Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Object classification using X-ray images

The main aim of the presented research was to assess the possibility of utilizing geometric features in object classification. Studies were conducted using X-ray images of kernels belonging to three different wheat varieties: Kama, Canadian and Rosa. As a part of the work, image processing methods were used to determine the main geometric grain parameters, including the kernel area, kernel perimeter, kernel length and kernel width. The results indicate significant differences between wheat varieties, and demonstrates the importance of their size and shape parameters in the classification process. The percentage of correctness of classification was about 92% when the k-Means algorithm was used. A classification rate of 93% was obtain using the K-Nearest Neighbour and Support Vector Machines. Herein, the Rosa variety was better recognized, whilst the Canadian and Kama varieties were less successfully differentiated

Classifying Wheat Genotypes using Machine Learning Models for Single Kernel Characterization System Measurements

The properties related to market value, milling, classification, storage, and transportation of bread wheat are determined by using some important physical quality characteristics such as weight, shape, dimensions, and hardness of wheat kernels. It is possible to measure all these features using single kernel characterization system (SKCS). Classification of wheat genotypes using computer-based algorithms is crucial to determine the most accurate physical quality classification for breeding studies. In this paper, four commercial wheat cultivars (Altay-2000, Bezostaja-1, Harmankaya-99, and Kate A-1) and six doubled haploid (DH) wheat genotypes are studied to classify wheat cultivars and DH wheat genotypes separately. In the classification stage, feature vectors constructed from measured characters namely, kernel weight, diameter, hardness, and moisture are applied to well-known classifiers such as Common Vector Approach (CVA), Support Vector Machines (SVM) and K-Nearest Neighbor (KNN). Satisfactory results especially for the training set are obtained from the experimental studies. Classification results are compared with single linkage hierarchical cluster (SLHC) analysis, which is the most widely used in breeding studies. Recognition of clustered genotypes in all three classification methods and dendrograms present similar results. The SVM model is found to be outperformed over other methods for studied characters and could therefore effectively be utilized for characterizing, classifying and/or identifying the wheat genotypes

Classifying Wheat Genotypes using Machine Learning Models for Single Kernel Characterization System Measurements

985-991The properties related to market value, milling, classification, storage, and transportation of bread wheat are determined by using some important physical quality characteristics such as weight, shape, dimensions, and hardness of wheat kernels. It is possible to measure all these features using single kernel characterization system (SKCS). Classification of wheat genotypes using computer-based algorithms is crucial to determine the most accurate physical quality classification for breeding studies. In this paper, four commercial wheat cultivars (Altay-2000, Bezostaja-1, Harmankaya-99, and Kate A-1) and six doubled haploid (DH) wheat genotypes are studied to classify wheat cultivars and DH wheat genotypes separately. In the classification stage, feature vectors constructed from measured characters namely, kernel weight, diameter, hardness, and moisture are applied to well-known classifiers such as Common Vector Approach (CVA), Support Vector Machines (SVM) and K-Nearest Neighbor (KNN). Satisfactory results especially for the training set are obtained from the experimental studies. Classification results are compared with single linkage hierarchical cluster (SLHC) analysis, which is the most widely used in breeding studies. Recognition of clustered genotypes in all three classification methods and dendrograms present similar results. The SVM model is found to be outperformed over other methods for studied characters and could therefore effectively be utilized for characterizing, classifying and/or identifying the wheat genotypes

Earth Resources: A continuing bibliography with indexes, Issue 35

This bibliography list 587 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between July 1, and September 30, 1982. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis