{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T18:45:32Z","timestamp":1780080332103,"version":"3.54.0"},"reference-count":27,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T00:00:00Z","timestamp":1716508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A combination tillage with disks, rippers, and roller baskets allows the loosening of compacted soils and the crumbling of soil clods. Statistical methods for evaluating the soil tilth quality of combination tillage are limited. Light Detection and Ranging (LiDAR) data and machine learning models (Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)) are proposed to investigate roller basket pressure settings on soil tilth quality. Soil profiles were measured using LiDAR (stop and go and on-the-go) and RGB visual images from a Completely Randomized Design (CRD) tillage experiment on clay loam soil with treatments of roller basket down, roller basket up, and no-till in three replicates. Utilizing RF, SVM, and NN methods on the LiDAR data set identified median, mean, maximum, and standard deviation as the top features of importance variables that were statistically affected by the roller settings. Applying multivariate discriminatory analysis on the four statistical measures, three soil tilth classes were predicted with mean prediction rates of 77% (Roller-basket down), 64% (Roller-basket up), and 90% (No till). The LiDAR data analytics-inspired soil tilth classes correlated well with the RGB image discriminatory analysis. Soil tilth machine learning models were shown to be successful in classifying soil tilth with regard to onboard operator pressure control settings on the roller basket of the combination tillage implement.<\/jats:p>","DOI":"10.3390\/s24113379","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T08:30:22Z","timestamp":1716539422000},"page":"3379","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Development of a Method for Soil Tilth Quality Evaluation from Crumbling Roller Baskets Using Deep Machine Learning Models"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2463-1061","authenticated-orcid":false,"given":"Mehari Z.","family":"Tekeste","sequence":"first","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50010, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junxian","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Xinjiang Agriculture University, Urumqi 830052, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1784-3517","authenticated-orcid":false,"given":"Desale","family":"Habtezgi","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, DePaul University, SAC 504, Chicago, IL 60604, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7596-9156","authenticated-orcid":false,"given":"Jia-Hao","family":"He","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50010, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Waz","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, DePaul University, SAC 504, Chicago, IL 60604, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"269","DOI":"10.2134\/agronj1919.00021962001100070001x","article-title":"Tillage: A Review of the Literature","volume":"2","author":"Sewell","year":"1919","journal-title":"J. Am. Soc. Agron."},{"key":"ref_2","unstructured":"Gill, W.R., and Vanden Berg, G.E. (1968). Soil Dynamics in Tillage and Traction, Handbook 316."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"379","DOI":"10.13031\/2013.5363","article-title":"Tillage depth, tillage timing, and cover crop effects on cotton yield, soil strength, and tillage energy requirements","volume":"16","author":"Raper","year":"2000","journal-title":"Appl. Eng. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.still.2013.05.013","article-title":"Soil quality response to long-term tillage and crop rotation practices","volume":"133","author":"Karlen","year":"2013","journal-title":"Soil Tillage Res."},{"key":"ref_5","unstructured":"(2018). Terminology and Definitions for Agricultural Tillage Implements (Standard No. ASAE S414.2, SAE S414.2 MAR2009 (R2023))."