Search results for: crop disease detection

Authors: Purnima Pandit

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Malaria remains one of the world’s most deadly infectious disease and arguably, the greatest menace to modern society in terms of morbidity and mortality. To choose the right treatment and to ensure a quality of life suitable for a specific patient condition, early and accurate diagnosis of malaria is essential. It reduces transmission of disease and prevents deaths. Our work focuses on designing an efficient, accurate fuzzy inference system for malaria diagnosis.

Keywords: fuzzy inference system, fuzzy logic, malaria disease, triangular fuzzy number

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Authors: Meysam Abedinpour

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Water limitation is the main challenge for crop production in a semi-arid environment. Deficit irrigation is a strategy that allows a crop to sustain some degree of water deficit in order to reduce costs and potentially increase income. For this goal, a field experimental carried out at Asrieh fields of Gorgan city in the north of Iran, during summer season 2011. The treatments imposed were different irrigation water regimes (i.e. W1:70, W2:80, W3:90, and W4:100) percent of field capacity (FC). The results showed that there was Significant difference between the yield and (WUE) under different levels of irrigation, excepting of soil moisture content at field capacity (W4) and 90% of field capacity (W3) on yield and water use efficiency (WUE). The seasonal irrigation water applied were (i.e. 375, 338, 300, and 263 mm ha-1) under different irrigation water treatments (100, 90, 80, 80 and 70%) of FC, respectively. Grain yield productions under treatments were 4180, 3955, 3640, and 3355 (kg ha-1) respectively. Furthermore, the results showed that water use efficiency (WUE) at different treatments were 7.67, 7.79, 7.74, and 7.75 Kg mm ha-1 for (100, 90, 80, and 70) per cent of field capacity, therefore the 90 % of FC treatment (W3) is recommended for Soybean irrigation for water saving. Furthermore, the result showed that the treatment of 90 % of filed capacity (W3) seemed to be better adapted to product a high crop yield with acceptable yield coupling with water use efficiency in Golestan province.

Keywords: deficit irrigation, water use efficiency, yield, soybean

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Authors: Tian Xia, Yuan Yan Tang

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In order to obtain higher small target detection accuracy, this paper presents an effective algorithm inspired by the local contrast mechanism. The proposed method can enhance target signal and suppress background clutter simultaneously. In the first stage, a enhanced image is obtained using the proposed Weighted Laplacian of Gaussian. In the second stage, an adaptive threshold is adopted to segment the target. Experimental results on two changeling image sequences show that the proposed method can detect the bright and dark targets simultaneously, and is not sensitive to sea-sky line of the infrared image. So it is fit for IR small infrared target detection.

Keywords: small target detection, local contrast, human vision system, Laplacian of Gaussian

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Authors: Saeed Yazdani, Shahrbanoo Bagheri, Sepideh Nikravesh

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Considering the importance and scarcity of water resources, the efficient management of water resources is of great importance. Adoption of modern irrigation technology is considered to be a key of increasing the efficiency of water used in agriculture. Policy makers have implemented several ways to induce the adoption of new irrigation technology. The empirical studies show that farmers are reluctant to utilize the use of new irrigation methods. This study aims to assess factors affecting on farmer’s decision on the application of water saving technologies with emphasize on crop price variation and water sources. A Logit model was employed to examine the impact of different variables on use of water saving technology. The required data gathered from a sample of 204 farmers in the year 2012. The results indicate that different variables such as crop price variability, water supply source, high-value crops, farm size, income, education, membership in cooperatives have a positive effect and variables such as age and number of plots have a negative impact on the probability of adopting modern water saving technologies.

