Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning (original) (raw)
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The development of molecular techniques for genetic analysis has enabled great advances in cereal breeding. However, their usefulness in hybrid breeding, particularly in assigning new lines to heterotic groups previously established, still remains unsolved. In this work we evaluate the performance of several state-of-art multiclass classifiers onto three molecular marker datasets representing a broad spectrum of maize heterotic patterns. Even though results are variable, they suggest supervised learning algorithms as a valuable complement to traditional breeding programs.▶ Molecular techniques have been proposed to assign maize inbreds to heterotic groups. ▶ We evaluate several supervised learning algorithms onto 3 maize datasets. ▶ Results suggest multiclass classifiers as an alternative to traditional statistical methods.
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Maize seed variety identification model using image processing and deep learning
Institute of Advanced Engineering and Science (iaes), 2024
Maize is Ethiopia's dominant cereal crop regarding area coverage and production level. There are different varieties of maize in Ethiopia. Maize varieties are classified based on morphological features such as shape and size. Due to the nature of maize seed and its rotation variant, studies are still needed to identify Ethiopian maize seed varieties. With expert eyes, identification of maize seed varieties is difficult due to their similar morphological features and visual similarities. We proposed a hybrid feature-based maize variety identification model to solve this problem. For training and testing the model, images of each maize variety were collected from the adet agriculture and research center (AARC), Ethiopia. A multi-class support vector machine (MCSVM) classifier was employed on a hybrid of handcrafted (i.e., gabor and histogram of oriented gradients) and convolutional neural network (CNN)-based feature selection techniques and achieved an overall classification accuracy of 99%.
Computational Intelligence and Neuroscience
Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. This study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. The classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtaine...
6th Intercontinental Geoinformation Days, 2023
In recent years, detailed monitoring of different vegetation classes by using modern remote sensing technologies has become one of the essential issues for smart agriculture activities. In this study, using three advanced machine learning algorithms, namely canonical correlation forest (CCF), rotation forest (RotFor) and support vector machines (SVM), and object-based image classification techniques on multispectral (MS) unmanned aerial vehicle (UAV) orthomosaics, the separability of 12 maize species were investigated. The investigations were performed in Sakarya Maize Research Institute application area located in Arifiye district of Sakarya province, Turkey. In maize monitoring, besides the five spectral bands (R, G, B, red edge, NIR) of the MS UAV, the Normalized Digital Surface Model (NDSM) describing the height of maize species was generated and included as an additional band to improve classification performance, evaluated with F-score, overall accuracy (OA) and Kappa metrics. The results demonstrated that CCF and RotFor algorithms provide similar OA as 76.61% and 76.75%, respectively and the SVM algorithm has 74.18%. In parallel, the Kappa values of CCF and RotFor are 0.75 and the SVM is 0.72. In terms of class-based F-scores, by all algorithms, C. Sweet and C. Arifiye were identified with over 97% and 94% accuracies, respectively, that prove the successful determination of their boundaries using object-based classification.