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The study builds new models for six soil sample groups (Soil_1 to Soil_6) using twelve machine learning algorithms, achieving overall accuracy above 99.2%, with best results of 99.83% using Bayes Net. Image-condition stability and texture-driven variability are considered, demonstrating feasibility of rapid, non-destructive and accurate soil classification.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-and-computer-vision-technology-to-analyze-and-discriminate-soil-samples-rapid-non-destructive-accurate-feasibility/291016/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-and-computer-vision-technology-to-analyze-and-discriminate-soil-samples-rapid-non-destructive-accurate-feasibility/291016.png","ImageObject",300,407,{"name":92,"@type":93},"Evangeline","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is soil discrimination based on texture important for agriculture?","Question",{"text":112,"@type":113},"Soil texture strongly affects water permeability and many soil–plant processes, making rapid and accurate identification valuable before planting and tillage.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How many soil groups and algorithms are used in the study?",{"text":117,"@type":113},"The study models six soil sample groups (Soil_1 to Soil_6) using twelve machine learning algorithms.",{"name":119,"@type":110,"acceptedAnswer":120},"Which model achieved the highest accuracy and what were the key performance metrics?",{"text":121,"@type":113},"Bayes Net achieved the greatest accuracy at 99.83%. For Bayes Net, MCC and F-measure were 0.994 and 0.995 for Soil_4 and Soil_6, and were 1.000 for other soil groups.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},291016,1789643185,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},13056703019662,"https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMachine learning and computer vision technology to analyze and discriminate soil samples  \nSema Kaplan1, Ewa Ropelewska2, SedaGünaydın3, Kadir Sabancı4 & Necati Çetin5*  \nSoil texture is one of the most important elements to consider before planting and tillage. These features affect the product selection and regulate its water permeability. Discrimination of soils by determining soil texture features requires an intense workload and is time-consuming. Therefore, having a powerful tool and knowledge for texture-based soil discrimination could enable rapid and accurate discrimination of soils. This study focuses on presenting new models for 6 different soil sample groups (Soil_ 1 to Soil_ 6) based on 12 different machine learning algorithms that can be utilized for various problems. As a result, overall accuracy values were determined as greater than 99.2%(Trilayered Neural Network). The greatest accuracy value was found in Bayes Net (99.83%) and followed by Subspace Discriminant (99.80%) . In the Bayes Net algorithm, MCC (Matthews Correlation Coefficient) and F-measure values were obtained as 0.994 and 0.995 for Soil_ 4 and Soil_6 sample groups while these values were 1.000 for other soil groups. Soil types can visually vary based on their texture, mineral composition, and moisture levels. The variability of this can be influenced by fertilization, precipitation levels, and soil cultivation. It is important to capture the images in soil conditions that are more stable. In conclusion, the present study has proven the feasibility of rapid, non-destructive, and accurate discrimination of soils by image processing-based machine learning.  \nKeywords Soil, Machine learning, Multi-object detection, Texture, Image processing  \nAgriculture has a strategic importance in the development of humanity, thus, is among the most important sectors of the world. Soils provide an environment for plant nutrients, water, and seed germination thus, are the main sources of agricultural production. Considering climate change-induced migrations, ecology and urban development, soil properties should be well-known for sustainable agriculture1,2. The biological, physical and chemical attributes of soils must be suitable to fulfill their functions and to provide soil-dependent ecosystem services. Advanced intensive farming practices cause soils to become increasingly prone to degradation3.  \nSoil texture is a physical soil property that expresses the percentage of individual particles within the soil mass. Texture is a critical factor with significant effects on physical and chemical characteristics, including primarily soil–water balance, germination, root development4, cation exchange capacity5, aggregation6 and erosion susceptibility1,7. Texture can traditionally be approximated by the “texture by feel” method, depending on the experience of the person performing the application in the field8. Detailed and accurate analysis is made in a laboratory using a hydrometer9 or pipette method10. These methods are costly and require some time to determine soil texture. They are also non-environment friendly methods since chemicals are used to provide dispersion. With the increase in application areas of new technologies in agriculture, developments are achieved in agricultural production. In this sense, precision farming practices, which are used more and more frequently, provide advantages to researchers in terms of developing reliable, cost-effective, and faster ways to measure and characterize soil properties11–14. Accurate definition of soil properties is quite a significant issue for optimum conditions and sustainable use of soil, which is a natural resource. Recently, textural properties have been determined using sensors15, 16. Also, Aitkenhead et al.17 estimated the texture of the soil profile using images obtained from a smartphone, although it could not achieve high accurac","cbCaibnvpk5qu3GB","https://ap.wps.com/l/cbCaibnvpk5qu3GB","pdf",1816457,11,"English","# Introduction\n# Soil texture and measurement challenges\n# Machine learning and image processing approach\n# Model setup and algorithms\n# Results and accuracy evaluation\n# Discussion and conclusion","[{\"question\":\"Why is soil discrimination based on texture important for agriculture?\",\"answer\":\"Soil texture strongly affects water permeability and many soil–plant processes, making rapid and accurate identification valuable before planting and tillage.\"},{\"question\":\"How many soil groups and algorithms are used in the study?\",\"answer\":\"The study models six soil sample groups (Soil_1 to Soil_6) using twelve machine learning algorithms.\"},{\"question\":\"Which model achieved the highest accuracy and what were the key performance metrics?\",\"answer\":\"Bayes Net achieved the greatest accuracy at 99.83%. For Bayes Net, MCC and F-measure were 0.994 and 0.995 for Soil_4 and Soil_6, and were 1.000 for other soil groups.\"}]","Machine learning and computer vision technology to analyze and discriminate soil samples - Rapid, non-destructive, accurate feasibility | PDF",28]