Keywords: irrigation, water, water saving technology, scarcity

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Authors: Laid Fekih

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Background: It is difficult to detect lying, deception, and misrepresentation just by looking at verbal or non-verbal expression during the criminal investigation process, as there is a common belief that it is possible to tell whether a person is lying or telling the truth just by looking at the way they act or behave. The process of detecting lies and deception during the criminal investigation process needs more studies and research to overcome the difficulties facing the investigators. Method: The present study aimed to identify the effectiveness of cognitive methods and techniques in detecting deception during the criminal investigation. It adopted the quasi-experimental method and covered a sample of (20) defendants distributed randomly into two homogeneous groups, an experimental group of (10) defendants be subject to criminal investigation by applying cognitive techniques to detect deception and a second experimental group of (10) defendants be subject to the direct investigation method. The tool that used is a guided interview based on models of investigative questions according to the cognitive deception detection approach, which consists of three techniques of Vrij: imposing the cognitive burden, encouragement to provide more information, and ask unexpected questions, and the Direct Investigation Method. Results: Results revealed a significant difference between the two groups in term of lie detection accuracy in favour of defendants be subject to criminal investigation by applying cognitive techniques, the cognitive deception detection approach produced superior total accuracy rates both with human observers and through an analysis of objective criteria. The cognitive deception detection approach produced superior accuracy results in truth detection: 71%, deception detection: 70% compared to a direct investigation method truth detection: 52%; deception detection: 49%. Conclusion: The study recommended if practitioners use a cognitive deception detection technique, they will correctly classify more individuals than when they use a direct investigation method.

Keywords: the cognitive lie detection approach, deception, criminal investigation, mental health

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Authors: Pratham Madnur, Prathamkumar Shetty, Sneha Varur, Gouri Parashetti

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In order to overcome conventional obstacles, this research paper investigates novel approaches for cricket pitch and ball detection that make use of cutting-edge technologies. The research integrates OpenCV for pitch inspection and modifies the YOLOv8 model for cricket ball detection in order to overcome the shortcomings of manual pitch assessment and traditional ball detection techniques. To ensure flexibility in a range of pitch environments, the pitch detection method leverages OpenCV’s color space transformation, contour extraction, and accurate color range defining features. Regarding ball detection, the YOLOv8 model emphasizes the preservation of minor object details to improve accuracy and is specifically trained to the unique properties of cricket balls. The methods are more reliable because of the careful preparation of the datasets, which include novel ball and pitch information. These cutting-edge methods not only improve cricket analytics but also set the stage for flexible methods in more general sports technology applications.

Keywords: OpenCV, YOLOv8, cricket, custom dataset, computer vision, sports

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Authors: Muhammad Zafarullah Khan, Sumeera Abbasi

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The Farmers Field School (FFS) aims at benefiting poor farmers by improving their knowledge of existing agricultural technologies and integrated crop management to become independent and confident in their decision. The study on effect of farmer’s field school on vegetables production before and after FFS implementation in district Peshawar in four selected villages on each crop in 2011 was conducted from 80 farmers. The results were compared by using paired t-test. It was observed that 80% of the respondents were satisfied with FFS approach as there was a significant increase in vegetable production. The seed rate of tomato and cucumber decreased from 0.185kg/kanal to 0.1 kg/ kanal and 0.120kg/kanal to 0.01kg/kanal while production of tomato and cucumber were increased from 8158.75kgs/kanal to 1030.25kgs/kanal and 3230kgs/kanal to 5340kgs/kanal, respectively after the activities of FFS. FFS brought a positive effect on vegetable production and technology adoption improving their income, skills and knowledge ultimately lead farmers towards empowerment. The input cost including seed, crop management, FYM, and weedicides for tomato were reduced by Rs.28, Rs. 3170 and Rs.658 and cucumber reduced by Rs.35, Rs.570 and Rs.430. Only fertilizers cost was increased by Rs. 2200 in case of tomato and 465 in case of cucumber. FFS facilitator and coordinator should be more skilled and practical oriented to facilitate poor farmers. In light of the above study, more FFS should be planned so that the more farmers should be benefited.

