The main purpose of this research is to build up a system, based on standard or objective parameters rather than non-standard or subjective parameters, which are already being employed by researchers, for the crop classification and crop growth analyses. The research is divided into two portions; the first part deals with the crop classification, whereas the second part is concerned with the crop growth analysis. For this purpose two types of datasets have been used; radiometric data and photographic data. In portion 1 radiometric data is acquired by using a handheld crop scan device ‗MSR5‘, in the form of five spectral bands, from 450nm to 1750nm, with five types of wavelength blue, green, red, infrared and far-infrared, whereas, the photographic data is obtained by a digital camera with14.1Mpixels resolution To meet the objectives a system has been developed and employed on two types of data; (a) test data and (b) experimental data. Both types of data (radiographic and photographic) are classified by using ANN classifier. In test data, five land classes are differentiated by this system. Photographic images of the same five types of land classification (as radiometric data) are used to extract following five types of 77 statistical textural features, which may be grouped as; first order (histogram) features, second order (GLCM) features, higher order (GLRM) features, autoregressive features, and gradient matrix based features are calculated from ROI (32x32),(64x64),(128x128),(256x256) and (512x512) by using MaZda software. The most relevant features for each size of ROI are selected by three approaches; Fisher‘s Co-efficient, Probability of Error plus Average Correlation Co-efficient, and Mutual Information Co-efficient. In this way the most relevant 10 features were selected by each method. We receive very poor results when data analysis capability is verified on the basis of 10 features are selected by each method for each size of ROI except (512x512), by three multivariate techniques; PCA, LDA, and NDA available in ‗B11‘, software integrated with MaZda. To improve the results, a set of 20 features is obtained by merging the features selected by each approach. An excellent clustering result with accuracy of 91.9% received, when data of these 20 features extracted from ROI (512x512) was deployed to NDA projection space. By using supervised classification approach, artificial neural network (ANN) the system is trained and tested on the basis of 70% and 30% of input data respectively. We received an accuracy of 100% and 91.33% in training and testing phase respectively. Similarly in radiometric data 250 data instances are taken for five different types of land (50 data instances for each type of land), for training purpose 40 data instances of each land type is used. Total 200 samples out of (250) are used to train the data system. Testing is performed on 50 samples (10 samples from each land type) and 96.40% accuracy result is obtained for radiometric data. On the basis of test data analysis, it is concluded that the proposed system produces the best result for large ROI window size when a combined set of features is deployed in NDA projection space. The photographic experimental data (five different types of crop) is analyzed under these settings. To check the system routine two disjoint sets of data with 70/30 ratio for training and testing respectively are developed ANN classifier available in B11 software under n-class training and testing option. is checked for the settings to which NDA has shown the best performance, Results show that the system training accuracy increases by number of neurons in input layer, and testing accuracy processes up to certain configuration. The best training accuracy of 85.17 and testing accuracy is 81.25% with 7 input layers at learning rate 0.35.For radiometric data ANN is trained and tested. For this purpose 400 scans data (80 scans from each class) is used to train the classifier and the remaining 100 scans data (20 scans from each class) was employed to test the classifier. We received an average accuracy of 94.50% during training and 96.00% accuracy in testing phase. In second part, which is concerned with the crop growth analysis, field data is acquired at different six stages by using crop scan MSR 5 (for radiometric data ) and a Photographic data was acquired by a digital camera mounted at a height by which approximately five square feet area is imaged in each photo. For every stage fifty images of photographic data are acquired from different regions of the crop field and the radiometric data is acquired at the altitude of 10 feet from the ground level. (This way the device scans an area of 5 square feet for each scan). For each stage approximately hundred scans are acquired by the said device from consecutive areas, the crop growth is assessed on the basis of reflectance values of five bands acquired by the devices at different stages. It is also observed that the same wave lengths (IR and FIR) are very helpful for the assessment of crop growth. For the growth assessment, in this study, we explore the changes in canopy spectrum feature of wheat. Reflectance patterns during the growing season expose a large amount of information about the changes in the visible and near-infrared (NIR) wavelengths. There is a rapid raise in the NIR values as soon as the crop develops while the changes in the visible wavebands adjust more slowly. Throughout the growing season the NIR wavelengths are more active than the visible wavelengths. This is due in part the reflectance values for bare soil being closer to the visible reflectance than NIR values. We bring to a close that the presence of infrared and far-infrared wavelengths makes the radiometric data more inventive for classification/differentiation as compared to photographic data.
