[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120006-en":3,"doc-seo-120006-105":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":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},120006,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine Learning for Soil Classification - Challenges and Opportunities","Soil classification underpins agriculture and environmental science by enabling informed choices for crop selection, land management, and environmental protection. Traditional soil classification relies heavily on human expertise and therefore suffers from high time and labor demands, limited specialist availability, and potential inconsistency. The study evaluates machine learning models to automate soil classification using large soil-sample datasets, examining methods such as SVM, decision trees, random forests, and neural networks, alongside Random Forest, Naïve Bayes, and k-NN.","Machine Learning for Soil Classification: Challenges and Opportunities  \nSiti Nur Fatin Liyana MohdAzmin 1, Hamijah Mohd Rahman2, Nur Nadhirah Mohd Harith Lim3, NureizeArbaiy4*  \n1 Faculty of Computer Science and Information Technology,  \nniversiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, 86400, MALAYSI  \n*Corresponding Author: [nureize@uthm.edu.my](nureize@uthm.edu.my)  \nDOI: [https://doi.org/10.30880/jastec.2024.01.01.004](https://doi.org/10.30880/jastec.2024.01.01.004)  \n\n| Article Info | Abstract |\n| --- | --- |\n| Received: 30 October 2023\u003Cbr>Accepted: 04 February 2024\u003Cbr>Available online: 27 February 2024 | In agriculture and environmental science, soil classification is essential for making well-informed decisions about crop selection, land management, and environmental protection. However conventional methods of classifying soil require a lot of work and time, and they |\n| Keywords\u003Cbr>Soil classification, machine learning, human expert availability, human expert consistency, agriculture, environmental science, land management, crop selection | mostly rely on human expertise. This work investigates the possibilities of machine learning (ML) models to automate soil classification utilizing large datasets of soil samples to overcome the shortcomings of existing techniques. In this paper, many machines learning techniques, including support vector machines (SVM), decision trees (DT), random forests (RF), and neural networks (NN), are examined for the classification of soil. There are certain models that work better than others, though, and this is based on the qualities of the soil samples. In addition to that, experiments using Random Forest, Naïve Bayes, and k-Nearest Neighbor (k-NN) were also undertaken. Classification strategies are being chosen to create a classified model using data mining. The algorithm with the highest accuracy is Random Forest (97.23%), followed by Naïve Bayes (96.82%), and k-Nearest Neighbor (k-NN), which has the lowest accuracy (92.92%) . The paper highlights the challenges of applying machine learning to soil classification, such as consistency and human specialist availability, to effectively categorize soil samples. The results indicate that, despite these challenges, ML models present a potential substitute for labor-intensive conventional methods in the classification of soil. |\n\n1. Introduction  \nSince long ago, agriculture has been regarded as the primary cultural practice. Understanding the type of soil to utilize for agricultural cultivation is essential for achieving the highest crop yield because soil is such an important aspect of agriculture. The kind of soil can be identified using a variety of techniques, including technological advancements, experience, and conventional procedures [1] . Understanding the soil's classification makes it easier to predict its behavior [2] . Through soil behavior and properties, observation prediction of soil potential can be done for agricultural growth. Soils are categorized and given names based on  \nthe chemical and physical characteristics of their horizons. Soil taxonomy classifies soil based on its color, texture, structure, and other characteristics. Soil classification is a way to arrange soil-related knowledge. It's because different soils have different physical, chemical, and biological characteristics [3] . The classification of soil allows land to be grouped or classes with similar properties and behaviors (chemical, physical, and biological), and it can be geo-mapped and referenced [4] .  \nFor years, traditional farmers have been deeply acquainted with the soils in their area, and they use this information to choose crops that are compatible with the soil's characteristics [5] . As an illustration, a farmer may be aware that some soil types are more suited for growing rice, while others are better for growing wheat. Depending on the region and the requirements of the farmers, traditional soil classification systems can be more ","cbCaij16SOtvvKBr","https://ap.wps.com/l/cbCaij16SOtvvKBr","pdf",580983,1,10,"English","en",105,"# Abstract\n# Introduction\n## Importance of soil classification\n## Limitations of conventional methods\n## Role of machine learning in soil classification","[{\"question\":\"Why is soil classification important in agriculture and environmental science?\",\"answer\":\"Soil classification supports decisions about crop selection, land management, and environmental protection by helping predict soil behavior and properties relevant to cultivation and protection.\"},{\"question\":\"What limitations do conventional soil classification methods have?\",\"answer\":\"Conventional methods are time- and labor-intensive because they require collecting and analyzing soil samples in laboratories and they mostly depend on human expertise, which can cause inconsistencies and limited availability.\"},{\"question\":\"Which machine learning model achieved the best classification accuracy in the study?\",\"answer\":\"Random Forest achieved the highest accuracy at 97.23%, followed by Naïve Bayes at 96.82%, while k-NN had the lowest accuracy at 92.92%.\"}]","Machine Learning for Soil Classification - 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