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"370","DOI":"10.13031\/2013.32793","article-title":"Tillage index based on created soil conditions","volume":"27","author":"Colvin","year":"1984","journal-title":"Trans. ASAE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"153","DOI":"10.2136\/sssaj1990.03615995005400010024x","article-title":"Soil tilth: A review of past perceptions and future needs","volume":"54","author":"Karlen","year":"1990","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.still.2016.07.006","article-title":"Visual soil evaluation: A summary of some applications and potential developments for agriculture","volume":"173","author":"Ball","year":"2017","journal-title":"Soil Tillage Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.13031\/2013.28797","article-title":"Tilth index: An approach to quantifying soil tilth","volume":"35","author":"Singh","year":"1992","journal-title":"Trans. ASAE"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.13031\/2013.28796","article-title":"Secondary Tillage Tool Effect on Soil Aggregation","volume":"35","author":"Adam","year":"1992","journal-title":"Trans. ASAE"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.biosystemseng.2007.03.025","article-title":"Development of an image-processing technique for soil tilth sensing","volume":"97","author":"Bogrekci","year":"2007","journal-title":"Biosyst. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fanigliulo, R., Antonucci, F., Figorilli, S., Pock, D., Pallottino, F., Fornaciari, L., Grilli, R., and Costa, C. (2020). Light Drone-Based Application to Assess Soil Tillage Quality Parameters. Sensors, 20.","DOI":"10.3390\/s20030728"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"399","DOI":"10.5194\/soil-1-399-2015","article-title":"Soil surface roughness: Comparing old and new measuring methods and application in a soil erosion","volume":"1","author":"Model","year":"2015","journal-title":"Soil"},{"key":"ref_14","unstructured":"Gee, G.W., and Bauder, J.W. (1986). Particle-Size Analysis in Methods of Soil Analysis, Part 1, Monograph 9, American Society of Agronomy. [2nd ed.]."},{"key":"ref_15","unstructured":"Nelson, D.W., and Sommers, L.E. (1996). Total carbon, organic carbon and organic matter, Part-3 Methods of soil analysis. Chemical Method, American Society of Agronomy. [1st ed.]."},{"key":"ref_16","unstructured":"(2010). Standard Test Method for Liquid Limit, Plastic Limit, and Plasticity Index of Soils (Standard No. D4318-10)."},{"key":"ref_17","unstructured":"Ghorbani, S. (2019). Simulation of Soil-to-Tool Interaction Using Discrete Element Method (DEM) and Multibody Dynamics (MBD) Coupling. [Ph.D. Thesis, Iowa State University]. Available online: https:\/\/www.proquest.com\/docview\/2242967820?pqorigsite=gscholar&fromopenview=true&sourcetype=Dissertations%20&%20Theses."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"15038","DOI":"10.1038\/s41598-019-51545-7","article-title":"New Method for Evaluating Surface Roughness Parameters Acquired by Laser Scanning","volume":"9","author":"Tonietto","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-009-9124-7","article-title":"Ensemble-based classifiers","volume":"33","author":"Rokach","year":"2010","journal-title":"Artif. Intell. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1016\/j.ejor.2016.10.031","article-title":"Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500","volume":"259","author":"Krauss","year":"2017","journal-title":"Eur. J. Oper. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, X., and Kumar, V. (2009). The Top Ten Algorithms in Data Mining, CRC Press.","DOI":"10.1201\/9781420089653"},{"key":"ref_22","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zwillinger, D., and Kokoska, S. (1999). CRC Standard Probability and Statistics Tables and Formulae, CRC Press.","DOI":"10.1201\/9780367802417"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.13031\/2013.17289","article-title":"An Evaluation of Seed Furrow Smearing","volume":"41","author":"Iqbal","year":"1998","journal-title":"Trans. ASAE"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.still.2003.06.001","article-title":"Laboratory assessment of the workable range of soils in the tropical zone of Veracruz, Mexico","volume":"74","author":"Hoogmoed","year":"2003","journal-title":"Soil Tillage Res."},{"key":"ref_26","unstructured":"Gholamy, A., Kreinovich, V., and Kosheleva, O. (2024, May 01). Why 70\/30 or 80\/20 Relation Between Training and Testing Sets: A Pedagogical Explanation. 2018. Departmental Technical Reports (CS). 1209. Available online: https:\/\/scholarworks.utep.edu\/cs_techrep\/1209."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.1093\/bioinformatics\/bts238","article-title":"Statistical interpretation of machine learning-based feature importance scores for biomarker discovery","volume":"28","author":"Wehenkel","year":"2012","journal-title":"Bioinformatics"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3379\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:48:09Z","timestamp":1760107689000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3379"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,24]]},"references-count":27,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113379"],"URL":"https:\/\/doi.org\/10.3390\/s24113379","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,24]]}}}