The Immensely Merciful to all, The Infinitely Compassionate to everyone.
56:01 a. When the Inevitable Event of Doom will descend, and herald the Resurrection,
56:02 a. then there will be no belying and denying of its descent;
56:03 a. it will be degrading and humbling some humans and jinn, and exalting some others.
56:04 a. When the whole of the terrestrial world will be shaken, shaken violently in a series of massive earthquakes and volcanic eruptions,
56:05 a. and the mountains will be made to crumble, utterly crumbling,
56:06 a. so as turning it to dust, scattered in the air like puffed wool.
56:07 a. And you all will be sorted out into the following three diverse categories:
56:08 a. As for the first category: b. the People of the right hand side – the lucky and blessed ones; c. how lucky and blessed will be the People of the right hand side!
56:09 a. And the second category: b. the People of the left hand side – the unlucky and wretched ones; c. how unlucky and wretched will be the People of the left hand side!
56:10 a. And the third category: b. those in the foremost who would have taken the lead c. – indeed they will be the foremost in their race to seek Allah’s Countenance!
56:11 a. For those will be the ones brought near,
56:12 a. in Gardens of Perpetual Bliss - abounding in peace, tranquility, and blessings.
The concept of time management is practice from decades. Time management has grabbed the attention of many scholars and there were many writings and analysis made. Time management is an important issue of human life as you cannot add more hours in a day, you have to plan yourself with the limitation of time. Islam focuses on the time management so that a believer should balance his life (spiritually, socially and economically). Islamic teachings are dynamic in their approach, they teach Muslim from every aspect of life and allow them to spend their time productively. Islam’s main focus is on the planning and organizing the time and our main focus is to depict what Islam teaches about time management and how it is practiced in the world. Then conventional methods of management are similar to the Islamic teachings.
Two hundred and sixty nine species of algae belonging to six phyla, two classes, fifteen orders, thirty three families and seventy five genera have been collected from various freshwater habitats of Pakistan from Gujranwala, Gujrat, Narowal and Sialkot in the province of Punjab, during March 2013 to August 2015. All species have been morphologically and cytologically investigated. All were taxonomically identified and described on the basis of their characteristics according to the recently proposed classification (Shameel, 2001, 2012). From the taxonomic studies of algae from North-east Punjab, Phylum Cyanophycota observed with twenty eight genera and 120 species in two orders that were found to be more prevalent in algal diversity (45.60%) than the phylum Bacillariophyota, which included eighteen genera and forty species in two orders and thus was smaller phylum (14.87%) in algal diversities than Cyanophycota diversity. The result of that Nostocophyceae (31.59%) observed most highly distributed class with single order Nostocales and 6 families with 19 genera and 85 species as compare to the other Cyanophyota class chroocophyceae (13.02%) with 09 genera and 35 species (Table 1). Phylum Volvocophycota revealed with thirteen genera and forty three species in three orders with 15.85% than the phylum Euglenophycota, which included four genera and twenty three species in one order with 8.55% taxonomic distribution in north-east, Punjab, Pakistan. Whereas Phylum Chlorophycota revealed with eleven genera and twenty six species in six orders with 13.38% than the Phylum Vaucheriophycota that included 2 genera and seven species in two orders (Table 2). Great proportion of Cyanophycota (45.60%) was made from north-east Punjab, Pakistan where Phylum Volvocophycota shown 15.98% of algal distribution. It was followed by phylum Bacillariophycota (14.87%), Phylum Chlorophycota (13.38%), Phylum Euglenophycota (8.55%) and Phylum Vaucheriophycota (2.60%) respectively.