In this study, PC technique was used to reduce the number of spectral bands or spatial variables in a data set by finding their linear combinations. To apply the PCA for spectral/spatial data set, Landsat TM data recorded from 5 different areas in Central Iran, and 18 soil varaibales were used. The result of PCA transformation for TM bands revealed the importance of PC1 in soil studies, PC2 and or PC3 for vegetation investigations. The results of PCA for Landsat TM and soil data showed that the TM data of 7 bands and 18 soil variables v/ere mainly compressed to just three PCs that describe more than 90% and 55% of spectral and spatial information respectively. Based on the obtained results we may conclude that PCA can be applied to different sources of spectral/spatial data, for a better establishment of sampling plan and save in money and time.

The principal component analysis (PCA) is one of the procedures that have been a successful performance in signal processing and dimension reduction of the signals. However, a requirement in applying PCA to the images is converting images into a vector.This process leads to loss spatial locality information. To solve this problem, the two-dimensional PCA was proposed. Also, most recently the sparse principal component was introduced that not only keep the properties of standard PCA but also try to make a lot of elements of the basis vectors to zero. In this paper, inspired by the above two ideas, the two-dimensional sparse principal component analysis (2-D. SPCA) is proposed.In this paper, the Least Angle Regression- Elastic Net formula, in addition, using l1 and l2 constraints is extended to two-dimensional model with a few minor changes in its input to approach 2-D. SPCA.The two-dimensional sparse principal component analysis is evaluated in image compression. Before applying the algorithm, the image is divided into several blocks with resolution 8×8 and a database of these blocks is formed. Comparison the performance of 2-D. SPCA and Discrete Cosine transform, for the same number of elements that are necessary to save the image after the conversion shows the good performance of the proposed algorithm. In addition, the proposed algorithm is applied to 8×8 blocks of 60 images with different textures, and the resulted two-dimensional sparse principal components could be used for other test images with a suitable performance.

Background and objectives: Sunflower is one of the most important oil plants adapted to different regions of the world. Oil sunflower cultivation in Iran started in 2016 with an area of 1,719 ha and reached 107,000 ha with an average seed yield of 690 kg ha-1 per in 2012. Due to mainly economic reasons as well as the incompleteness of the seed supply cycle, the area under sunflower cultivation decreased to about 10.000 ha in the year of 2021. The ability of grow sunflower in spring and summer in temperate and cold climate and the ability to grow it in autumn and winter in southern tropical regions show the ability and adaptability of this plant to different regions of Iran. The possibility of mechanized cultivation, short growing period relative drought tolerance and high oil percentage of 40-45% are the advantages of sunflower cultivation. One of the most important factors in the reduction of the sunflower cultivation area is the lack of access to certified seeds at the time of cultivation, and this is while since 1987 to 2019, a number of 12 hybrids adapted to different conditions of Iran were introduced by the Institute of Breeding and Seedling and Seed Production. The object of this research was to evaluate the compatibility and determine the agronomic value of different sunflower genotypes studied in Shahrood region during two years by the method of principal component analysis (PCA) in different and stable genotypes for introduction for cultivation in the region.Materials and methods: The experiment was conducted in the research farm of the Shahrood Agricultural Research and Training Center in a factorial format in the form of a randomized complete block design in three replications in two crop years 1400 and 1401. In this test, ten promising hybrids, two free pollinator breeding populations of sunflower were compared with the cultivars Zarin, Golsa, Lacomka and Progressus as experimental control cultivars.Results: Based on the results, the two hybrids SUN97-H2 and SUN97-H28 and the Shahed Progress cultivar were ranked first with their seed yield of 4557, 4516 and 4305 kg ha-1, respectively. Examining the performance characteristics and performance components of the examined genotypes based on the environmental change coefficient showed the performance stability of SUN97-H2 and SUN95-H7 hybrids and all four open-pollinated populations. Based on the results of composite variance analysis, a significant difference was observed between the studied genotypes. The main effects of the year on the characteristics of the day of flowering, plant height, weight of 1000 seeds and diameter according to maturity, plant height, weight of 1000 seeds and diameter according to the probability level of 1% and on the trait of plant height at the probability level of 5% were significant. It was not significant on the traits of day to maturity, stem bending, seed yield, oil yield, oil percentage, number of seeds per pod and stem diameter. The main effects of genotype, in addition to the significant effect on the aforementioned traits, were also significant on oil percentage and yield. Also, the interaction between year and genotype on yield, oil percentage, stem bending and number of seeds according to stem diameter was significant. The coefficient of variation in the analysis of variance varied between 2.8 and 26.12 and only in oil performance this coefficient was more than 25. Based on the results of factor analysis, the evaluated traits were divided into two factors. Conclusion: According to the results of this research, there was significant difference between genotypes in terms of yield and yield components.

A proposal is introduced for evaluation of relative color strength of two synthesized basic dyes Yellow 28 and Blue 41 by means of the principal component analysis technique. This property is usually determined by reflectance data of dyed samples at various concentrations. However, acceptable results are only yielded whenever one constant Kubelka-Munk function (K/S) exhibited linear behavior with concentration. In the present study, principal component analysis technique was applied to K/S data to find an optimum linear concentration range. The results showed that an appropriate range of concentration can be recognized by the percent variation for the first eigenvector. In addition, the ratio of eigenvalues corresponding to the first eigenvector in both synthesized and commercial sample (standard) is described as a criterion for determination and control of relative color strength. The results are compared with those obtained by the three common methods namely visual assessment, absorptiometry of colored solution and a weighted Kubelka-Munk function.