Search results for: forest farming

Authors: Nigar Hussain

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It is a subset of big data. Dark data means those data in which we fail to use for future decisions. There are many issues in existing work, but some need powerful tools for utilizing dark data. It needs sufficient techniques to deal with dark data. That enables users to exploit their excellence, adaptability, speed, less time utilization, execution, and accessibility. Another issue is the way to utilize dark data to extract helpful information to settle on better choices. In this paper, we proposed upgrade strategies to remove the dark side from dark data. Using a supervised model and machine learning techniques, we utilized dark data and achieved an F1 score of 89.48%.

Keywords: big data, dark data, machine learning, heatmap, random forest

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Authors: William Sigvardsson, Christoffer Alenius, Jenny Lindblom, Andreas Johansson, Marcus Sandberg

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This article covers a study focusing on a subarctic greenhouse located in Nikkala, Sweden. Through a visit and the creation of a CFD model, the study investigates the differences in energy demand with high pressure sodium (HPS) lights and light emitting diode (LED) lights in combination with an air-carried and water-carried heating system accordingly. Through an IDA ICE model, the impact of insulating the parts of the greenhouse without active cultivation was also investigated. This, with the purpose of comparing the current system in the greenhouse to state-of-the-art alternatives and evaluating if an investment in either a water-carried heating system in combination with LED lights and insulating the non-cultivating parts of the greenhouse could be considered profitable. Operating a greenhouse in the harsh subarctic climate found in the northern parts of Sweden is not an easy task and especially if the operation is year-round. With an average temperature of under -5 °C from November through January, efficient growing techniques are a must to ensure a profitable business. Today the most crucial parts of a greenhouse are the heating system, lighting system, dehumidifying measures, as well as thermal screen, and the impact of a poorly designed system in a sub-arctic could be devastating as the margins are slim. The greenhouse studied uses a pellet burner to power their air- carried heating system which is used. The simulations found the resulting savings amounted to just under 14 800 SEK monthly or 18 % of the total cost of energy by implementing the water-carrying heating system in combination with the LED lamps. Given this, a payback period of 3-9 years could be expected given different scenarios, including specific time periods, financial aids, and the resale price of the current system. The insulation of the non-cultivating parts of the greenhouse was found to have possible savings of 25 300 SEK annually or 46 % of the current heat demand resulting in a payback period of just over 1-2 years. Given the possible energy savings, a reduction in emitted CO2 equivalents of almost 1,9 tonnes could be achieved annually. It was concluded that relatively inexpensive investments in modern greenhouse equipment could make a significant contribution to reducing the energy consumption of the greenhouse resulting in a more competitive business environment for sub-arctic greenhouse owners. New parts of the greenhouse should be built with the water-carried heating system in combination with state-of-the-art LED lights, and all parts which are not housing active cultivation should be insulated. If the greenhouse in Nikkala is eligible for financial aid or finds a resale value in the current system, an investment should be made in a new water-carried heating system in combination with LED lights.

Keywords: energy efficiency, sub-arctic greenhouses, energy measures, greenhouse climate control, greenhouse technology, CFD

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Authors: Sara Bouzekri, Said Madani

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In many Mediterranean cities such as Algiers, the human activity, the strong mobility the urban sprawl, the air pollution, the problems of waste management, the wasting of the resources and the degradation of the environment weaken in an unquestionable way the farming. The question of sustainable action vis-a-vis these threats arises then in order to maintain a level of desired local development. The methodology is based on a multi-criteria method based on the AFOM diagnosis, which classifies agricultural strength indicators and those of threat, according to an analytical approach. In a sustainable development perspective, it will be appropriate to link the threat factors of the case study with the factors of climate change to see their impact on the future of agriculture. This will be accompanied by a SWOT analysis, which crosses the most significant criteria to arrive at the necessary recommendations based on future projects for urban agriculture.

Keywords: Algiers, environment, urban agriculture, threat factors

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Authors: Jaber Jafarzadeh

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The two nodes of treatment for the study of indirect effects on the reproductive performance of sheep farming machines used. During the harvest period of 30 days (from 20th July to 20th September) and coincides with the period, sheep are also harvested the following day why the fields and in the second group were 30 ewes and were kept in farms that harvest was done by machinery during harvest about 15-20 days (from 20th July to early September), respectively. -Ya Term mating season is better than the ram up Astafadh Knym- of early September, no matter the point of beginning. Based on the data obtained, it was found that the rate of return to oestrus in the first group is lower than the second group and the rate of lambing in the first group was significantly (0.05> P) is greater than the second group (138% vs. 97%). Estrus synchronization in the first group and the second group was better than that.

Keywords: mechanized harvest, twin birth, mating season, reproductive performance of ewes

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Authors: Marilyn S. Painagan

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This general objective of the study was to apply the AquaCrop model to the conditions in the municipality of Carmen, North Cotabato in terms of predicting corn yields in this area and determine the influence of rainfall and soil depth on simulated yield. The study revealed wide disparity in monthly yields as a consequence of similarly varying monthly rainfall magnitudes. It also found out that simulated yield varies with the depth of soil, which in this case was clay loam, the predominant soil in the study area. The model was found to be easy to use even with limited data and shows a vast potential for various farming and policy applications, such as formulation of a cropping calendar.

Keywords: aquacrop, evapotranspiration, crop modelling, crop simulation

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Authors: Firat Arslan, Hasan Degirmenci, Serife Tulin Akkaya Aslan

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The aim of the current experiment was to determine the relationship among shape indexes which are used by the researchers in many fields to evaluate parcel shapes which is very important for farming even if these indexes are controversial. In the current study, land consolidation project of Halitaga village in Mersin province in Turkey which has 278 parcel and cover 894.4 ha, was taken as a material. Commonly used indicators such as fractal dimension (FD), shape index (SI), form factor (FORM), areal form factor (AFF) and two distinct area-perimeter ratio (APR-1 and APR2) in land consolidation are used to measure agricultural plot’s shape. FD was positively correlated with SI, APR-1 and APR-2 whereas it was negatively correlated with FORM and AFF. SI was positively correlated with APR-1 and APR-2 whereas it was negatively correlated with FORM and AFF. As a conclusion, it is likely that these indexes involved may be used interchangeably due to high correlations among them.

Keywords: GIS, land consolidation, parcel shape, shape index

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Hritik Bhattarai

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Land use and land cover play a crucial role in preserving and managing Earth's natural resources. Various factors, such as economic, demographic, social, cultural, technological, and environmental processes, contribute to changes in land use and land cover (LULC). Rupandehi District is significantly influenced by a combination of driving forces, including its geographical location, rapid population growth, economic opportunities, globalization, tourism activities, and political events. Urbanization and urban growth in the region have been occurring in an unplanned manner, with internal migration and natural population growth being the primary contributors. Internal migration, particularly from neighboring districts in the higher and lower Himalayan regions, has been high, leading to increased population growth and density. This study utilizes geospatial technology, specifically geographic information system (GIS), to analyze and illustrate the land cover and land use changes in the Rupandehi district for the years 2009 and 2019, using freely available Landsat images. The identified land cover categories include built-up area, cropland, Das-Gaja, forest, grassland, other woodland, riverbed, and water. The statistical analysis of the data over the 10-year period (2009-2019) reveals significant percentage changes in LULC. Notably, Das-Gaja shows a minimal change of 99.9%, while water and forest exhibit increases of 34.5% and 98.6%, respectively. Riverbed and built-up areas experience changes of 95.3% and 39.6%, respectively. Cropland and grassland, however, show concerning decreases of 102.6% and 140.0%, respectively. Other woodland also indicates a change of 50.6%. The most noteworthy trends are the substantial increase in water areas and built-up areas, leading to the degradation of agricultural and open spaces. This emphasizes the urgent need for effective urban planning activities to ensure the development of a sustainable city. While Das-Gaja seems unaffected, the decreasing trends in cropland and grassland, accompanied by the increasing built-up areas, are unsatisfactory. It is imperative for relevant authorities to be aware of these trends and implement proactive measures for sustainable urban development.

