Search results for: precision agriculture

Authors: Chongya Song, Kang Yen, Alexander Pons, Jin Liu

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In the field of machine learning, the ensemble has been employed as a common methodology to improve the performance upon multiple base classifiers. However, the true predictions are often canceled out by the false ones during consensus due to a phenomenon called “curse of correlation” which is represented as the strong interferences among the predictions produced by the base classifiers. In addition, the existing practices are still not able to effectively mitigate the problem of imbalanced classification. Based on the analysis on our experiment results, we conclude that the two problems are caused by some inherent deficiencies in the approach of consensus. Therefore, we create an enhanced ensemble algorithm which adopts a designed rank-based chain-mode consensus to overcome the two problems. In order to evaluate the proposed ensemble algorithm, we employ a well-known benchmark data set NSL-KDD (the improved version of dataset KDDCup99 produced by University of New Brunswick) to make comparisons between the proposed and 8 common ensemble algorithms. Particularly, each compared ensemble classifier uses the same 22 base classifiers, so that the differences in terms of the improvements toward the accuracy and reliability upon the base classifiers can be truly revealed. As a result, the proposed rank-based chain-mode consensus is proved to be a more effective ensemble solution than the traditional consensus approach, which outperforms the 8 ensemble algorithms by 20% on almost all compared metrices which include accuracy, precision, recall, F1-score and area under receiver operating characteristic curve.

Keywords: consensus, curse of correlation, imbalance classification, rank-based chain-mode ensemble

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Authors: Omid Ghollipoor Bashiri, Farzam Hatefi

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Dichlorvos a synthetic organophosphate poisons are used as insecticide. These toxins can be used insecticides in agriculture and medicine for destruction and/or eradication of ectoparasites of animals. Studies have shown that Dichlorvos creation seizure effects in different animals. Amlodipine, dihydropyridine calcium channel blockers, widely used for treatment of cardiovascular diseases. Studies have shown that the calcium channel blockers are anticonvulsant effects in different animal models. The aim of this study was to determine the effect of Amlodipine on Dichlorvos-induced seizures in mice. In this experiment, the animals were received different doses of Amlodipine (2.5, 5, 10, 20 and 40 mg/ kg b.wt.) intraperitoneally 30 min before intraperitoneal injection of Dichlorvos (50 mg/kg b.wt). After Dichlorvos injection, clonic and tonic seizures, and finally was the fate was investigated. Results showed that Amlodipine dose-dependently reduced the severity of Dichlorvos-induced seizures, so that Amlodipine at a dose of 5mg (The lowest, p<0.05) and 40 mg/kg b.wt. (The highest, p<0.001) which had anticonvulsant effects. The anticonvulsant activity of Amlodipine suggests that possibly due to the antagonistic effect on voltage-dependent calcium channel.

Keywords: dichlorvos, amlodipine, seizures, mice

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Authors: Simone Leonardi, Luca Ardito

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Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.

Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media

Procedia PDF Downloads 187

Authors: R. K. Patel

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The experiment entitled “Bionomics of Cryptophlebia ombrodelta Lower (Lepidoptera: Tortricidae), a Major Pest of Tamarind, Tamarindus indica in Bastar tribal belt of Chhattisgarh” was conducted at S. G. College of Agriculture and Research Station, Jagdalpur (Chhattisgarh) during 2014-15. The moth, Cryptophlebia ombrodelta (Lower) is very destructive pest to tamarind, Tamarindus indica. The mature larva is pinkish in colour whereas, the moth is generally grayish in colour and it lays pale yellowish - white, flat and round eggs near the peduncle joint of pod (fruit) or on the pod surface. The newly hatched larva enters into the fruit by making hole packed with excreta. It completes three to four generation in a year and can cause fourty two per cent loss to tamarind fruits. The morphological details of this pest were studied.

Keywords: bionomics, Cryptophlebia ombrodelta, loss, pest, Tamarind, Tamarindus indica

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Authors: Aya Effat, Ossama A. Hosny, Elkhayam M. Dorra

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Estimating costs is a crucial step in construction management and should be completed at the beginning of every project to establish the project's budget. The precision of the cost estimate plays a significant role in the success of construction projects as it allows project managers to effectively manage the project's costs. Site overhead costs constitute a significant portion of construction project budgets, necessitating accurate prediction and management. These costs are influenced by a multitude of factors, requiring a thorough examination and analysis to understand their relative importance and impact. Thus, the main aim of this research is to enhance the contractor’s ability to predict and manage site overheads by identifying and analyzing the main factors influencing the site overheads costs in the Egyptian construction industry. Through a comprehensive literature review, key factors were first identified and subsequently validated using a thorough comparative analysis of data from 55 real-life construction projects. Through this comparative analysis, the relationship between each factor and site overheads percentage as well as each site overheads subcategory and each project construction phase was identified and examined. Furthermore, correlation analysis was done to check for multicollinearity and identify factors with the highest impact. The findings of this research offer valuable insights into the key drivers of site overhead costs in the Egyptian construction industry. By understanding these factors, construction professionals can make informed decisions regarding the estimation and management of site overhead costs.

Keywords: comparative analysis, cost estimation, construction management, site overheads

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Authors: Nurul-Akidah M. Yusak, Rahmah Mohamed, Noor Zuhaira Abd Aziz

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The introduction of degradable plastic materials into agricultural sectors has represented a promising alternative to promote green agriculture and environmental friendly of modern farming practices. Major challenges of developing degradable agricultural films are to identify the most feasible types of degradation mechanisms, composition of degradable polymers and related processing techniques. The incorrect choice of degradable mechanisms to be applied during the degradation process will cause premature losses of mechanical performance and strength. In order to achieve controlled process of agricultural film degradation, the compositions of degradable agricultural film also important in order to stimulate degradation reaction at required interval of time and to achieve sustainability of the modern agricultural practices. A set of photodegradable polyethylene based agricultural film was developed and produced, following the selective optimization of processing parameters of the agricultural film manufacturing system. Example of agricultural films application for oil palm seedlings cultivation is presented.

Keywords: photodegradable polyethylene, plasticulture, processing schemes

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Authors: Awais Ahmad, Mudssar Ali

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Berseem is the major fodder crop grown in Pakistan and is highly preferred by cattle farmers due to its multicut nature and nutritious value. The quality seed production in berseem is largely dependent upon the activities of insect pollinators, particularly bees. In order to determine the effectiveness of native bee species in quality seed production of berseem, an experiment was conducted in the research field of MNS-University of Agriculture, Multan, Pakistan. The pollinator community of berseem was composed of four bees, three syrphid fly, and two butterfly species. Pesudapis sp. was the most abundant insect visitor, followed by Apis mellifera and A. dorsata. The visitation rate of A. mellifera was found highest, followed by Pesudapis sp. and A. dorsata. Moreover, single-visit efficacy in terms of seed per head and 1000 seed weight proved A. mellifera and Pesudapis sp as the most effective pollinators. Conserving these bee species may lead to sustainable berseem seed production in Pakistan.

Keywords: honey bees, syrphid fly, visitation rate, single visit

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Authors: S. Aswath, Chinmaya Krishna Tilak, Amal Suresh, Ganesh Udupa

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There are technologies to control a humanoid robot in many ways. But the use of Electromyogram (EMG) electrodes has its own importance in setting up the control system. The EMG based control system helps to control robotic devices with more fidelity and precision. In this paper, development of an electromyogram based interface for human gesture recognition for the control of a humanoid robot is presented. To recognize control signs in the gestures, a single channel EMG sensor is positioned on the muscles of the human body. Instead of using a remote control unit, the humanoid robot is controlled by various gestures performed by the human. The EMG electrodes attached to the muscles generates an analog signal due to the effect of nerve impulses generated on moving muscles of the human being. The analog signals taken up from the muscles are supplied to a differential muscle sensor that processes the given signal to generate a signal suitable for the microcontroller to get the control over a humanoid robot. The signal from the differential muscle sensor is converted to a digital form using the ADC of the microcontroller and outputs its decision to the CM-530 humanoid robot controller through a Zigbee wireless interface. The output decision of the CM-530 processor is sent to a motor driver in order to control the servo motors in required direction for human like actions. This method for gaining control of a humanoid robot could be used for performing actions with more accuracy and ease. In addition, a study has been conducted to investigate the controllability and ease of use of the interface and the employed gestures.

Keywords: electromyogram, gesture, muscle sensor, humanoid robot, microcontroller, Zigbee

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: Supriady R. P. Siregar, Rizki Barkah Aulia, Dhiya Fadilla Dewi

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Polybag is a plastic that is used to seed plants. The common type that used for polybag is a synthetic that made from petroleum such as polyethylene. Beside the character of the raw material that are non-renewable and limited, synthetic polybag ability to disintegrate in the environment is very low. According to that situation, we need a solution to overcome these problems by creating an environmentally friendly polybag. In this research, using the water hyacinth plant fibers (Eichornia crassipes) as a major component in manufacturing the environmentally friendly polybag, the water hyacinth (Eichornia crassipes) contains approximately 60% cellulose. The research method used is an experiment by testing the mechanical characters and biodegradability bio-polybag water hyacinth fibers (Eichornia crassipes) on three medium that is dissolved in water, river water and buried in soil. The research shows bio-polybag of hyacinth fibers can rapidly degraded. This study is expected to be the beginning of the creation bio-polybag of water hyacinth fiber (Eichornia crassipes) and can be applied in agriculture.

