TOMATO DISEASE DETECTION MODEL BASED ON DENSENET AND TRANSFER LEARNING

Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%

Evaluation of occupational exposure to TiO2 nanoparticles: microwave-assisted acid digestion method on air membrane filters

Titanium dioxide (TiO2) nanoparticles have been extensively used in various industrial sectors and applications, including cosmetics, catalysts, food additives, inks, paints, and coatings. However, the International Agency for Research on Cancer (IARC) has classified TiO2 nanoparticles as a potential carcinogen for humans, meaning they may cause cancer and pose serious health complications, particularly for manufacturing workers. This highlights the need for better evaluation to determine worker exposure. In this study, suspended TiO2 nanoparticles were sampled using a nanoparticle respiratory deposition (NRD) sampler fitted with specially designed membrane filters and analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The digestion method used for titanium element recovery after nanoparticle sampling is crucial for optimal ICP-MS analysis. Therefore, this study aimed to investigate the most suitable digestion method. A microwave-acid digestion method using concentrated nitric acid and concentrated hydrochloric acid at a 7:4 ratio, with a run time of 30 minutes and the temperature set to 200°C showed remarkable titanium recovery compared to other methods. These findings may pave the way for optimal analysis of suspended TiO2 nanoparticles in assessing occupational exposure while promoting sustainability and eco-friendliness in resource utilization

GenericSOA: a Proposed Framework for Dynamic Service Composition

Abstract Many organizations' developers use Service Oriented Architecture (SOA) in building their systems. It provides them by an easy way to re-use their already developed software components. Now, researchers try to enhance the SOA architecture to improve its performance and scalability. This paper, proposes a new SOA architecture called "GenericSOA" that allows dealing with legacy systems problem and enhancing SOA elasticity. The proposed architecture aims to easily integrating the newly developed software components. The main idea behind GenericSOA is to support its users by a set of predefine task templates. These templates can be used in building the new developed services that can be easily integrated in a loosely coupled way to compose the target system

The potential of agro based nanomaterials for nanofilters to capture suspended titanium nanoparticles in the air

Nanomaterials have a wide range of new technologies and industrial use and have created many new products and employment opportunities. However, they can also pose unknown risks and specific uncertainties in occupational safety and health issues. The latest and most worrying issue involves the increasing production and nanoparticles, particularly titanium dioxide (TiO2). Therefore, a rigorous study should be carried out to obtain more intensive information to develop a new technique for personal exposure monitoring. The commercially available nanoparticle respiratory deposition (NRD) sampler usually occupied with nylon filter contains TiO2 background material and is rather expensive. As an alternative, agro based nanofilters were developed from nanomaterials synthesized from rice husks, namely, nanosilica and nanozeolite embedded on/in a polyvinylidene fluoride (PVDF) membrane. As a comparison, graphene was also used to produce nanofilters due to its outstanding performance in chemical absorption. Analysis using Field Emission Scanning Electron Microscope (FESEM) showed a formation of cracks on both nanofilters when embedded with 1% w/v of either nanosilica and nanozeolite compared to 0.1 and 0.5 % w/v. Agglomerate of nanosilica particles with the size between 20 – 40 nm and nanozeolite with the size between 18 – 30 nm were identified on the developed nanofilter. Energy Dispersive X-ray (EDX) confirmed the presence of functional groups such as silica, oxide, sodium, alumina, and carbon on the developed nanofilters, further confirming the deposition of the nanomaterials on the PVDF membrane. Further investigation on the ability to capture titanium nanoparticles using 0.1 % w/v nanofilters from both materials showed that all filters tested could capture titanium nanoparticles with nanozeolite filters showing the highest accumulation with 9170 mg/m3. These results suggest that agro-based nanomaterials can be used as nanofilters to capture titanium nanoparticles in the air

Cultivation-time Recommender System Based on Climatic Conditions for Newly Reclaimed Lands in Egypt

