649,1,9,"English","en",105,"# Introduction\n# Related Work\n# Methods\n# Experimental Settings and Results\n# Conclusion","[{\"question\":\"What problem does the proposed system address in smart agriculture?\",\"answer\":\"It optimizes irrigation decisions to improve water-resource utilization while supporting efficient fertigation for crop growth.\"},{\"question\":\"Which sensed parameters are used to predict irrigation needs?\",\"answer\":\"The system uses soil moisture, light intensity, temperature, and humidity from automated monitoring devices.\"},{\"question\":\"How does the study validate the effectiveness of the system?\",\"answer\":\"It evaluates the approach using one month of data and reports high accuracy results from Random Forest, Neural Network, and kNN.\"}]","Unsupervised Fertigation and Machine Learning for Crop Vegetation Parameter Analysis | PDF",1785812873,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"unsupervised-fertigation-and-machine-learning-for-crop-vegetation-parameter-analysis","",{"@graph":36,"@context":86},[37,54,69],{"@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/unsupervised-fertigation-and-machine-learning-for-crop-vegetation-parameter-analysis/122783/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the proposed system address in smart agriculture?","Question",{"text":76,"@type":77},"It optimizes irrigation decisions to improve water-resource utilization while supporting efficient fertigation for crop growth.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which sensed parameters are used to predict irrigation needs?",{"text":81,"@type":77},"The system uses soil moisture, light intensity, temperature, and humidity from automated monitoring devices.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the study validate the effectiveness of the system?",{"text":85,"@type":77},"It evaluates the approach using one month of data and reports high accuracy results from Random Forest, Neural Network, and kNN.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"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":107,"slug":138},19,"General","general"]