Browsing by Author "ISLAM, RAKIBUL"

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    EFFECT OF DIFFERENT SOURCES OF PHOSPHORUS AND MULCH MATERIALS ON GROWTH AND YIELD OF FRENCH BEAN
    (DEPARTMENT OF HORTICULTURE, 2020) ISLAM, RAKIBUL
    The experiment was conducted in the Horticultural Farm of Sher-e-Bangla Agricultural University, Dhaka -1207, during the period from November 2019 to February 2020 to study the effect of different sources of phosphorus and mulch materials on growth and yield of french bean. The experiment consisted of two factors. Factor A: different sources of phosphorus as P 1 = 100% TSP (control), P 2 = 50% TSP + 50% Vemicompost, P 3 = 50% TSP + 50% Mushroom spent compost, P 4 = 50% Vemicompost + 50% Mushroom spent compost and Factor B: different type of mulch materials as M 1 = No mulch (control), M 2 = White polythene mulch, M 3 = Black polythene mulch. The experiment was laid out in Randomized Complete Block Design with three replications. In case of different sources of phosphorus of french bean the maximum number of flower (29.06), the highest number of pod harvested per plant (24.00) and the highest yield (14.35 t/ha) were found from P 4 treatment, whereas the lowest from P 1 treatment. For different mulch materials the highest number of flower (29.37), the maximum number of pod per plant (24.21) and the highest yield (14.33 t/ha) were recorded from M 2 treatment, while the minimum were from M 0 treatment. Due to combined effect, the maximum number of flower (30.72), the maximum number of pod harvested per plant (26.83), the highest yield (16.76 t/ha) were observed from P 4 II M 2 treatment combination, while the lowest were from P 1 M 0 treatment combination. The highest net return (2,74,100 Tk./ha) was obtained from P 4 M 2 and the lowest (1,42,286 Tk./ha) in P 3 treatment combination. The highest (2.19) benefit cost ratio was obtained from P 4 treatment combination, while the lowest (1.63) in P 3 M 1 treatment combination. So, the P 4 M 2 treatment combination appeared to be the best for achieving the higher growth and yield of french bean.
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    NATIONAL FLAG DETECTION USING CNN
    (Daffodil International University, 2018-12-01) SULTANA, SUMAIYA; ISLAM, RAKIBUL; KHAN, ABU SHAIF
    This research project focus on “National Flag Detection Using Convolutional Neural Network”. This is a kind of image processing work with the help of convolutional neural network. In this project we solve National Flag classification problem, where our goal will be to tell which flag the input image belongs to. We are achieved it by training an artificial neural network on few thousand images of national flag. We make the Neural Network learn to predict which class the image belongs to. Finally, we achieve our goal for detection of national flag using Artificial Neural Network when corresponding national flag is provided. We work with more than 5,000 images. Finally, after developing the project we are able to obtained a satisfactory accuracy that is 98%.