AbstractThis research proposes cultivation-time recommender system for predicting the best sowing dates for winter cereal crops in the newly reclaimed lands in Farafra Oasis, The Egyptian Western Desert. The main goal of the proposed system is to support the best utilization of farm resources. In this research, predicting the best sowing dates for the aimed crops is based on weather conditions prediction along with calculating the seasonal accumulative growing degree days (GDD) fulfillment duration for each crop. Various Machine Learning (ML) regression algorithms have been used for predicting the daily minimum and maximum air temperature based on historical weather conditions data for twenty-five growing seasons (1990/91 to 2014/15). Experimental results showed that using the M5P and IBk ML regression algorithms have outperformed the other implemented regression algorithms for predicting the daily minimum and maximum air temperature based on historical weather conditions data. That has been measured based on the calculated mean absolute error (MAE). Also, obtained experimental results obviously indicated that the best cultivation-time prediction by the proposed recommender system has been achieved by the M5P algorithm, based on the seasonal accumulative GDD fulfillment duration, for the coming five growing seasons (2016/17 to 2019/20)

Kajian pensampelan dan kaedah analisis untuk nanopartikel

Nanosilika dan nanoziolit yang disintesis dari sekam padi telah dikaji bagi mengenalpasti potensi bahan ini sebagai membran nanofilter berkos rendah sebagai alternatif untuk menggantikan membran nilon yang digunakan dalam sampel “nanoparticle respiratory deposition” (NRD) untuk menangkap nanopartikel titanium dioksida (TiO2) di udara. Oleh kerana keupayaan penjerapannya yang luar biasa terhadap banyak bahan bukan organik, graphene oxide juga diselidiki mengenai potensinya untuk menangkap nanopartikel TiO2. Nanofilter dihasilkan dengan menggunakan pelbagai kepekatan nanomaterial yang disintesis dengan mendepositkannya pada membran polivinilidena fluorida (pvdf) menggunakan kaedah pemendapan lapisan dan selanjutnya dicirikan oleh “field emission scanning microscopy / energy dispersive x-ray” (fesem-edx) analisis. Untuk menyiasat potensinya dalam menangkap nanopartikel, setiap nanofilter didedahkan kepada TiO2 nanopartikel selama 15 minit dengan aliran udara rata-rata 2.5 l/min dan dibandingkan dengan membran nilon konvensional yang digunakan dalam NRD. Walaupun sejumlah unsur Ti dikesan menggunakan analisis “induktif coupled plasma-mass spectrometry” (icp-ms) untuk membran nilon yang terdedah kepada nanopartikel TiO2, namun, analisis icp-ms juga menemui tahap latar belakang ti yang tinggi (1212 μg/g) ) dalam membran nilon yang tidak terpapar. Ini menyebabkan ianya tidak sesuai untuk digunakan dalam pengambilan sampel nanopartikel TiO2. Di antara semua penapis nano yang dikembangkan, penapis nanoziolit (0.1% w/v) menunjukkan kepekatan tertinggi Ti (81.7 μg/g) berbanding dengan nanosilika (56.7 μg/g) dan penapis graphene oxide (8.2 μg/g). Menariknya, semua nanofilter yang dikembangkan tidak menunjukkan adanya Ti menunjukkan spesifikasi terhadap menangkap nanopartikel Ti di udara

Photodegradation of oxytetracycline using fluorescent light driven ZnO quantum dots synthesised via microwave method

In this study, Li+ ions capped zinc oxide quantum dots (ZnO QDs) was synthesised using the microwave method. The X-ray diffraction (XRD), transmission electron microscopy (TEM), high-transmission electron microscopy (HR-TEM), scanning electron microscopy (SEM), UV–Visible diffuse reflectance spectroscopy (UV-DRS), and photoluminescence (PL) techniques were used to characterise the structural, morphological, optical properties of the ZnO QDs. The XRD analysis reveals that ZnO QDs have a hexagonal wurtzite structure with an average crystallite size of 9.9 nm. The morphology of ZnO QDs was observed to be quasi-spherically shaped with an average particle size of 10 nm. The PL analysis detected the presence of various defects. All these factors enhanced the photodegradation of oxytetracycline (OTC) under fluorescent light irradiation. Within 40 min, 88.3% of OTC was removed, which was higher compared to the bulk ZnO reported in the literature. This technology is aimed at small animal husbandries due to the photocatalyst synthesis method’s simplicity and the photocatalysis process’s requirements