Keywords: effect, farmer field school, vegetables production, integrated crop management

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Authors: Shah Abbas

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Rheumatoid arthritis is a systemic, inflammatory autoimmune disease, affecting an overall 1% of the global population. Despite being tremendous efforts by scientists, early diagnosis of RA still has not been achieved. In the current study, a Graphene oxide (GO) based electrochemical sensor has been developed for early diagnosis of RA through Cyclic voltammetry. Chitosan (CHI), a CPnatural polymer has also been incorporated along with GO in order to enhance the biocompatibility and functionalization potential of the biosensor. CCPs are known antigens for Anti Citrullinated Peptide Antibodies (ACPAs) which can be detected in serum even 14 years before the appearance of symptoms, thus they are believed to be an ideal target for the early diagnosis of RA. This study has yielded some promising results regarding the binding and detection of ACPAs through changes in the electrochemical properties of biosensing material. The cyclic voltammogram of this biosensor reflects the binding of ACPAs to the biosensor surface, due to its shifts observed in the current flow (cathodic current) as compared to the when no ACPAs bind as it is absent in RA negative patients.

Keywords: rheumatoid arthritis, peptide sensor, graphene oxide, anti citrullinated peptide antibodies, cyclic voltammetry

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Authors: M. J. Pourvaghar, M. E. Bahram, Sh. Khoshemehry

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Hyperlipidemia or an increase in blood lipids is a condition that has been rising, especially during the last decade, with the advancement of the life-span of the car, as an important disease. In fact, it is one of the complications of industrial life and semi-industrial. Hyperlipidemia alone is not a disease, but it is recognized as an important risk factor for coronary artery disease. The methodology of this review article is the use of research to provide the best solution for physical activity and exercise in relation to lowering blood lipids and lowering blood pressure. Also, factors that contribute to improving the health status of humans should be introduced. Research findings in this article show that physical activity with a specific duration and severity can keep a person away from the cardiovascular disease. The result shows that regular physical activity with low intensity and long periods of time is essential for human health. Physical mobility reduces blood pressure, reduces the harmful fats and does not cause cardiovascular disease. More than half of the patients suffering from cardiovascular problems are afflicted with blood lipids. On the other hand, high blood pressure is one of the serious health hazards in the world today, which causes a large number of cardiovascular problems and mortality in the world. Undoubtedly, the second most common risk factor for heart disease is high blood pressure after cigarette smoking.

Keywords: blood pressure, cardiovascular, hyperlipidemia, risk factor

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Authors: Sisay Negash, Zebene Asfaw, Kibreselassie Daniel, Michael Zech

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A significant portion of the Ethiopian landscape features scattered trees that are deliberately managed in crop fields to enhance soil fertility and crop yield in which the compatibility of crops with these trees varies depending on location, tree species, and annual crop type. This study aimed to examine the effects of scattered Vachellia tortilis and Vachellia nilotica trees on selected physico-chemical properties of the soil, as well as the yield and yield components of sorghum in Ethiopia. Vachellia tortilis and Vachellia nilotica were selected on abundance occurrence and managed in crop fields. A randomized complete block design was used, with a distance from the tree canopy (middle, edge, and outside) as a treatment, and five trees of each species served as replications. Sorghum was planted up to 15 meters in the east, west, south, and north directions from the tree trunk to assess growth and yield. Soil samples were collected from the two tree species, three distance factors, three soil depths(0-20cm, 20-40cm, and 40-60cm), and five replications, totaling 45 samples for each tree species. These samples were analyzed for physical and chemical properties. The results indicated that both V. tortilis and V. nilotica significantly affected soil physico-chemical properties and sorghum yield. Specifically, soil moisture content, EC, total nitrogen, organic carbon, available phosphorus and potassium, CEC, sorghum plant height, panicle length, biomass, and yield decreased with increasing distance from the canopy. Conversely, bulk density and pH increased. Under the canopy, sorghum yield increased by 66.4% and 53.5% for V. tortilis and V. nilotica, respectively, due to higher soil moisture and nutrient availability. The study recommends promoting trees in crop fields, management options for new saplings, and further research on root decomposition and nutrient supply.