Keywords: revolution, biopolybag, renewable, environment

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Authors: S. Visetpotjanakit, N. Nakkaew

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Determination of ⁹⁰Sr in environmental samples has been widely developed with several radioanlytical methods and radiation measurement techniques since ⁹⁰Sr is one of the most hazardous radionuclides produced from nuclear reactors. Liquid extraction technique using di-(2-ethylhexyl) phosphoric acid (HDEHP) to separate and purify ⁹⁰Y and Cherenkov counting using liquid scintillation counter to determine ⁹⁰Y in secular equilibrium to ⁹⁰Sr was developed and performed at our institute, the Office of Atoms for Peace. The approach is inexpensive, non-laborious, and fast to analyse ⁹⁰Sr in environmental samples. To validate our analytical performance for the accurate and precise criteria, determination of ⁹⁰Sr using the IAEA-TEL-2016-04 ALMERA proficiency test samples were performed for statistical evaluation. The experiment used two spiked tap water samples and one naturally contaminated spruce needles sample from Austria collected shortly after the Chernobyl accident. Results showed that all three analyses were successfully passed in terms of both accuracy and precision criteria, obtaining “Accepted” statuses. The two water samples obtained the measured results of 15.54 Bq/kg and 19.76 Bq/kg, which had relative bias 5.68% and -3.63% for the Maximum Acceptable Relative Bias (MARB) 15% and 20%, respectively. And the spruce needles sample obtained the measured results of 21.04 Bq/kg, which had relative bias 23.78% for the MARB 30%. These results confirm our analytical performance of ⁹⁰Sr determination in water and spruce needles samples using the same developed method.

Keywords: ALMERA proficiency test, Cerenkov counting, determination of 90Sr, environmental samples

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Authors: Ersin Karakaya, Halit Tutar

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Reliable foods must be consumed in terms of healthy nutrition. The production and dissemination of diatom products in Turkey is rapidly evolving on the basis of preserving ecological balance, ensuring sustainability in agriculture and offering quality, reliable products to consumers. In this study, year in Turkey as (2002- 2015) to determine values of such as cultivated land of organic vegetable production, production levels, production quantity, number of products, number of farmers. It is intended to make the econometric analysis of the factors affecting the production of organic vegetable production (Number of products, Number of farmers and cultivated land). The main material of the study has created secondary data in relation to the 2002-2015 period as organic vegetable production in Turkey and regression analysis of the factors affecting the value of production of organic vegetable is determined by the Least Squares Method with EViews statistical software package.

Keywords: number of farmers, cultivated land, Eviews, Turkey

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Authors: Muhammad Luqman, Mujahid Karim

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Agricultural journalism considered an effective tool in the diffusion of agricultural technologies among the members of farming communities. Various agricultural journalism forms are used by the different organization in order to address the community problems and provide solutions to them. The present study was conducted for analyzing the role of agricultural journalism in the dissemination of agricultural information. The universe of the study was district Sargodha from which a sample of 100 was collected through a validating and pre-tested questionnaire. Statistical analysis of collected data was done with the help of SPSS. It was concluded that majority (64.6%) of the respondent were middle-aged (31-50) years, also indicates a high (73.23%) literacy rate above middle-level education, most (78.3%) of the respondents were connected with the occupation of farming. In various forms of agricultural journalism “Radio/T.V./F.M) is used by 99.4% of the respondent, Mobile phones (96%), Magazine/ Newspaper/ periodical (66.4%) and social media (60.9%). Regarding majors areas focused on agriculture journalism “Help farmers to enhance their productivity is on the highest level with a mean of ( =3.98/5.00). The regression model of farmer's education and various forms of agricultural journalism facilities used was found to be significant.