Keywords: land use and land cover, geospatial, urbanization, geographic information system, sustainable urban development

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Authors: Rajendra Kumar Isaac, Monisha Isaac

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India, having a vast cultivable area and regional climatic variability, encounters water Resource Management Problems at various levels. The agricultural production of India needs to be increased to meet out projected population growth. Sustainable water resource is the only option to ensure food security, especially in northern Indian states, where the ground and surface water resources are fast depleting. Various tools and technologies available for management of scarce water resources have been discussed. It was concluded that multiple use of water, adopting latest water management options, identification of climate adoptable cropping and farming systems, can enhance water productivity and would encounter the fast growing water management and water shortage problems in Indian agriculture.

Keywords: water resource management, sustainable, water management technologies, water productivity, agriculture

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Authors: Noor-ul-Ain, Muhammad Thair Khan, Adeela Ajmal, Hamid Mustafa

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Pakistan is an agricultural country and livestock only share 11.8 percent to national GDP during 2015-16. Pakistan is a 3rd largest milk producing country having 41.2, 35.6, 29.4, 68.4 and 1.0 million head cattle, buffalo, sheep, goat and camel, respectively. Modern urbanization and shortage of feed resources for livestock species in a country is an alarming threat. The introduction of new technology and advanced techniques solve this issue. This includes drought feeding, increase production, aid to crop management, balance nutrition and easily storaged of wet feed products. It is therefore clear that silage has important role in animal feed and feeding. Financial model of this study clear the effectiveness of silage. Therefore, it is revealed from this study that silage is a cost-effective option for a profitable dairy farming in Pakistan.

Keywords: feed, silage, dairy, production, Pakistan

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Authors: Sarah Okende

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It is universally recognized that the forests of the Congo Basin remain a common good and a complex ecosystem, and insufficiently known. Historically and throughout the world, forests have been valued for the multiple products and benefits they provide. In addition to their major role in the conservation of global biodiversity and in the fight against climate change, these forests also have an essential role in the regional and global ecology. This is particularly the case of the Luki Biosphere Reserve, a highly diversified evergreen Guinean-Congolese rainforest. Despite the efforts of sustainable management of the said reserve, the understanding of the place occupied by the soil under the influence of the latter does not seem to be an interesting subject for the general public or even scientists. The Luki biosphere reserve is located in the west of the DRC, more precisely in the south-east of Mayombe Congolais, in the province of Bas-Congo. The vegetation of the Luki Biosphere Reserve is very heterogeneous and diversified. It ranges from grassy formations to semi-evergreen dense humid forests, passing through edaphic formations on hydromorphic soils (aquatic and semi-aquatic vegetation; messicole and segetal vegetation; gascaricole vegetation; young secondary forests with Musanga cercropioides, Xylopia aethiopica, Corynanthe paniculata; mature secondary forests with Terminalia superba and Hymenostegia floribunda; primary forest with Prioria balsamifera; climax forests with Gilbertiodendron dewevrei, and Gilletiodendron kisantuense). Field observations and reading of previous and up-to-date work carried out in the Luki biosphere reserve are the methodological approaches for this study, the aim of which is to show the impact of soil types in determining the varieties of vegetation. The results obtained prove that the four different types of soil present (purplish red soils, developing on amphibolites; red soils, developed on gneisses; yellow soils occurring on gneisses and quartzites; and alluvial soils, developed on recent alluvium) have a major influence apart from other environmental factors on the determination of different facies of the vegetation of the Luki Biosphere Reserve. In conclusion, the Luki Biosphere Reserve is characterized by a wide variety of biotopes determined by the nature of the soil, the relief, the microclimates, the action of man, or the hydrography. Overall management (soil, biodiversity) in the Luki Biosphere Reserve is important for maintaining the ecological balance.

Keywords: soil, biodiversity, forest, Luki, rainforest

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Authors: Jeremy Johnson, Craig Nitschke, Luke Kelly