Keywords: canopy, crop yield, soil nutrient, soil organic matter, yield components

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Authors: Fadime Eroglu

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Acanthamoeba genus belongs to kingdom protozoa, and it is known as free-living amoebae. Acanthamoeba genus has been isolated from human bodies, swimming pools, bottled mineral water, contact lens solutions, dust, and soil. The members of the genus Acanthamoeba causes Acanthamoeba Keratitis which is a painful sight-threatening disease of the eyes. In recent years, the prevalence of Acanthamoeba keratitis has been high rate reported. The eight different Acanthamoeba species are known to be effective in Acanthamoeba keratitis. These species are Acanthamoeba castellanii, Acanthamoeba polyphaga, Acanthamoeba griffini, Acanthamoeba hatchetti, Acanthamoeba culbertsoni and Acanhtamoeba rhysodes. The conventional diagnosis of Acanthamoeba Keratitis has relied on cytological preparations and growth of Acanthamoeba in culture. However molecular methods such as real-time PCR has been found to be more sensitive. The real-time PCR has now emerged as an effective method for more rapid testing for the diagnosis of infectious disease in decade. Therefore, a real-time PCR assay for the detection of Acanthamoeba keratitis and Acanthamoeba species have been developed in this study. The 18S rRNA sequences from Acanthamoeba species were obtained from National Center for Biotechnology Information and sequences were aligned with MEGA 6 programme. Primers and probe were designed using Custom Primers-OligoPerfectTMDesigner (ThermoFisherScientific, Waltham, MA, USA). They were also assayed for hairpin formation and degree of primer-dimer formation with Multiple Primer Analyzer ( ThermoFisherScientific, Watham, MA, USA). The eight different ATCC Acanthamoeba species were obtained, and DNA was extracted using the Qiagen Mini DNA extraction kit (Qiagen, Hilden, Germany). The DNA of Acanthamoeba species were analyzed using newly designed primer and probe set in real-time PCR assay. The early definitive laboratory diagnosis of Acanthamoeba Keratitis and the rapid initiation of suitable therapy is necessary for clinical prognosis. The results of the study have been showed that new primer and probes could be used for detection and distinguish for Acanthamoeba species. These new developing methods are helpful for diagnosis of Acanthamoeba Keratitis.

Keywords: Acathamoeba Keratitis, Acanthamoeba species, fast diagnosis, Real-Time PCR

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Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

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Authors: Ghoul Rachid Brahim

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Introduction: Degenerative disc disease is a process of premature and accelerated deterioration of the intervertebral disc; it is of multifactorial origin and is responsible for chronic low back pain. Objectives: Determine an epidemiological profile of patients with painful lumbar degenerative disc disease. Patients and methods: We performed a prospective study of 104 patients operated on for degenerative painful lumbar disc disease over a period of 25 months. The parameters analyzed were: age, sex, Body Mass Index (BMI), comorbidities, family history of low back pain, and difficulty with professional activity. Results: The average age was 43.3 years, with a clear predominance of men: 72 men for 32 women, the average BMI was 26.80Kg / m2, and 63.5% of the patients were overweight. The occurrence of disc degeneration in pathological conditions was noted in 14.4% of cases. The notion of familial low back pain was found in 49% of cases. The majority of patients perform more or less arduous work (51%) in the cases. Conclusion: In our series, degenerative painful lumbar disc disease predominates in the male subject, active obese who performs more or less painful work, in whom we find a family history of low back pain.

Keywords: degenerative disc disease, low back pain, body mass index, disque intervertebrale

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Authors: Shoujia Fang, Guoqing Ding, Xin Chen

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The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.

Keywords: keypoint detection, curve feature, convolutional neural network, press-fit assembly

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Authors: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang

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Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.