Keywords: agricultural information, journalism, farming community, technology diffusion and adoption

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Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

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A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

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Authors: Ehikhamele Isaac Erhomosele, Ogbogu Sunday Sylvester

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A biodiversity survey was conducted in Shasha Forest Reserve, Ife southern, Osun State, Nigeria between May 2019 to April 2021 with a view to determining the nature of Odonata fauna of the forest. A total number of 1055 individuals of adult dragonflies and damselflies belonging to 8 families (Aeshnidae, Calopterygidae, Chlorocyphidae, Coenagrionidae, Gomphidae, Lestidae, Libellulidae and Platycnemididae) were recorded. Five (5) of these families of which belong to the suborder Zygoptera, and the remaining three (3) to Anisoptera. Libellulidae was the most abundant family while Gomphidae recorded the least representative. No new species of Odonata was recorded. Most of the families reported were previously been documented in the tropical region. Logging activities and human disturbance have been attributed to the nature of Odonata species in the Forest. It is therefore recommended that logging should be restricted to designated areas of the forest and regulated by the Department of Forestry of Osun State Ministry of Agriculture and Natural Resources in conjunction with the Federal Ministry of Environment.

Keywords: checklist, Odonata, Shasha, families

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Authors: Dionisio Andujar, Juan lópez-Correa, Hugo Moreno, Angela Ri

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The increased number of weeds in tomato crops highly lower yields. Weed identification with the aim of machine learning is important to carry out site-specific control. The last advances in computer vision are a powerful tool to face the problem. The analysis of RGB (Red, Green, Blue) images through Artificial Neural Networks had been rapidly developed in the past few years, providing new methods for weed classification. The development of the algorithms for crop and weed species classification looks for a real-time classification system using Object Detection algorithms based on Convolutional Neural Networks. The site study was located in commercial corn fields. The classification system has been tested. The procedure can detect and classify weed seedlings in tomato fields. The input to the Neural Network was a set of 10,000 RGB images with a natural infestation of Cyperus rotundus l., Echinochloa crus galli L., Setaria italica L., Portulaca oeracea L., and Solanum nigrum L. The validation process was done with a random selection of RGB images containing the aforementioned species. The mean average precision (mAP) was established as the metric for object detection. The results showed agreements higher than 95 %. The system will provide the input for an online spraying system. Thus, this work plays an important role in Site Specific Weed Management by reducing herbicide use in a single step.

Keywords: deep learning, object detection, cnn, tomato, weeds

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Authors: Zoran Šverko, Saša Vlahinić, Nino Stojković, Ivan Markovinović

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In this article, the classification of target letters is performed using data from the EEG P300 Speller paradigm. Neural networks trained with the results of dynamic connectivity analysis between different brain regions are used for classification. Dynamic connectivity analysis is based on the adaptive window size and the imaginary part of the complex Pearson correlation coefficient. Brain dynamics are analysed using the relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient method (RICI-imCPCC). The RICI-imCPCC method overcomes the shortcomings of currently used dynamical connectivity analysis methods, such as the low reliability and low temporal precision for short connectivity intervals encountered in constant sliding window analysis with wide window size and the high susceptibility to noise encountered in constant sliding window analysis with narrow window size. This method overcomes these shortcomings by dynamically adjusting the window size using the RICI rule. This method extracts information about brain connections for each time sample. Seventy percent of the extracted brain connectivity information is used for training and thirty percent for validation. Classification of the target word is also done and based on the same analysis method. As far as we know, through this research, we have shown for the first time that dynamic connectivity can be used as a parameter for classifying EEG signals.

Keywords: dynamic connectivity analysis, EEG, neural networks, Pearson correlation coefficients

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Authors: Roberto Machado

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We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, analyzing 2,996 studies and synthesizing 41 of them to explore the evolution of data science and its integration into geography. By employing optimization algorithms, we accelerated the review process, significantly enhancing the efficiency and precision of literature selection. Our findings indicate that data science has developed over five decades, facing challenges such as the diversified integration of data and the need for advanced statistical and computational skills. In geography, the integration of data science underscores the importance of interdisciplinary collaboration and methodological innovation. Techniques like large-scale spatial data analysis and predictive algorithms show promise in natural disaster management and transportation route optimization, enabling faster and more effective responses. These advancements highlight the transformative potential of data science in geography, providing tools and methodologies to address complex spatial problems. The relevance of this study lies in the use of optimization algorithms in systematic reviews and the demonstrated need for deeper integration of data science into geography. Key contributions include identifying specific challenges in combining diverse spatial data and the necessity for advanced computational skills. Examples of connections between these two fields encompass significant improvements in natural disaster management and transportation efficiency, promoting more effective and sustainable environmental solutions with a positive societal impact.

