Identifying the total number of fruits on trees has long been of interest in agricultural crop estimation work. Yield prediction of fruits in practical environment is one of the hard and significant tasks to obtain better results in crop management system to achieve more productivity with regard to moderate cost. Utilized color vision in machine vision system to identify citrus fruits, and estimated yield information of the citrus grove in-real time. Fruit recognition algorithms based on color features to estimate the number of fruit. In the current research work, some low complexity and efficient image analysis approach was proposed to count yield fruits image in the natural scene. Semi automatic segmentation and yield calculation of fruit based on shape analysis is presented. Color and shape analysis was utilized to segment the images of different fruits like apple, pomegranate obtained under different lighting conditions. First the input sectional tree image was converted from RGB colour space into the colour space transform (i.e., YUV, YIQ, or YCbCr). The resultant image was then applied to the algorithm for fruit segmentation. After it is applied Morphological Operations which is enhanced image then execute Blob counting method which identify the object and count the number of it. Accuracy of this algorithm used in this thesis is 82.21% for images that have been scanned.
The aqueous extract of banana fruits peal was tested for its effect on mitosis . The root tips of Allium cepa were used as plant test system and the bone marrow cells of the albino mice Mus musculus were used as mammalians test system in vivo .Root tips of Allium cepa were treated for four hours with five concentrations of the extract (5 , 10 , 20 , 40 ,60 mg / ml.).The Metaphase was arrested in all the treatments , the highest percentage ( 100 % ) was recorded in the first concentration , the last concentration caused stickiness and clumping of the chromosomes. The treatments did not cause significant difference in the mitotic index. The peals extract (5 mg /ml) was compared with the extracts of fruits bulb, leaves and r
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The aqueous extract of banana fruits peal was tested for its effect on mitosis . The root tips of Allium cepa were used as plant test system and the bone marrow cells of the albino mice Mus musculus were used as mammalians test system in vivo .Root tips of Allium cepa were treated for four hours with five concentrations of the extract (5 , 10 , 20 , 40 ,60 mg / ml.).The Metaphase was arrested in all the treatments , the highest percentage ( 100 % ) was recorded in the first concentration , the last concentration caused stickiness and clumping of the chromosomes. The treatments did not cause significant difference in the mitotic index. The peals extract (5 mg /ml) was compared with the extracts of fruits bulb, leaves and roots of
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Ammi species belong to the family Umbellifereae that provide a host of bioactive compounds (mainly coumarins and flavonoids) of important biological activities, like prevention and treatment of heart and vascular disease and some types of cancer. Literature survey revealed that there was no study concerning Ammi flavonoids in Iraq. Ammi majus and Ammi visnaga, which are wildly grown in Iraq, were chosen for this study. This study concerned with extraction, identification, isolation, and purification of some biologically important flavonols quercetin and kaempferol from the fruits of Ammi majus and Ammi visnaga. Extraction of these flavonols was carried out using 85% methanol and 90% e
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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Abstract
Metal cutting processes still represent the largest class of manufacturing operations. Turning is the most commonly employed material removal process. This research focuses on analysis of the thermal field of the oblique machining process. Finite element method (FEM) software DEFORM 3D V10.2 was used together with experimental work carried out using infrared image equipment, which include both hardware and software simulations. The thermal experiments are conducted with AA6063-T6, using different tool obliquity, cutting speeds and feed rates. The results show that the temperature relatively decreased when tool obliquity increases at different cutting speeds and feed rates, also it
... Show MoreThe issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of
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