Keywords: CCS concepts, computing methodologies, interest point, salient region detections, image segmentation

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Authors: Vaclav Novak, Katerina Krizova, Petr Sarec

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Modern agriculture has to face many issues from which soil degradation and lack of organic matter in the soil are only a few of them. Apart from Climate Change, human utilization of landscape is the cause of a majority part of these problems. Cattle production in Czechia has been reduced by more than half in recent 30 years. However, cattle manure is considered as staple organic fertilizer, and its role in attempts for sustainable agriculture is irreplaceable. This study aims to describe the impact of so-called activators of biological manure transformation (Z´fix, Olmix Group) mainly on physical soil properties but also on crop status. The experiment has been established in 2017; nevertheless, initial measurements of implement draft have been performed before the treated manure application. In 2018, the physical soil properties and crop status (sugar beet) has been determined and compared with the untreated manure and control variant. Significant results have been observed already in the first year, where the implement draft decreased by 9.2 % within the treated manure variant in comparison with the control variant.

Keywords: field experiment, implement draft, vegetation index, sugar beet

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Authors: C. Gallottini, W. Di Mari, A. Amaddeo, K. Barbaro, A. Dolci, G. Dolci, L. Gallottini, G. Barraco, S. Eramo

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A group of 10 dogs (group A) with Periodontal Disease in the third stage, were subjected to regenerative therapy of periodontal tissues, by use of nano hydroxy apatite (NHA). These animals induced by general anesthesia, where treated by ultrasonic scaling, root planning, and at the end by a mucogingival flap in which it was applied NHA. The flap was closed and sutured with simple steps. Another group of 10 dogs (group B), control group, was treated only by scaling and root planning. No patient was subjected to antibiotic therapy. After three months, a check was made by inspection of the oral cavity, radiography and bone biopsy at the alveolar level. Group A showed a total restitutio ad integrum of the periodontal structures, and in group B still mild gingivitis in 70% of cases and 30% of the state remains unchanged. Numerous experimental studies both in animals and humans have documented that the grafts of porous hydroxyapatite are rapidly invaded by fibrovascular tissue which is subsequently converted into mature lamellar bone tissue by activating osteoblast. Since we acted on the removal of necrotic cementum and rehabilitating the root tissue by polishing without intervention in the ligament but only on anatomical functional interface of cement-blasts, we can connect the positive evolution of the clinical-only component of the cement that could represent this perspective, the only reason that Periodontal Disease become a Cement Disease, while all other clinical elements as nothing more than a clinical pathological accompanying.

Keywords: nanoidroxiaphatite, parodontal disease, cement disease, regenerative therapy

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Authors: Vhahangwele Belemu, Isaac Busayo Oluwatayo

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Post-harvest losses pose a serious challenge to smallholder fruit crop farmers, especially in the rural communities of South Africa, affecting their economic livelihoods and food security. This study investigated the post-harvest handling practices and technologies harnessed by smallholder fruit crop farmers in the Vhembe district of Limpopo province, South Africa. Data were collected on a random sample of 224 smallholder fruit crop farmers selected from the four municipalities of the district using a multistage sampling technique. Analytical tools employed include descriptive statistics and the tobit regression model. A descriptive analysis of farmers’ socioeconomic characteristics showed that a sizeable number of these farmers are still in their active working age (mean = 52 years) with more males (63.8%) than their female (36.2%) counterparts. Respondents’ distribution by educational status revealed that only a few of these had no formal education (2.2%), with the majority having secondary education (48.7%). Results of data analysis further revealed that the prominent post-harvest technologies and handling practices harnessed by these farmers include using appropriate harvesting techniques (20.5%), selling at a reduced price (19.6%), transportation consideration (18.3%), cleaning and disinfecting (17.9%), sorting and grading (16.5%), manual cleaning (15.6%) and packaging technique (11.6%) among others. The result of the Tobit regression analysis conducted to examine the determinants of post-harvest technologies and handling practices harnessed showed that age, educational status of respondents, awareness of technology/handling practices, farm size, access to credit, extension contact, and membership of association were the significant factors. The study suggests enhanced awareness creation, access to credit facility and improved access to market as important factors to consider by relevant stakeholders to assist smallholder fruit crop farmers in the study area.