Keywords: data science, geography, systematic review, optimization algorithms, supervised learning

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Authors: Shaila Bahl, S. P. Lochab, Pratik Kumar

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Nanotechnology and nanomaterials have attracted researchers from different fields, especially from the field of luminescence. Recent studies on various luminescent nanomaterials have shown their relevance in dosimetry of ionizing radiations for the measurements of high doses using the Thermoluminescence (TL) technique, where the conventional microcrystalline phosphors saturate. Ion beams have been used for diagnostic and therapeutic purposes due to their favorable profile of dose deposition at the end of the range known as the Bragg peak. While dealing with human beings, doses from these beams need to be measured with great precision and accuracy. Henceforth detailed investigations of suitable thermoluminescent dosimeters (TLD) for dose verification in ion beam irradiation are required. This paper investigates the TL response of nanocrystalline BaSO4 doped with Eu to 85 MeV carbon beam. The synthesis was done using Co-precipitation technique by mixing Barium chloride and ammonium sulphate solutions. To investigate the crystallinity and particle size, analytical techniques such as X-ray diffraction (XRD) and Transmission electron microscopy (TEM) were used which revealed the average particle sizes to 45 nm with orthorhombic structure. Samples in pellet form were irradiated by 85 MeV carbon beam in the fluence range of 1X1010-5X1013. TL glow curves of the irradiated samples show two prominent glow peaks at around 460 K and 495 K. The TL response is linear up to 1X1013 fluence after which saturation was observed. The wider linear TL response of nanocrystalline BaSO4: Eu and low fading make it a superior candidate as a dosimeter to be used for detecting the doses of carbon beam.

Keywords: radiation, dosimetry, carbon ions, thermoluminescence

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Authors: Anderson Rocha, Pedro Miguel de Figueiredo Dinis Oliveira Gaspar

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Machine vision has been widely used in recent years in agriculture, as a tool to promote the automation of processes and increase the levels of productivity. The aim of this work is the development of a path recognition algorithm based on image processing to guide a terrestrial robot in-between tree rows. The proposed algorithm was developed using the software MATLAB, and it uses several image processing operations, such as threshold detection, morphological erosion, histogram equalization and the Hough transform, to find edge lines along tree rows on an image and to create a path to be followed by a mobile robot. To develop the algorithm, a set of images of different types of orchards was used, which made possible the construction of a method capable of identifying paths between trees of different heights and aspects. The algorithm was evaluated using several images with different characteristics of quality and the results showed that the proposed method can successfully detect a path in different types of environments.

Keywords: agricultural mobile robot, image processing, path recognition, hough transform

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Authors: Ciro Sobrinho Campolina Martins, Halison da Silva Pereira, Vitor Mainenti Leal Lopes

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Unmanned Aerial vehicles (UAVs) has been used in a wide range of applications, from precise agriculture monitoring for irrigation and fertilization to military attack missions. Long range performance is required for many of these applications. Tailless aircrafts are commonly used as long-range configurations and, due to its small amount of stability, the airfoil shape design of its wings plays a central role on the performance of the airplane. In this work, a new family of flying wing airfoils is designed for low Reynolds number flows, typical of small-middle UAVs. Camber, thickness and their maximum positions in the chord are variables used for the airfoil geometry optimization. Aerodynamic non-dimensional coefficients were obtained by the well-established Panel Method. High efficient airfoils with small pitch moment coefficient are obtained from the analysis described and its aerodynamic polars are plotted.

Keywords: airfoil design, flying wing, low Reynolds number, tailless aircraft, UAV

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Authors: Jianghao Hu, Hongyu Wang

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The effective and efficient human pose estimation algorithm is an important task for real-time human pose estimation on mobile devices. This paper proposes a light-weight human key points detection algorithm, Light-Weight Network for Real-Time Pose Estimation (LWPE). LWPE uses light-weight backbone network and depthwise separable convolutions to reduce parameters and lower latency. LWPE uses the feature pyramid network (FPN) to fuse the high-resolution, semantically weak features with the low-resolution, semantically strong features. In the meantime, with multi-scale prediction, the predicted result by the low-resolution feature map is stacked to the adjacent higher-resolution feature map to intermediately monitor the network and continuously refine the results. At the last step, the key point coordinates predicted in the highest-resolution are used as the final output of the network. For the key-points that are difficult to predict, LWPE adopts the online hard key points mining strategy to focus on the key points that hard predicting. The proposed algorithm achieves excellent performance in the single-person dataset selected in the AI (artificial intelligence) challenge dataset. The algorithm maintains high-precision performance even though the model only contains 3.9M parameters, and it can run at 225 frames per second (FPS) on the generic graphics processing unit (GPU).