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Fire regimes, climate and topographic gradients interact to influence ecosystem structure and function across fire-prone, montane landscapes worldwide. Biota have developed a range of adaptations to historic fire regime thresholds, which allow them to persist in these environments. In south-eastern Australia, a signal of fire regime changes is emerging across these landscapes, and anthropogenic climate change is likely to be one of the main drivers of an increase in burnt area and more frequent wildfire over the last 25 years. This shift has the potential to modify vegetation structure and composition at broad scales, which may lead to landscape patterns to which biota are not adapted, increasing the likelihood of local extirpation of some mammal species. This study aimed to address concerns related to the influence of abrupt changes in fire regimes on mammal populations in montane landscapes. It first examined the impact of climate, topography, and vegetation on fire patterns and then explored the consequences of these changes on mammal populations and their habitats. Field studies were undertaken across diverse vegetation, fire severity and fire frequency gradients, utilising camera trapping and passive acoustic monitoring methodologies and the collection of fine-scale vegetation data. Results show that drought is a primary contributor to fire regime shifts at the landscape scale, while topographic factors have a variable influence on wildfire occurrence at finer scales. Frequent, high severity wildfire influenced forest structure and composition at broad spatial scales, and at fine scales, it reduced occurrence of hollow-bearing trees and promoted coarse woody debris. Mammals responded differently to shifts in forest structure and composition depending on their habitat requirements. This study highlights the complex interplay between fire regimes, environmental gradients, and biotic adaptations across temporal and spatial scales. It emphasizes the importance of understanding complex interactions to effectively manage fire-prone ecosystems in the face of climate change.

Keywords: fire, ecology, biodiversity, landscape ecology

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Authors: Newton Muhury, Armando A. Apan, Tek Maraseni

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This study modelled the relationships between vegetation response and available water below the soil surface using the Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS) generated Normalised Difference Vegetation Index (NDVI) and soil water content (SWC) data. The Soil & Water Assessment Tool (SWAT) interface known as ArcSWAT was used in ArcGIS for the groundwater analysis. The SWAT model was calibrated and validated in SWAT-CUP software using 10 years (2001-2010) of monthly streamflow data. The average Nash-Sutcliffe Efficiency during the calibration and validation was 0.54 and 0.51, respectively, indicating that the model performances were good. Twenty years (2001-2020) of monthly MODIS NDVI data for three different types of vegetation (forest, shrub, and grass) and soil water content for 43 sub-basins were analysed using the WEKA, machine learning tool with a selection of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF). The modelling results show that different types of vegetation response and soil water content vary in the dry and wet season. For example, the model generated high positive relationships (r=0.76, 0.73, and 0.81) between the measured and predicted NDVI values of all vegetation in the study area against the groundwater flow (GW), soil water content (SWC), and the combination of these two variables, respectively, during the dry season. However, these relationships were reduced by 36.8% (r=0.48) and 13.6% (r=0.63) against GW and SWC, respectively, in the wet season. On the other hand, the model predicted a moderate positive relationship (r=0.63) between shrub vegetation type and soil water content during the dry season, which was reduced by 31.7% (r=0.43) during the wet season. Our models also predicted that vegetation in the top location (upper part) of the sub-basin is highly responsive to GW and SWC (r=0.78, and 0.70) during the dry season. The results of this study indicate the study region is suitable for seasonal crop production in dry season. Moreover, the results predicted that the growth of vegetation in the top-point location is highly dependent on groundwater flow in both dry and wet seasons, and any instability or long-term drought can negatively affect these floodplain vegetation communities. This study has enriched our knowledge of vegetation responses to groundwater in each season, which will facilitate better floodplain vegetation management.

Keywords: ArcSWAT, machine learning, floodplain vegetation, MODIS NDVI, groundwater

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Authors: Gulmira Zhakupova, Tamara Tultabayeva, Aknur Muldasheva, Assem Sagandyk

