Mstatc Software - For Windows 7 - Analysis Agriculture Data

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Project Goals: To develop an integrated microcomputer program that will assist agricultural scientists in most of the steps involved in doing agricultural research-that is to generate experimental designs, manage and transform data, and analyze experiments from both a biological and economical perspective. The program includes the following general features: generate experimental designs; print field books, labels and field maps; sort data; transform data; create histograms; create one and two-way tables; generate descriptive statistics; perform economic analysis; organize plant breeding programs; produce ASCII files; and accept ASCII files.

Abstract:Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars.Keywords: rice; crop; image processing; cultivar; artificial intelligence; artificial neural networks; big data; food informatics; machine learning

Partial least squares regression (PLSR), also known as PLS, is a new type of multivariate statistical analysis method. It is the most common method for developing multidimensional calibration models. It can process linear data and reduce the number of calibration samples required by the gold standard in chemistry [22]. When the dependent variables have a higher linear correlation, PLS can be more effective. PLS is a two-line model based on the matrix x (independent variables) and Y (dependent variables), which can be considered as external and internal relations [21].

Abu-Khalaf [30] studied the quality parameters of olive oil using PLS models to analyze the chemical data and the EN. The results illustrated that the EN could model the acidity parameter with good performance. The correlation coefficients obtained, using the PLS model, for the calibration and validation of acidity were 0.87 and 0.87,, respectively. Zhang, et al. [31] reported similar results using the PLSR method and an electronic nose for grapes, with R2 of 0.93. Zhou and Zeng [12] applied partial least squares spectroscopy and linear audit analysis (PLS-LDA) and found similar results, with an R2 of 0.96.

Coffee is the most important export crop of the south Ethiopian region with more than 46 percent share of the national market. It covers more than 185 000 ha of land in 50 Woredas (districts) with 11 are high, 7 medium and 32 are low coffee producers. Garden coffee comprises 130000 ha, semi forest 45 000 ha and forest coffee 10000 ha where the semi forest and forest coffee production systems are pertinent to the western part of the region. A field experiment on evaluation of 41 south Ethiopian coffee accessions with 2 standard checks of the southwest Ethiopian origin was conducted using Randomized Complete Block Design at Wonago Research Sub-Station during 1999- 2000 cropping seasons. Data on 7 morphological agronomic characters, average of three years data on severity of CBD and CLR infestations and yield was obtained for the 43 genotypes. The germplasm accessions differedsignificantly for all the 7 morphological agronomic characters and coffee bean yield in the univariate analyses of variances indicating the prevalence of variability among south Ethiopian coffee germplasm accessions. Further, the first four principal components explained 82.63 percent of the total variation prevalent within the germplasm accessions out of which 32.52 percent was explained by the first principal component. Average linkage cluster analysis using Mahalanobis (D2) distance for the 10 characters grouped the 43 accessions in to 9 clusters. The number of accessions per cluster ranged from 1 in cluster IX to 13 in cluster II. The clustering pattern of the accessions revealed the prevalence of genetic diversity in the south Ethiopian coffee for the characters considered. The maximum inter-cluster distance was observed between clusters V and VII while the minimum was observed between clusters VI and VII. The study highlighted the possibility of using accessions of the distant clusters as potential candidates for the genetic improvement of south Ethiopian coffee through crossing and selection.

Data were performed by a one-way analysis of variance (ANOVA) for a design type here, that is, complete randomized block design [30]. These results based on combination analysis of the two years. A Mstatc software program (citation) [31] was used to conduct the analysis of variance. A correlation procedure [32] was used to determine the correlation coefficients between soil ammonium concentration, microbial nitrogen, and ?ﬁx for each treatment. The significance of treatment mean was determined at ?

Statistical analysis of data. MSTAT-C and Microsoft Excel and DMRTwere used to measure the variation of mean data of treatments. Treatmentmeans were compared at P [less than or equal to] 0.05. The data were analysedstatistically following computer package MSTATC. All the data werestatistically analysed following the ANOVA technique and the significance ofmean differences was adjusted by Duncan's Multiple Range Test. 2b1af7f3a8