Keywords: depthwise separable convolutions, feature pyramid network, human pose estimation, light-weight backbone

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Authors: D. S. Mohale, A. P. Dewani, A. S.tripathi, A. V. Chandewar

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Present work demonstrates the applicability of high-performance liquid chromatography (HPLC) with UV-Vis detection for the quantification of malondialdehyde as malondialdehyde-thiobarbituric acid complex (MDA-TBA) in-vivo in rats. The HPLC method for MDA-TBA was achieved by isocratic mode on a reverse-phase C18 column (250mm×4.6mm) at a flow rate of 1.0mLmin−1 followed by detection at 532 nm. The chromatographic conditions were optimized by varying the concentration and pH of water followed by changes in percentage of organic phase optimal mobile phase consisted of mixture of water (0.2% triethylamine pH adjusted to 2.3 by ortho-phosphoric acid) and acetonitrile in ratio (80:20v/v). The retention time of MDA-TBA complex was 3.7 min. The developed method was sensitive as limit of detection and quantification (LOD and LOQ) for MDA-TBA complex were (standard deviation and slope of calibration curve) 110 ng/ml and 363 ng/ml respectively. Calibration studies were done by spiking MDA into rat plasma at concentrations ranging from 500 to 1000 ng/ml. The precision of developed method measured in terms of relative standard deviations for intra-day and inter-day studies was 1.6–5.0% and 1.9–3.6% respectively. The HPLC method was applied for monitoring MDA levels in rats subjected to chronic treatment of levofloxacin (LEV) (5mg/kg/day) for 21 days. Results were compared by findings in control group rats. Mean peak areas of both study groups was subjected for statistical treatment to unpaired student t-test to find p-values. The p value was <0.001 indicating significant results and suggesting increased MDA levels in rats subjected to chronic treatment of LEV of 21 days.

Keywords: malondialdehyde-thiobarbituric acid complex, levofloxacin, HPLC, oxidative stress

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Authors: Beng Yew Low, Cher Liang Cha, Cheng Yong Teoh

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Due to a lack of continual assessment or grade related data, identifying first-year engineering students in a polytechnic education at risk of failing is challenging. Our experience over the years tells us that there is no strong correlation between having good entry grades in Mathematics and the Sciences and excelling in hardcore engineering subjects. Hence, identifying students at risk of failure cannot be on the basis of entry grades in Mathematics and the Sciences alone. These factors compound the difficulty of early identification and intervention. This paper describes the development of a predictive analytics model in the early detection of students at risk of failing and evaluates its effectiveness. Data from continual assessments conducted in term one, supplemented by data of student psychological profiles such as interests and study habits, were used. Three classification techniques, namely Logistic Regression, K Nearest Neighbour, and Random Forest, were used in our predictive model. Based on our findings, Random Forest was determined to be the strongest predictor with an Area Under the Curve (AUC) value of 0.994. Correspondingly, the Accuracy, Precision, Recall, and F-Score were also highest among these three classifiers. Using this Random Forest Classification technique, students at risk of failure could be identified at the end of term one. They could then be assigned to a Learning Support Programme at the beginning of term two. This paper gathers the results of our findings. It also proposes further improvements that can be made to the model.

Keywords: continual assessment, predictive analytics, random forest, student psychological profile

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Authors: Khin Nyunt Yee, Mu Mu Thein

Abstract:

Detection survey of mango seed and Pulp weevils was undertaken at major mango production areas, Yat Sauk, Taunggyi, Nyaung Shwe and Hopong Townships, in Shan State (South) of Myanmar on two mango cultivars of Sein Ta Lone and Yinkwe from May to August 2016 to coincide with fruiting season to conduct a survey of mango seed and pulp weevils population. The total numbers of 6300 fruits of both mango cultivars were sampled. Among them, 2900 fruits from 5674 fruit bearing plants were collected for Sein Ta Lone cultivar of five well managed, one unmanaged orchards and Urban in Yatsauk Twonship, 400 fruits from only one well managed orchard in Taunggyi Township, 400 fruits from two managed orchards in Nyaung Shwe Township and 400 fruits from one managed orchard in Hopong Township from May to June. 2200 fruits were collected from 4043 fruit bearing plants for Yinkwe Cultivar of four well managed orchards, one unmanaged orchards and one wild tree only in Yat Sauk Township from July to August, 2016. Fruit sample size was 200 fruits /orchard, / wild or /volunteer trees as minimum number. The pulps of all randomly sampling fruits were longitudinal cut open into three slices on each side of fruit and seed were cut longitudinally to inspect the presence of mango weevils. The collected weevils were identified up to species level at Plant Quarantine Laboratory, Plant Protection Division, Department of Agriculture, Ministry of Agriculture, Livestock and Irrigation, Yangon, Myanmar. Mango Pulp and Seed weevils were found on Sein Ta Lone Mango Cultivar in three out of four surveyed Townships except Hopong with the level of infestation ranged from 0.0% to 3.5% of fruits per Township with 0.0% to 39.0% of fruits per orchard. The highest infestation rate per township was 3.5% of fruits (n=400 fruits) in Nyaung Shwe, then, at Yat Suak, the rate was 2.47% (n=2900 fruits). A well-managed orchard at Taung Gyi had 0.75% (n=400 fruits) whereas Hopong was free 0.0% (n=400). The weevils were also recorded on Yinkwe Mango Cultivar in Yatsauk Township where the infestation level was 12.63% of fruits (n=2200) with 0.0% to 67.0% of fruits per orchard. This high level of infestation was obtained by including an absolutely non Integrated Pest Management (non IPM) orchards in both survey with the infestation rates 63.0% of fruits (n=200) and 67.0% of fruits (n=200) respectively on Yinkwe cultivar. Two different species; mango pulp weevil, Sternochetus frigitus, and mango seed weevil Sternochetus olivieri (Faust) of family Curculionidae under the order Coleoptera were recorded. Sternochetus mangiferae was not found during these surveys. Three different developmental stages of mango seed and pulp weevils: larva, pupa and adult were first detected since the first survey in 3rd week of May and mostly were recorded as adult stages in the following surveys in June, July and August The number of Mango pulp weevil was statistically higher than that of mango seed weevils at P < 0.001%. More precise surveys should be carried out national wide to detect the mango weevils.

Keywords: mango pulp weevil, Sternochetus frigitus, mango seed weevil Sternochetus olivieri, faust, Sternochetus mangiferae, fabricius, Sein Ta Lone, Yinkwe mango cultivars, Shan State (South) Myanmar

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Authors: W. A. D. P. Wanigasundera, P. C. B. Alahakoon

Abstract:

Kandy district in Sri Lanka has small scale and rain-fed paddy farming, and highly vulnerable to climate change. In this study, the status of climate change was assessed using meteorological data and compared with the perceptions of paddy farming community. Factors affecting the adaptation to the climate smart farming were also assessed. Meteorological data for 33 years were collected and the changes over time compared with the perceptions of farmers. The temperature, rainfall and number of rainy days have increased in both locations. The onset of rains also has shifted. The perceptions of the majority of the farmers were in line with the actual changes. The knowledge and attitudes about the causes of climate change and adaptation were medium and related to level of adoption. Formulating effective communication strategies, and a collaborative approach involving state, private sector, civil society to make Sri Lankan agriculture ‘climate-smart’ is urgently needed.

Keywords: adaptation of climate-smart technology, climate change, perception, rain-fed paddy

Procedia PDF Downloads 338

Authors: Jelena Bašić, Višnjica Vučetić, Mislav Anić, Tomislav Bašić

Abstract:

Lack of precipitation combined with high temperatures causes great damage to the agriculture and economy in Croatia. Therefore, it is important to understand water circulation and balance. We decided to gain a better insight into the spatial distribution of water balance components (WBC) and their long-term changes in Croatia. WBC are precipitation (P), potential evapotranspiration (PET), actual evapotranspiration (ET), soil moisture content (S), runoff (RO), recharge (R), and soil moisture loss (L). Since measurements of the mentioned components in Croatia are very rare, the Palmer model has been applied to estimate them. We refined method by setting into the account the corrective factor to include influence effects of the wind as well as a maximum soil capacity for specific soil types. We will present one hundred years’ time series of PET and ET showing the trends at few meteorological stations and a comparison of components of two climatological periods. The meteorological data from 109 stations have been used for the spatial distribution map of the WBC of Croatia.

Keywords: croatia, long-term trends, the palmer method, water balance components

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Authors: Huseyin Canci

Abstract:

Chlorophylls are green photosynthetic pigment in plants. Therefore, photosynthesis in plants occurs in the leaves. Roles of chlorophylls help plants to get energy from light. The aim of the present study is to compare of chlorophyll contents in some bean species including common bean (Phaseolus vulgaris L.) and runner bean (P. coccineous L.) and genotypes. This research was carried out in fields of Faculty of Agriculture, Akdeniz University in Antalya. Species and genotypes were grown in 2 m single row and 50 cm row spacing. A randomized blocks design was used with two replications. Totally, 124 beans species and genotypes which 122 common beans and 2 runner beans were sown on February, 17th 2014 by hand. Chlorophyll a + b (SPAD values) were determined seedling stage, days to flowering 50% and pod setting stage on bean genotypes. Results showed that there were significant differences for genotypes, stages and interaction of genotypes X stages. There was statistically significant relationships between yield and chlorophyll content of bean species and genotypes.

