Linear discriminant analysis to differentiate sorghum germplasm: A crucial tool for breeding programmes

A total of 86 Sorghum genotypes along with three checks (CO 30, CO 32, and K 12) were evaluated during Rabi 2021 season to identify variations and character associations among grain yield and yield component traits. The phenotypic data collected were used to create a statistical database and were analyzed using linear discriminant analysis (LDA) to identify and discriminate landraces for utilization in sorghum breeding. The LDA successfully differentiated the genotypes into three groups with an accuracy of 73.52%. The study revealed a significant level of variation among the genotypes, based on observations for nine quantitative traits. Further analysis using the LDA biplot showed that the genotypes within clusters 1 and 4 hold potential for future breeding programmes. Therefore, the observed phenotypic data can be useful for identifying and selecting appropriate accessions for sorghum improvement

Morpho-Colorimetric Characterization of the Sardinian Endemic Taxa of the Genus Anchusa L. by Seed Image Analysis

In this work, the seed morpho-colorimetric differentiation of the Sardinian endemic species of Anchusa (Boraginaceae) was evaluated. In Sardinia, the Anchusa genus includes the following seven taxa: A. capellii, A. crispa ssp. crispa, A. crispa ssp. maritima, A. formosa, A. littorea, A. montelinasana, and A. sardoa. Seed images were acquired using a flatbed scanner and analyzed using the free software package ImageJ. A total of 74 seed morpho-colorimetric features of 2692 seed lots of seven taxa of Anchusa belonging to 17 populations were extrapolated and used to build a database of seed size, shape, and color features. The data were statistically elaborated by the stepwise linear discriminant analysis (LDA) to compare and discriminate each accession and taxon. In addition, the seed morpho-colorimetric differences among coastal and mountainous taxa were evaluated. Considering the ecological conditions, the LDA was able to discriminate among the Anchusa taxa with a correct identification of 87.4% and 90.8% of specimens for mountainous and coastal plants, respectively. Moreover, the LDA of the 17 populations of Anchusa showed a low separation among species and populations within the coastal group, highlighting how the long-distance dispersal by flotation on the sea water surface and the pollination network may influence the similarity patterns observed. In addition, a misattribution was observed for A. crispa ssp. crispa, which was misclassified as A. crispa ssp. maritima in 14.1% of cases, while A. crispa ssp. maritima was misidentified as A. crispa ssp. crispa in 21.1% of cases, highlighting a close phenotypic relationship between these two taxa. The statistical results obtained through the seed image analysis showed that the morpho-colorimetric features of the seeds provide important information about the adaptation and evolution of Anchusa taxa in Sardinia

Análise de sementes associado a aprendizagem de máquina para identificar espécies florestais nativas

