The general health of palm trees, encompassing the roots, stems, and leaves, significantly impacts palm oil production, therefore, meticulous attention is needed to achieve optimal yield. One of the challenges encountered in sustaining productive crops is the prevalence of pests and diseases afflicting oil palm plants. These diseases can detrimentally influence growth and development, leading to decreased productivity. Oil palm productivity is closely related to the conditions of its leaves, which play a vital role in photosynthesis. This research employed a comprehensive dataset of 1,230 images, consisting of 410 showing leaves, another 410 depicting bagworm infestations, and an additional 410 displaying caterpillar infestations. Furthermore, the major objective was to formulate a deep learning model for the identification of diseases and pests affecting oil palm leaves, using image analysis techniques to facilitate pest management practices. To address the core problem under investigation, the GoogLeNet deep learning approach was applied, alongside various hyperparameters. The classification experiments were executed across 16 trials, each capped at a computational timeframe of 10 minutes, and the predominant duration spanned from 2 to 7 minutes. The results, particularly derived from the superior performance in Model 4 (M4), showed evaluation accuracy, precision, recall, and F1-score rates of 93.22%, 93.33%, 93.95%, and 93.15%, respectively. These were highly satisfactory, warranting their application in oil palm companies to enhance the management of pest and disease attacks.
The red palm weevil Rhynchophorus ferrugineus (Olivier, 1790), which belongs to the family Curculionidae, order Coleoptera, is one of the most important palm pests, which leads to heavy losses in date palms. In the last few years, it has been recorded in Iraq, from Safwan city south of Iraq, where it was detected on date palm trees (Phoenix dactylifera L, Arecales: Arecaceae). In the current study, specimens of adult weevils were collected from infested date palms (Phoenix dactylifera), that showed signs of infection in Safwan district, south of Basra province. The results of the investigation showed the presence of phoretic mites associated with the red palm weevil Rhynchophorus ferrugineus (Olivier,
... Show MoreA seemingly uncorrelated regression (SUR) model is a special case of multivariate models, in which the error terms in these equations are contemporaneously related. The method estimator (GLS) is efficient because it takes into account the covariance structure of errors, but it is also very sensitive to outliers. The robust SUR estimator can dealing outliers. We propose two robust methods for calculating the estimator, which are (S-Estimations, and FastSUR). We find that it significantly improved the quality of SUR model estimates. In addition, the results gave the FastSUR method superiority over the S method in dealing with outliers contained in the data set, as it has lower (MSE and RMSE) and higher (R-Squared and R-Square Adjus
... Show MoreAbstract
Objective: the idea of this study to improve transdermal permeability of Methotrexate using eucalyptus oil, olive oil and peppermint oil as enhancers.
Method: eucalyptus oil (2% and 4%), peppermint oil (2% and 4%) and olive oil (2% and 4%) all used as natural enhancers to develop transdermal permeability of Methotrexate via gel formulation. The gel was subjected to many physiochemical properties tests. In-vitro release and permeability studies for the drug were done by Franz cell diffusion across synthetic membrane, kinetic model was studied via korsmeyer- peppas equation.
Result: the results demonstrate that safe, nonirritant or cause necrosis to rats' skin and stable till 60 days gel was successfully formulated.<
The detection of diseases affecting plant is very important as it relates to the issue of food security, which is a very serious threat to human life. The system of diagnosis of diseases involves a series of steps starting with the acquisition of images through the pre-processing, segmentation and then features extraction that is our subject finally the process of classification. Features extraction is a very important process in any diagnostic system where we can compare this stage to the spine in this type of system. It is known that the reason behind this great importance of this stage is that the process of extracting features greatly affects the work and accuracy of classification. Proper selection of
... Show MoreBackground: The systemic host modulation therapy is new approach in treatment of periodontal diseases. Materials and methods:The target of this treatment is the host response to microbial infection because at present time,it is well known that most of damage found in periodontal diseases cause by the inflammatory -immune response to periodontal infections.Sub-antimicrobial-dose Doxycycline (SDD) is a 20-mg dose of Doxycycline (Periostat) that is approved and indicated as an adjunct to scaling and root planning (SRP) in the treatment of chronic periodontitis. Results:At present, SDD (Periostat) is the only systemically administered agent that is approved by the U.S. Food and Drug Administration (FDA) and accepted by the American Dental Asso
... Show MoreEconomic performance is one of the most important indicators of economic activity and with the performance of the economy progress varied sources of output and increase economic growth rates and per capita national income, and to recover the business environment and increase investment rates and rising effectiveness of the financial and monetary institutions and credit market. Which leads to increased employment rates and reducing unemployment rates and the elimination of many of the social problems and improve the average per capita income as well as improve the level of national income.
The input / output tables is a technique mathematical indicates economic performance
... Show MoreIn this work, satellite images classification for Al Chabaish marshes and the area surrounding district in (Dhi Qar) province for years 1990,2000 and 2015 using two software programming (MATLAB 7.11 and ERDAS imagine 2014) is presented. Proposed supervised classification method (Modified Vector Quantization) using MATLAB software and supervised classification method (Maximum likelihood Classifier) using ERDAS imagine have been used, in order to get most accurate results and compare these methods. The changes that taken place in year 2000 comparing with 1990 and in year 2015 comparing with 2000 are calculated. The results from classification indicated that water and vegetation are decreased, while barren land, alluvial soil and shallow water
... Show More