Affiliation:
1. Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA
2. Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA
3. Department of Chemical & Biomedical Engineering, University of Missouri, Columbia, MO 65211, USA
Abstract
Plant lodging is one of the most essential phenotypes for soybean breeding programs. Soybean lodging is conventionally evaluated visually by breeders, which is time-consuming and subject to human errors. This study aimed to investigate the potential of unmanned aerial vehicle (UAV)-based imagery and machine learning in assessing the lodging conditions of soybean breeding lines. A UAV imaging system equipped with an RGB (red-green-blue) camera was used to collect the imagery data of 1266 four-row plots in a soybean breeding field at the reproductive stage. Soybean lodging scores were visually assessed by experienced breeders, and the scores were grouped into four classes, i.e., non-lodging, moderate lodging, high lodging, and severe lodging. UAV images were stitched to build orthomosaics, and soybean plots were segmented using a grid method. Twelve image features were extracted from the collected images to assess the lodging scores of each breeding line. Four models, i.e., extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN) and artificial neural network (ANN), were evaluated to classify soybean lodging classes. Five data preprocessing methods were used to treat the imbalanced dataset to improve classification accuracy. Results indicate that the preprocessing method SMOTE-ENN consistently performs well for all four (XGBoost, RF, KNN, and ANN) classifiers, achieving the highest overall accuracy (OA), lowest misclassification, higher F1-score, and higher Kappa coefficient. This suggests that Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) may be a good preprocessing method for using unbalanced datasets and the classification task. Furthermore, an overall accuracy of 96% was obtained using the SMOTE-ENN dataset and ANN classifier. The study indicated that an imagery-based classification model could be implemented in a breeding program to differentiate soybean lodging phenotype and classify lodging scores effectively.
Subject
Plant Science,Ecology,Ecology, Evolution, Behavior and Systematics
Reference91 articles.
1. Barrett, J. (2023, February 28). Corn and Soybean Production up in 2021, USDA Reports, Corn and Soybean Stocks up from Year Earlier, Winter Wheat Seedings up for 2022, Available online: https://www.nass.usda.gov.
2. Soybean seed growth dynamics exposed to heat and water stress during the filling period under field conditions;Veas;J. Agron. Crop Sci.,2021
3. Stage of Development Descriptions for Soybeans, Glycine Max (L.) Merrill;Fehr;Crop Sci.,1971
4. Influence of simulated early lodging upon soybean seed yield and its components;Woods;Agron. J.,1977
5. Effect on lodging on seed yield of field-grown soybean—Artificial lodging and lodging preventing treatments;Saito;Jpn. J. Crop. Sci.,2012
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献