Keywords: fruit crop farmers, handling practices, post harvest losses, smallholder, Vhembe District, South Africa

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Authors: A. Harras, A. Tsuji, K. Terada

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Tracking of moving people has gained a matter of great importance due to rapid technological advancements in the field of computer vision. The objective of this study is to design a motion based detection and tracking multiple walking pedestrians randomly in different directions. In our proposed method, Gaussian mixture model (GMM) is used to determine moving persons in image sequences. It reacts to changes that take place in the scene like different illumination; moving objects start and stop often, etc. Background noise in the scene is eliminated through applying morphological operations and the motions of tracked people which is determined by using the Kalman filter. The Kalman filter is applied to predict the tracked location in each frame and to determine the likelihood of each detection. We used a benchmark data set for the evaluation based on a side wall stationary camera. The actual scenes from the data set are taken on a street including up to eight people in front of the camera in different two scenes, the duration is 53 and 35 seconds, respectively. In the case of walking pedestrians in close proximity, the proposed method has achieved the detection ratio of 87%, and the tracking ratio is 77 % successfully. When they are deferred from each other, the detection ratio is increased to 90% and the tracking ratio is also increased to 79%.

Keywords: automatic detection, tracking, pedestrians, counting

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Authors: Gigih Priyandoko, Mohd Fairusham Ghazali, Tan Siew Fun

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This paper discusses about the defect detection of plastic pipe by using nonlinear acoustic wave modulation method. It is a sensitive method for damage detection and it is based on the propagation of high frequency acoustic waves in plastic pipe with low frequency excitation. The plastic pipe is excited simultaneously with a slow amplitude modulated vibration pumping wave and a constant amplitude probing wave. The frequency of both the excitation signals coincides with the resonances of the plastic pipe. A PVP pipe is used as the specimen as it is commonly used for the conveyance of liquid in many fields. The results obtained are being observed and the difference between uncracked specimen and cracked specimen can be distinguished clearly.

Keywords: plastic pipe, defect detection, nonlinear acoustic modulation, excitation

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Authors: P. Sindhumole, K. Soumya, R. Renjimol

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Rice (Oryza sativa L.) is the major staple food crop over the world. It is prone to a number of biotic and abiotic stresses, out of which Bacterial Leaf Blight (BLB), caused by Xanthomonas oryzae pv. oryzae, is the most rampant. Management of this disease through chemicals or any other means is very difficult. The best way to control BLB is by the development of Host Plant Resistance. BLB resistance is not an activity of a single gene but it involves a cluster of more than thirty genes reported. Among these, Xa5 and Xa13 genes are two important ones, which can be diagnosed through marker assisted selection using closely linked molecular markers. During 2014, the first phase of field screening using forty traditional rice genotypes was carried out and twenty resistant symptomless genotypes were identified. Molecular characterisation of these genotypes using RM 122 SSR marker revealed the presence of Xa5 gene in thirteen genotypes. Forty-two traditional rice genotypes were used for the second phase of field screening for BLB resistance. Among these, sixteen resistant genotypes were identified. These genotypes, along with two susceptible check genotypes, were subjected to marker assisted selection for Xa13 gene, using the linked STS marker RG-136. During this process, presence of Xa13 gene could be detected in ten resistant genotypes. In future, these selected genotypes can be directly utilised as donors in Marker assisted breeding programmes for BLB resistance in rice.

Keywords: oryza sativa, SSR, STS, marker, disease, breeding

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Authors: Mrinal Kanti Bhowmik, Kakali Das Jr., Barin Kumar De, Debotosh Bhattacharjee

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Breast cancer is the most common cancer and a leading cause of death for women in the 35 to 55 age group. Early detection of breast cancer can decrease the mortality rate of breast cancer. Mammography is considered as a ‘Gold Standard’ for breast cancer detection and a very popular modality, presently used for breast cancer screening and detection. The screening of digital mammograms often leads to over diagnosis and a consequence to unnecessary traumatic & painful biopsies. For that reason recent studies involving the use of thermal imaging as a screening technique have generated a growing interest especially in cases where the mammography is limited, as in young patients who have dense breast tissue. Tumor is a significant sign of breast cancer in both mammography and thermography. The tumors are complex in structure and they also exhibit a different statistical and textural features compared to the breast background tissue. Fractal geometry is a geometry which is used to describe this type of complex structure as per their main characteristic, where traditional Euclidean geometry fails. Over the last few years, fractal geometrics have been applied mostly in many medical image (1D, 2D, or 3D) analysis applications. In breast cancer detection using digital mammogram images, also it plays a significant role. Fractal is also used in thermography for early detection of the masses using the thermal texture. This paper presents an overview of the recent aspects and initiatives of fractals in breast cancer detection in both mammography and thermography. The scope of fractal geometry in automatic breast cancer detection using digital mammogram and thermogram images are analysed, which forms a foundation for further study on application of fractal geometry in medical imaging for improving the efficiency of automatic detection.