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The development of technology and production of products with increased biological value based on the use of natural food raw materials are important tasks in the policy of the food market of the Republic of Kazakhstan. For Kazakhstan, livestock farming, in particular sheep farming, is the most ancient and developed industry and way of life. The history of the Kazakh people is largely connected with this type of agricultural production, with established traditions using dairy products from sheep's milk. Therefore, the development of new technologies from sheep’s milk remains relevant. In addition, one of the most promising areas for the development of food technology for therapeutic and prophylactic purposes is sheep milk products as a source of protein, immunoglobulins, minerals, vitamins, and other biologically active compounds. This article presents the results of research on the study of milk processing technology. The objective of the study is to study the possibilities of processing sheep milk and its role in human nutrition, as well as the results of research to improve the technology of sheep milk products. The studies were carried out on the basis of sanitary and hygienic requirements for dairy products in accordance with the following test methods. To perform microbiological analysis, we used the method for identifying Salmonella bacteria (Horizontal method for identifying, counting, and serotyping Salmonella) in a certain mass or volume of product. Nutritional value is a complex of properties of food products that meet human physiological needs for energy and basic nutrients. The protein mass fraction was determined by the Kjeldahl method. This method is based on the mineralization of a milk sample with concentrated sulfuric acid in the presence of an oxidizing agent, an inert salt - potassium sulfate, and a catalyst - copper sulfate. In this case, the amino groups of the protein are converted into ammonium sulfate dissolved in sulfuric acid. The vitamin composition was determined by HPLC. To determine the content of mineral substances in the studied samples, the method of atomic absorption spectrophotometry was used. The study identified the technological parameters of sheep milk products and determined the prospects for researching sheep milk products. Microbiological studies were used to determine the safety of the study product. According to the results of the microbiological analysis, no deviations from the norm were identified. This means high safety of the products under study. In terms of nutritional value, the resulting products are high in protein. Data on the positive content of amino acids were also obtained. The results obtained will be used in the food industry and will serve as recommendations for manufacturers.

Keywords: dairy, milk processing, nutrition, colostrum

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Authors: M. S. Sarkar, J. A. Johnson, S. Sen, G. K. Saha, K. Ramesh

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There is growing recognition of several important roles played by tigers for maintaining sustainable biodiversity at diverse ecosystems in South and South-East Asia. Only <3200 individuals are left in the wild because of poaching and habitat loss. Thus, restoring wild population is an emerging as well as important conservation initiative, but such efforts still remain challenging due to their elusive and solitary behavior. After careful translocation of few individuals, how reintroduced individuals colonize into suitable habitat and achieve stable stage population through reproduction is vital information for forest managers and policy makers of its 13 distribution range countries. Four wild and two captive radio collared tigers were reintroduced at Panna Tiger Reserve, Madhya-pradesh, India during 2009-2014. We critically examined their settlement behavior and population growth over the period. Results from long term telemetry data showed that male explored larger areas rapidly in short time span, while females explored small area in long time period and with significant high rate of movement in both sexes during exploratory period. Significant difference in home range sizes of tigers were observed in exploratory and settlement period. Though all reintroduced tigers preferred densely vegetated undisturbed forest patches within the core area of tiger reserve, a niche based k select analysis showed that individual variation in habitat selection was prominent among reintroduced tigers. Total 18 litter of >42 known cubs were born with low mortality rate, high maternity rate, high observed growth rate and short generation time in both the sexes. The population achieved its carrying capacity in a very short time span, marking success of this current tiger conservation programme. Our study information could provide significant insights on the tiger biology of translocated tigers with implication for future conservation strategies that consider translocation based recovery in their range countries.

Keywords: reintroduction, tiger, home range, demography

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Levy flight, distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: Egboja Simon Oga

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One of the most important keys to the success of a nation is to ensure steady development and national economic self-sufficiency and independence. It is therefore in this regard that this paper is designed to identify food security to be crucial to all nations’ effort toward sustainable national development. Nigeria as a case study employed various effort by the successive government towards food security. Emphasis were placed on the extent to which government has boosted food security situation on the basis of the identified limitations, conclusion was drawn, recommendation/suggestions proffered, that subsidization of the process of farm inputs like fertilizer, improved seeds and agrochemical, education of farmers on modern methods of farming through extension services, improvisation of village-based food storage mechanism and provision of infrastructural facilities in rural areas to facilitate the preservation and easy evacuation of farm produces are necessary.

Keywords: food, governance, development, security

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Authors: C. Angel, S. Asha

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Despite the advances made in various new technologies, application of these technologies for agriculture still remains a formidable task, as it involves integration of diverse domains for monitoring the different process involved in agricultural management. Advances in ambient intelligence technology represents one of the most powerful technology for increasing the yield of agricultural crops and to mitigate the impact of water scarcity, climatic change and methods for managing pests, weeds, and diseases. This paper proposes a GPS-assisted, machine to machine solutions that combine information collected by multiple sensors for the automated management of paddy crops. To maintain the economic viability of paddy cultivation, the various techniques used in agriculture are discussed and a novel system which uses ambient intelligence technique is proposed in this paper. The ambient intelligence based agricultural system gives a great scope.

