[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117317-en":3,"doc-seo-117317-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117317,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques","Corn grain moisture (CGM) is essential for judging grain maturity and planning harvest timing, yet common approaches such as manual scouting and destructive laboratory testing are time-consuming, costly, spatially limited, and can be subjective. A study evaluates pre-harvest CGM estimation using high-resolution aerial multispectral imagery (1.3 cm/pixel) and machine learning. Data from 116 experimental corn plots in the 2022 season derive 24 vegetation indices plus reflectance features, then train and test multiple regression and ML models. Results show input formulation and train-test splits affect accuracy, with Random Forest offering the best and most stable performance for reliable precision harvest scheduling.","land  \nArticle  \nPre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques  \nPius Jjagwe 1,2, Abhilash K. Chandel 1,2, * and David Langston 1  \nCitation: Jjagwe, P.; Chandel, A.K.; Langston, D. Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques. Land 2023, 12, 2188. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)land12122188  \nAcademic Editor: Chuanrong Zhang  \nReceived: 23 November 2023  \nRevised: 14 December 2023  \nAccepted: 15 December 2023  \nPublished: 18 December 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Virginia Tech Tidewater Agricultural Research and Extension Center, Suffolk, VA 23437, USA; [pjjagwe@vt.edu](pjjagwe@vt.edu) (P.J.); [dblangston@vt.edu](dblangston@vt.edu) (D.L.)  \n2 Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA  \n* Correspondence: [abhilashchandel@vt.edu](abhilashchandel@vt.edu)  \nAbstract: Corn grain moisture (CGM) is critical to estimate grain maturity status and schedule harvest. Traditional methods for determining CGM range from manual scouting, destructive laboratory analyses, and weather-based dry down estimates. Such methods are either time consuming, expensive, spatially inaccurate, or subjective, therefore they are prone to errors or limitations. Realizing that precision harvest management could be critical for extracting the maximum crop value, this study evaluates the estimation of CGM at a pre-harvest stage using high-resolution (1.3 cm/pixel) multispectral imagery and machine learning techniques. Aerial imagery data were collected in the 2022 cropping season over 116 experimental corn planted plots. A total of 24 vegetation indices (VIs) were derived from imagery data along with reflectance (REF) information in the blue, green, red, rededge, and near-infrared imaging spectrum that was initially evaluated for inter-correlations as well as subject to principal component analysis (PCA) . VIs including the Green Normalized Difference Index (GNDVI), Green Chlorophyll Index (GCI), Infrared Percentage Vegetation Index (IPVI), Simple Ratio Index (SR), Normalized Difference Red-Edge Index (NDRE), and Visible Atmospherically Resistant Index (VARI) had the highest correlations with CGM (r: 0.68–0.80) . Next, two state-of-the-art statistical and four machine learning (ML) models (Stepwise Linear Regression (SLR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)), and their 120 derivates (six ML models 􀀂 two input groups (REFs and REFs+VIs) 􀀂 10 train–test data split ratios (starting 50:50)) were formulated and evaluated for CGM estimation. The CGM estimation accuracy was impacted by the ML model and train-test data split ratio. However, the impact was not significant for the input groups. For validation over the train and entire dataset, RF performed the best at a 95:5 split ratio, and REFs+VIs as the input variables (r train: 0.97, rRMSE train: 1.17%, r entire: 0.95, rRMSE entire: 1.37%) . However, when validated for the test dataset, an increase in the train–test split ratio decreased the performances of the other ML models where SVM performed the best at a 50:50 split ratio (r = 0.70, rRMSE = 2.58%) and with REFs+VIs as the input variables. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for relatively smaller or medium-sized datasets. RF wasidentified to be the most stable and consistent ML model (r: 0.95, rRMSE: 1.37%) . Findings in the st","cbCaimPs3yvmVPbx","https://ap.wps.com/l/cbCaimPs3yvmVPbx","pdf",5066322,1,15,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction\n## Grain moisture importance\n## Corn grain moisture dynamics","[{\"question\":\"Why is corn grain moisture (CGM) important for harvest decisions?\",\"answer\":\"CGM determines optimal harvest timing, affecting harvest and storage outcomes. Harvesting too low can cause losses from shrinkage and other issues, while harvesting too high increases fungal infection risk and drying costs.\"},{\"question\":\"What data and features are used for pre-harvest CGM estimation in this study?\",\"answer\":\"Aerial high-resolution multispectral imagery is collected over experimental corn plots. The study derives 24 vegetation indices plus reflectance information across blue, green, red, red-edge, and near-infrared bands.\"},{\"question\":\"Which machine learning model performed best, and under what evaluation setting?\",\"answer\":\"Random Forest showed the best performance at a 95:5 train-test split ratio and was identified as the most stable model. When validated on the test dataset, performance depended on the train-test split ratio for other models as well.\"}]",1785675137,38,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"pre-harvest-corn-grain-moisture-estimation-using-aerial-multispectral-imagery-and-machine-learning-techniques","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/pre-harvest-corn-grain-moisture-estimation-using-aerial-multispectral-imagery-and-machine-learning-techniques/117317/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is corn grain moisture (CGM) important for harvest decisions?","Question",{"text":74,"@type":75},"CGM determines optimal harvest timing, affecting harvest and storage outcomes. Harvesting too low can cause losses from shrinkage and other issues, while harvesting too high increases fungal infection risk and drying costs.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What data and features are used for pre-harvest CGM estimation in this study?",{"text":79,"@type":75},"Aerial high-resolution multispectral imagery is collected over experimental corn plots. The study derives 24 vegetation indices plus reflectance information across blue, green, red, red-edge, and near-infrared bands.",{"name":81,"@type":72,"acceptedAnswer":82},"Which machine learning model performed best, and under what evaluation setting?",{"text":83,"@type":75},"Random Forest showed the best performance at a 95:5 train-test split ratio and was identified as the most stable model. When validated on the test dataset, performance depended on the train-test split ratio for other models as well.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]