Keywords: fractal, tumor, thermography, mammography

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Authors: Mohammadali Abedini Sanigy, Jiangang Fei

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In this paper, a mathematical optimization model was developed to maximize the profit in a vegetable production planning problem. It serves as a decision support system that assists farmers in land allocation to crops and harvest scheduling decisions. The developed model can handle different rotation rules in two consecutive cycles of production, which is a common practice in organic production system. Moreover, different production methods of the same crop were considered in the model formulation. The main strength of the model is that it is not restricted to predetermined production periods, which makes the planning more flexible. The model is classified as a mixed integer programming (MIP) model and formulated in PYOMO -a Python package to formulate optimization models- and solved via Gurobi and CPLEX optimizer packages. The model was tested with secondary data from 'Australian vegetable growing farms', and the results were obtained and discussed with the computational test runs. The results show that the model can successfully provide reliable solutions for real size problems.

Keywords: crop rotation, harvesting, mathematical model formulation, vegetable production

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Marilyn S. Painagan, Willie Jones B. Saliling

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This paper reports an optimized cropping calendar and land suitability for maize in North Cotabato derived from modeling crop productivity over time and space. Using Quantum GIS, eight representative soil types and 0.3o x 0.3o climate grids shapefiles were intersected to form thirty two pedoclimatic zones within the boundaries of the province. Surveys were done to ascertain crop performance and phenological properties on field. Based on these surveys, crop parameters were calibrated specific for a variety of maize. Soil properties and climatic data (daily precipitation, maximum and minimum temperatures) from pedoclimatic zones were loaded to the FAO Aquacrop Water Productivity Model along with the crop properties from field surveys to simulate yield from 1980 to 2010. The average yield per month was computed to come up with the month of planting having the highest and lowest probable yield in a year assuming that all lands were planted with maize. The yield attributes were visualized in the Quantum GIS environment. The study revealed that optimal cropping patterns varied across North Cotabato. Highest probable yield (8000 kg/ha) can be obtained when maize is planted on May and September (sandy clay-loam soils) in the northern part of the province while the lowest probable yield (1000 kg/ha) can be obtained when maize is planted on January, February and March (clay loam soils) at the northern part of the province. Yields are simulated on the basis of varieties currently planted by farmers of North Cotabato. The resulting maps suggest where and when maize is most suitable to achieve high yields. There is a need to ground truth and validate the cropping calendar on field.

Keywords: aquacrop, quantum GIS, maize, cropping calendar, water productivity

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Authors: Shao-Hsuan Chien, Ju-Hui Fu

Abstract:

Parkinson’s disease (PD) is a degenerative disorder of the central nervous system that is characterized by progressive loss of dopaminergic neurons in the substantia nigra pars compacta and motor impairment. Aggregation of α-synuclein in neuronal cells plays a key role in this disease. At present, therapeutics for PD provides moderate symptomatic benefit but is not able to delay the development of this disease. Current efforts for the treatment of PD are to identify new drugs that show slow or arrest progressive course of PD by interfering with a disease-specific pathogenetic process in PD patients. Maackiain is a bioactive compound isolated from the roots of the Chinese herb Sophora flavescens. The purpose of the present study was to assess the potential for maackiain to ameliorate PD in Caenorhabditis elegans models. Our data reveal that maackiain prevents α-synuclein accumulation in the transgenic Caenorhabditis elegans model and also improves dopaminergic neuron degeneration, food-sensing behavior, and life-span in 6-hydroxydopamine-induced Caenorhabditis elegans model, thus indicating its potential as a candidate antiparkinsonian drug.