Keywords: ambient intelligence, agricultural technology, smart agriculture, precise farming

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Authors: Kelum N. Manamendra-Arachchi, Kalangi Rodrigo

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Bones, teeth, and shells have been acknowledged over the last two centuries as evidence of chronology, Palaeo-environment, and human activity. Faunal traces are valid evidence of past situations because they have properties that have not changed over long periods of time. Sri Lanka has been known as an Island, which has a diverse variation of prehistoric occupation among ecological zones. Defining the Paleoecology of the past societies has been an archaeological thought developed in the 1960s. It is mainly concerned with the reconstruction from available geological and biological evidence of past biota, populations, communities, landscapes, environments, and ecosystems. Sri Lanka has dealt with this subject and considerable research has been already undertaken. The fossil and material record of Sri Lanka’s Wet Zone tropical forests continues from c. 38,000–34,000 ybp. This early and persistent human fossil, technical, and cultural florescence, as well as a collection of well-preserved tropical-forest rock shelters with associated ' on-site ' Palaeoenvironmental records, makes Sri Lanka a central and unusual case study to determine the extent and strength of early human tropical forest encounters. Excavations carried out in prehistoric caves in the low country wet zone has shown that in the last 50,000 years, the temperature in the lowland rainforests has not exceeded 5 degrees. Based on Semnopithecus Priam (Gray Langur) remains unearned from wet zone prehistoric caves, it has been argued that periods of momentous climate changes during the LGM and Terminal Pleistocene/Early Holocene boundary, with a recognizable preference for semi-open ‘Intermediate’ rainforest or edges. Continuous Genus Acavus and Oligospira occupation along with uninterrupted horizontal pervasive of Canarium sp. (‘kekuna’ nut) have proven that temperatures in the lowland rain forests have not changed by at least 5 oC over the last 50,000 years. Site Catchment or Territorial analysis cannot be no longer defensible, due to time-distance based factors as well as optimal foraging theory failed as a consequences of prehistoric people were aware of the decrease in cost-benefit ratio and located sites, and generally played out a settlement strategy that minimized the ratio of energy expanded to energy produced.

Keywords: palaeo-environment, prehistory, palaeo-ecology, zooarchaeology

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Authors: Melis Inalpulat, Levent Genc

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Land Use Land Cover (LULC) changes due to human activities and natural causes have become a major environmental concern. Assessment of temporal remote sensing data provides information about LULC impacts on environment. Land Surface Temperature (LST) is one of the important components for modeling environmental changes in climatological, hydrological, and agricultural studies. In this study, LULC changes (September 7, 1984 and July 8, 2014) especially in agricultural lands together with population changes (1985-2014) and LST status were investigated using remotely sensed and census data in South Marmara Watershed, Turkey. LULC changes were determined using Landsat TM and Landsat OLI data acquired in 1984 and 2014 summers. Six-band TM and OLI images were classified using supervised classification method to prepare LULC map including five classes including Forest (F), Grazing Land (G), Agricultural Land (A), Water Surface (W), and Residential Area-Bare Soil (R-B) classes. The LST image was also derived from thermal bands of the same dates. LULC classification results showed that forest areas, agricultural lands, water surfaces and residential area-bare soils were increased as 65751 ha, 20163 ha, 1924 ha and 20462 ha respectively. In comparison, a dramatic decrement occurred in grazing land (107985 ha) within three decades. The population increased % 29 between years 1984-2014 in whole study area. Along with the natural causes, migration also caused this increase since the study area has an important employment potential. LULC was transformed among the classes due to the expansion in residential, commercial and industrial areas as well as political decisions. In the study, results showed that agricultural lands around the settlement areas transformed to residential areas in 30 years. The LST images showed that mean temperatures were ranged between 26-32 °C in 1984 and 27-33 °C in 2014. Minimum temperature of agricultural lands was increased 3 °C and reached to 23 °C. In contrast, maximum temperature of A class decreased to 41 °C from 44 °C. Considering temperatures of the 2014 R-B class and 1984 status of same areas, it was seen that mean, min and max temperatures increased by 2 °C. As a result, the dynamism of population, LULC and LST resulted in increasing mean and maximum surface temperatures, living spaces/industrial areas and agricultural lands.

