Impact Assessment of Nematode Infestation on Soybean Crop Production Using Aerial Multispectral Imagery and Machine Learning
| dc.contributor.author | Jjagwe, Pius | en |
| dc.contributor.author | Chandel, Abhilash K. | en |
| dc.contributor.author | Langston, David B. | en |
| dc.date.accessioned | 2024-07-12T13:02:43Z | en |
| dc.date.available | 2024-07-12T13:02:43Z | en |
| dc.date.issued | 2024-06-24 | en |
| dc.date.updated | 2024-07-12T12:41:52Z | en |
| dc.description.abstract | Accurate and prompt estimation of geospatial soybean yield (SY) is critical for the producers to determine key factors influencing crop growth for improved precision management decisions. This study aims to quantify the impacts of soybean cyst nematode (SCN) infestation on soybean production and the yield of susceptible and resistant seed varieties. Susceptible varieties showed lower yield and crop vigor recovery, and high SCN population (20 to 1080) compared to resistant varieties (SCN populations: 0 to 340). High-resolution (1.3 cm/pixel) aerial multispectral imagery showed the blue band reflectance (r = 0.58) and Green Normalized Difference Vegetation Index (GNDVI, r = −0.6) have the best correlation with the SCN populations. While GDNVI, Green Chlorophyll Index (GCI), and Normalized Difference Red Edge Index (NDRE) were the best differentiators of plant vigor and had the highest correlation with SY (r = 0.59–0.75). Reflectance (REF) and VIs were then used for SY estimation using two statistical and four machine learning (ML) models at 10 different train–test data split ratios (50:50–95:5). The ML models and train–test data split ratio had significant impacts on SY estimation accuracy. Random forest (RF) was the best and consistently performing model (r: 0.84–0.97, rRMSE: 8.72–20%), while a higher train–test split ratio lowered the performances of the ML models. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for modeling over smaller or medium-sized datasets. Such insights derived using high spatial resolution data can be utilized to implement precision crop protective operations for enhanced soybean yield and productivity. | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Jjagwe, P.; Chandel, A.K.; Langston, D.B. Impact Assessment of Nematode Infestation on Soybean Crop Production Using Aerial Multispectral Imagery and Machine Learning. Appl. Sci. 2024, 14, 5482. | en |
| dc.identifier.doi | https://doi.org/10.3390/app14135482 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/120663 | en |
| dc.language.iso | en | en |
| dc.publisher | MDPI | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | SUAS multispectral sensing | en |
| dc.subject | machine learning | en |
| dc.subject | soybean yield | en |
| dc.subject | vegetation index | en |
| dc.subject | precision management | en |
| dc.title | Impact Assessment of Nematode Infestation on Soybean Crop Production Using Aerial Multispectral Imagery and Machine Learning | en |
| dc.title.serial | Applied Sciences | en |
| dc.type | Article - Refereed | en |
| dc.type.dcmitype | Text | en |