Keywords: maackiain, Parkinson’s disease, dopaminergic neurons, α-Synuclein

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Authors: Muhammad SAFDAR, Mehmet Ozaslan, Musarrat Abbas Khan

Abstract:

Babesiosis is a haemoparasitic disease due to the multiplication of protozoan’s parasite, Babesia ovis in the red blood cells of the host, and contributes numerous economical losses, including sheep and goat ruminants. The early identification and successful treatment of Babesia Ovis spp. belong to the key steps of control and health management of livestock resources. The objective of this study was to construct a polymerase chain reaction (PCR) based method for the detection of Babesia spp. in small ruminants and to determine the risk factors involved in the spreading of babesiosis infections. A total of 100 blood samples were collected from 50 sheep and 50 goats along with different areas of Muzaffargarh, Pakistan, from randomly selected herds. Data on the characteristics of sheep and goats were collected through questionnaires. Of 100 blood samples examined, 18 were positive for Babesia ovis upon microscopic studies, whereas 11 were positive for the presence of Babesia spp. by PCR assay. For the recognition of parasitic DNA, a set of 500bp oligonucleotide was designed by PCR amplification with sequence 18S rRNA gene for B. ovis. The prevalence of babesiosis in small ruminant’s sheep and goat detected by PCR was significantly higher in female animals (28%) than male herds (08%). PCR analysis of the reference samples showed that the detection limit of the PCR assay was 0.01%. Taken together, all data indicated that this PCR assay was a simple, fast, specific detection method for Babesia ovis species in small ruminants compared to other available methods.

Keywords: Babesia ovis, PCR amplification, 18S rRNA, sheep and goat

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Authors: Isao Tomita

Abstract:

The detection of environmental gases, 12CO_2, 13CO_2, and CH_4, using near-infrared semiconductor lasers with a short laser path length is studied by means of wavelength-modulation spectroscopy. The developed system is compact and has high sensitivity enough to detect the absorption peaks of isotopic 13CO_2 of a 3-% CO_2 gas at 2 um with a path length of 2.4 m, where its peak size is two orders of magnitude smaller than that of the ordinary 12CO_2 peaks. In addition, the detection of 12CO_2 peaks of a 385-ppm (0.0385-%) CO_2 gas in the air is made at 2 um with a path length of 1.4 m. Furthermore, in pursuing the detection of an ancient environmental CH_4 gas confined to a bubble in ice at the polar regions, measurements of the absorption spectrum for a trace gas of CH_4 in a small area are attempted. For a 100-% CH_4 gas trapped in a 1 mm^3 glass container, the absorption peaks of CH_4 are obtained at 1.65 um with a path length of 3 mm, and also the gas pressure is extrapolated from the measured data.

Keywords: environmental gases, Near-Infrared Laser Detection, Wavelength-Modulation Spectroscopy, gas pressure

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Authors: Tim Farrelly

Abstract:

In recent years, AI-powered object detection systems have opened the doors for innovative new applications and products, especially those operating in the real world or ‘on edge’ – namely, in sport. This paper investigates the viability of an ultra-low parameter convolutional neural network specially designed for the detection of footballs on ‘on the edge’ devices. The main contribution of this paper is the exploration of integrating new design features (depth-wise separable convolutional blocks and squeezed and excitation modules) into an ultra-low parameter network and demonstrating subsequent improvements in performance. The results show that tracking the ball from Full HD images with negligibly high accu-racy is possible in real-time.

Keywords: deep learning, object detection, machine vision applications, sport, network design

Procedia PDF Downloads 141

Authors: Onder Yakut, Oguzhan Timus, Emine Dogru Bolat

Abstract:

Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.

Keywords: arrhythmic beat detection, ECG, HRV, kNN classifier

Procedia PDF Downloads 350