Keywords: census data, landsat, land surface temperature (LST), land use land cover (LULC)

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Authors: Masoomeh Alsadat Mirshafaei

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The present study aims to explore the effect of an 8-week aerobic training regimen combined with cinnamon extract on serum irisin and leptin levels in insulin-resistant rats. Additionally, this research leverages various machine learning (ML) and deep learning (DL) algorithms to model the complex interdependencies between exercise, nutrition, and metabolic markers, offering a groundbreaking approach to obesity and diabetes research. Forty-eight Wistar rats were selected and randomly divided into four groups: control, training, cinnamon, and training cinnamon. The training protocol was conducted over 8 weeks, with sessions 5 days a week at 75-80% VO2 max. The cinnamon and training-cinnamon groups were injected with 200 ml/kg/day of cinnamon extract. Data analysis included serum data, dietary intake, exercise intensity, and metabolic response variables, with blood samples collected 72 hours after the final training session. The dataset was analyzed using one-way ANOVA (P<0.05) and fed into various ML and DL models, including Support Vector Machines (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN). Traditional statistical methods indicated that aerobic training, with and without cinnamon extract, significantly increased serum irisin and decreased leptin levels. Among the algorithms, the CNN model provided superior performance in identifying specific interactions between cinnamon extract concentration and exercise intensity, optimizing the increase in irisin and the decrease in leptin. The CNN model achieved an accuracy of 92%, outperforming the SVM (85%) and RF (88%) models in predicting the optimal conditions for metabolic marker improvements. The study demonstrated that advanced ML and DL techniques could uncover nuanced relationships and potential cellular responses to exercise and dietary supplements, which is not evident through traditional methods. These findings advocate for the integration of advanced analytical techniques in nutritional science and exercise physiology, paving the way for personalized health interventions in managing obesity and diabetes.

Keywords: aerobic training, cinnamon extract, insulin resistance, irisin, leptin, convolutional neural networks, exercise physiology, support vector machines, random forest

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

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In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

Procedia PDF Downloads 198

Authors: Dahiru Muhammad, Muhammad Danladi, Muhammad Yahaya, Adamu Garba

Abstract:

This paper put forward the potentialities of biomass for energy as divers means of sustainable economic development. The paper explains, in brief, the ways or methods that are used to generate energy from biomass, such as combustion, pyrolysis, anaerobic, and gasification, and also how biomass for energy can enhance the sustainable economic development of a Nation. Currently, the nation depends on fossil fuels as a sources of generating its energy which is finite and deflectable with time, while on the other hand, biomass is an alternative and endless product which consists of forest biomass, agricultural residues, and energy crops. Finally, recommendations and conclusion were made on the role of biomass for energy in improving sustainable economic development.

Keywords: biomass, energy, sustainability, economic

Procedia PDF Downloads 134

Authors: Mariam Mtunguja, Henry Laswai, Yasinta Muzanilla, Joseph Ndunguru

Abstract:

Starchy tuberous roots of cassava provide food for people but also find application in various industries. Recently there has been the focus of concentrated research efforts to fully exploit its potential as a sustainable multipurpose crop. High starch yield is the important trait for commercial cassava production for the starch industries. Furthermore, cyanide present in cassava root poses a health challenge in the use of cassava for food. Farming communities where cassava is a staple food, prefer bitter (high cyanogenic) varieties as protection from predators and thieves. As a result, food insecure farmers prefer growing bitter cassava. This has led to cyanide intoxication to this farming communities. Cassava farmers can benefit from marketing cassava to starch producers thereby improving their income and food security. This will decrease dependency on cassava as staple food as a result of increased income and be able to afford other food sources. To achieve this, adequate information is required on the right cassava cultivars and appropriate harvesting period so as to maximize cassava production and profitability. This study aimed at identifying suitable cassava cultivars and optimum time of harvest to maximize starch production. Six commonly grown cultivars were identified and planted in a complete random block design and further analysis was done to assess variation in physicochemical characteristics, starch yield and cyanogenic potentials across three environments. The analysis showed that there is a difference in physicochemical characteristics between landraces (p ≤ 0.05), and can be targeted to different industrial applications. Among landraces, dry matter (30-39%), amylose (11-19%), starch (74-80%) and reducing sugars content (1-3%) varied when expressed on a dry weight basis (p ≤ 0.05); however, only one of the six genotypes differed in crystallinity and mean starch granule particle size, while glucan chain distribution and granule morphology were the same. In contrast, the starch functionality features measured: swelling power, solubility, syneresis, and digestibility differed (p ≤ 0.05). This was supported by Partial least square discriminant analysis (PLS-DA), which highlighted the divergence among the cassavas based on starch functionality, permitting suggestions for the targeted uses of these starches in diverse industries. The study also illustrated genotypic difference in starch yield and cyanogenic potential. Among landraces, Kiroba showed potential for maximum starch yield (12.8 t ha-1) followed by Msenene (12.3 t ha-1) and third was Kilusungu (10.2 t ha-1). The cyanide content of cassava landraces was between 15 and 800 ppm across all trial sites. GGE biplot analysis further confirmed that Kiroba was a superior cultivar in terms of starch yield. Kilusungu had the highest cyanide content and average starch yield, therefore it can also be suitable for use in starch production.