Orientador: Prof. Dr. Antonio Carlos NogueiraCoorientadores: Profª. Drª. Dagma Kratz e Prof. Dr. Richardson RibeiroTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Agrárias, Programa de Pós-Graduação em Engenharia Florestal. Defesa : Curitiba, 31/07/2023Inclui referênciasResumo: A identificação e caracterização de sementes nativas representam um desafio para o setor florestal devido à variabilidade de características morfobiométricas. Atualmente, as metodologias para a análise biométrica de sementes florestais são realizadas por especialistas humanos utilizando métodos tradicionais de medições, como os paquímetros e variáveis baseadas em tamanho. Nesse contexto, concebeu-se uma nova metodologia empregando técnicas de processamento de imagens digitais e aprendizado de máquina com base em características externas das sementes para possibilitar a identificação de espécies florestais. A pesquisa foi dividida em três capítulos distintos. No primeiro capítulo foi realizada uma análise bibliométrica para quantificar e analisar os estudos científicos que abordam a análise de imagens e o aprendizado de máquina aplicados às sementes, e com isso apontar os principais tópicos e lacunas existentes para pesquisas com sementes florestais com esse enfoque. Os resultados indicam um aumento significativo de publicações a partir de 2017, com foco predominante em espécies de culturas agrícolas. Esses estudos estão direcionados principalmente para a classificação, identificação/detecção de cultivares e avaliação da qualidade das sementes, em que apenas 6,6% das publicações abordam espécies florestais, evidenciando a necessidade de mais pesquisas nesse campo com espécies nativas. No segundo capítulo foi proposta uma metodologia de captura e processamento de imagens para caracterização e diferenciação de espécies florestais nativas. Os resultados demonstraram que a análise de imagens de sementes, por meio dessa metodologia, contribuiu para a caracterização e diferenciação de espécies florestais nativas do Brasil, o que apresenta implicações diretas nos aspectos silviculturais, ecológicos e genéticos. No terceiro capítulo foram aplicados diferentes classificadores de aprendizado de máquina associados à análise de imagens para identificar espécies florestais nativas com base em características morfobiométricas das sementes. Os resultados revelaram que é possível identificar espécies florestais nativas com taxa satisfatória de acurácia usando imagens de sementes e aprendizado de máquina. Recomenda-se o classificador de árvores de decisão para a identificação de espécies. Os resultados fornecem subsídios importantes para aprimorar a caracterização e identificação de espécies, o que pode ser aplicado em diversos campos. Por fim, este trabalho contribui para identificar espécies florestais nativas, por meio do desenvolvimento de uma metodologia de análise e processamento de imagens e da aplicação de técnicas de aprendizado de máquina em sementes florestais.Abstract: The identification and characterization of native seeds represent a challenge for the forest sector due to the variability of morphobiometric characteristics. Currently, methodologies for the biometric analysis of forest seeds are carried out by human specialists using traditional measurement methods, such as calipers and variables based on size. In this context, a new methodology was conceived using techniques of digital image processing and machine learning based on external characteristics of the seeds to enable the identification of forest species. The research was divided into three distinct chapters. In the first chapter, a bibliometric analysis was carried out to quantify and analyze scientific studies that address image analysis and machine learning applied to seeds, and thereby point out the main topics and existing gaps for research with forest seeds with this focus. The results indicate a significant increase in publications from 2017 onwards, with a predominant focus on agricultural crop species. These studies are mainly focused on classification, identification/detection of cultivars and evaluation of seed quality, in which only 6.6% of publications address forest species, highlighting the need for further research in this field with native species. In the second chapter, a methodology for capturing and processing images for the characterization and differentiation of native forest species was proposed. The results showed that the analysis of seed images, using this methodology, contributed to the characterization and differentiation of forest species native to Brazil, which has direct implications for silvicultural, ecological, and genetic aspects. In the third chapter, different machine learning classifiers associated with image analysis were applied to identify native forest species based on morphobiometric characteristics of seeds. The results revealed that it is possible to identify native forest species with a satisfactory rate of accuracy using seed images and machine learning. The decision tree classifier is recommended for species identification. The results provide important subsidies to improve the characterization and identification of species, which can be applied in several fields. Finally, this work contributes to identify native forest species, through the development of an image analysis and processing methodology and the application of machine learning techniques in forest seeds

Seed morphometry is suitable for apple-germplasm diversity-analyses

The main objective of this study was to evaluate the trustworthiness of seed image analysis as an approach to discriminate apple germplasm accessions. Digital images of seeds from 42 apple cultivars, acquired by a flatbed scanner, provided a phenotypic dataset with 106 morphometric variables. Stepwise Linear Discriminant Analysis (LDA) was used to examine this dataset, and the results were compared with available genetic data. The first comparison among cultivars provided a 38.8% cross-validation of correct identifications with a discriminant percentage ranging between 11.7 and 70%. In agreement with the genetic diversity analysis, the LDA could discriminate between the apples cultivars, identifying two main groups that could be further divided into additional subgroups. Based on our findings, we propose that seed image analysis is a valuable and affordable tool to investigate phenotypic diversity among a large number of apple cultivars