Keywords: bean, chlorophyll, Phaseolus, SPAD values

Procedia PDF Downloads 247

Authors: Azar Khodabakhshi, Elham Bolandnazar

Abstract:

Agriculture as the most important food manufacturing sector is not only the energy consumer, but also is known as energy supplier. Using energy is considered as a helpful parameter for analyzing and evaluating the agricultural sustainability. In this study, the pattern of energy consumption of strawberry greenhouses of Jiroft in Kerman province of Iran was surveyed. The total input energy required in the strawberries production was calculated as 113314.71 MJ /ha. Electricity with 38.34% contribution of the total energy was considered as the most energy consumer in strawberry production. In this study, Neuro Fuzzy networks was used for function modeling in the production of strawberries. Results showed that the best model for predicting the strawberries function had a correlation coefficient, root mean square error (RMSE) and mean absolute percentage error (MAPE) equal to 0.9849, 0.0154 kg/ha and 0.11% respectively. Regards to these results, it can be said that Neuro Fuzzy method can be well predicted and modeled the strawberry crop function.

Keywords: crop yield, energy, neuro-fuzzy method, strawberry

Procedia PDF Downloads 386

Authors: O. O. Sufyani, M. E. Oraiby, S. A. Qumaiy, A. I. Alaamri, Z. M. Eisa, A. M. Hakami, M. A. Attafi, O. M. Alhassan, W. M. Elsideeg, E. M. Noureldin, Y. A. Hobani, Y. Q. Majrabi, I. A. Khardali, A. B. Maashi, A. A. Al Mane, A. H. Hakami, I. M. Alkhyat, A. A. Sahly, I. M. Attafi

Abstract:

According to the Food and Agriculture Organization (FAO) Pesticides Use Database, pesticide use in agriculture in Saudi Arabia has more than doubled from 4539 tons in 2009 to 10496 tons in 2019. Among pesticides, pyrethroids is commonly used in Saudi Arabia. Pesticides may increase susceptibility to a variety of diseases, particularly among pesticide workers, due to their extensive use, indiscriminate use, and long-term exposure. Therefore, analyzing blood chemo-profiles and evaluating the detected substances as biomarkers for pyrethroid pesticide exposure may assist to identify and predicting adverse effects of exposure, which may be used for both preventative and risk assessment purposes. The purpose of this study was to (a) analyze chemo-profiling by Gas Chromatography-Mass Spectrometry (GC-MS) analysis, (b) identify the most commonly detected chemicals in a time-exposure-dependent manner using a Venn diagram, and (c) identify their associated disease among pesticide workers using analyzer tools on the Comparative Toxicogenomics Database (CTD) website, (250 healthy male volunteers (20-60 years old) who deal with pesticides in the Jazan region of Saudi Arabia (exposure intervals: 1-2, 4-6, 6-8, more than 8 years) were included in the study. A questionnaire was used to collect demographic information, the duration of pesticide exposure, and the existence of chronic conditions. Blood samples were collected for biochemistry analysis and extracted by solid-phase extraction for gas chromatography-mass spectrometry (GC-MS) analysis. Biochemistry analysis reveals no significant changes in response to the exposure period; however, an inverse association between the albumin level and the exposure interval was observed. The blood chemo-profiling was differentially expressed in an exposure time-dependent manner. This analysis identified the common chemical set associated with each group and their associated significant occupational diseases. While some of these chemicals are associated with a variety of diseases, the distinguishing feature of these chemically associated disorders is their applicability for prevention measures. The most interesting finding was the identification of several chemicals; erucic acid, pelargonic acid, alpha-linolenic acid, dibutyl phthalate, diisobutyl phthalate, dodecanol, myristic Acid, pyrene, and 8,11,14-eicosatrienoic acid, associated with pneumoconiosis, asbestosis, asthma, silicosis and berylliosis. Chemical-disease association study also found that cancer, digestive system disease, nervous system disease, and metabolic disease were the most often recognized disease categories in the common chemical set. The hierarchical clustering approach was used to compare the expression patterns and exposure intervals of the chemicals found commonly. More study is needed to validate these chemicals as early markers of pyrethroid insecticide-related occupational disease, which might assist evaluate and reducing risk. The current study contributes valuable data and recommendations to public health.

Keywords: occupational, toxicology, chemo-profiling, pesticide, pyrethroid, GC-MS

Procedia PDF Downloads 107