Keywords: cyanogen, cassava starch, food security, starch yield

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Authors: Ashenafi Sileshi Etefa, Bezawit Worku Yenealem

Abstract:

Micro-insurance is a relatively new concept that is just being introduced in Ethiopia. For an agrarian economy dominated by small holder farming and vulnerable to natural disasters, mainly drought, the need for an Index-Based Micro Insurance (IBMI) is crucial. Since IBMI solves moral hazard, adverse selection, and access issues to poor clients, it is preferable over traditional insurance products. IBMI is being piloted in drought prone areas of Ethiopia with the aim of learning and expanding the service across the country. This article analyses the demand of IBMI and the barriers to demand and finds that the demand for IBMI has so far been constrained by lack of awareness, trust issues, costliness, and the level of basis risk; and recommends reducing the basis risk and increasing the role of government and farmer cooperatives.

Keywords: agriculture, index based micro-insurance (IBMI), drought, micro-finance institution (MFI)

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Authors: Zeeshan Muzammal, Usama Latif, Fouzia Younas, Syed Muhammad Hassan, Samia Razaq

Abstract:

Unexpected rainfall has always been an issue in the lifetime of crops and brings destruction for the farmers who harvest them. Unfortunately, Pakistan is one of the countries in which untimely rain impacts badly on crops like wash out of seeds and pesticides etc. Pakistan’s GDP is related to agriculture, especially in rural areas farmers sometimes quit farming because leverage of huge loss to their crops. Through our surveys and research, we came to know that farmers in the rural areas of Pakistan need rain information to avoid damages to their crops from rain. We developed a prototype using ICTs to inform the farmers about rain one week in advance. Our proposed solution has two ways of informing the farmers. In first we send daily messages about weekly prediction and also designed a helpline where they can call us to ask about possibility of rain.

Keywords: ICTD, farmers, mobile based, Pakistan, rural areas, weather prediction

Procedia PDF Downloads 572

Authors: Zlatina Peteva, Stanislava Georgieva, Mona Stancheva, Lubomir Makedonsky

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Yessotoxins (YTX) and pectenotoxins (PTX) are lipophilic polyether marine biotoxins that accumulate in shellfish and are regulated in the European Union. Aim of this study is investigation of the presence of YTXs and PTXs in Black Sea mussel (Mytilus galloprovincialis) on the North Black Sea cast of Bulgaria. YTX and PTX-2 have been identified in samples of the digestive glands of wild and cultivated samples. Mussel samples are harvested from two locations in important farming area on the Bulgarian North Black Sea coast in the period September 2016 – May 2017. Samples are analyzed by liquid chromatography tandem mass spectrometry (LC–MS/MS). In about 50 % of the samples analyzed the obtained concentration of YTX ranged from 10 – 5000 pg/g hepatopancreas and PTX-2 ranged from 300-7000 pg/g hepatopancreas. This is to our knowledge the first report of occurrence of lipophilic toxins in mussels from Bulgaria.

Keywords: yessotoxins, pectenotoxins, Black sea, mussels

Procedia PDF Downloads 151

Authors: Dahiru Muhammad, Muhammad Danladi, Adamu Garba, Muhammad Yahaya

Abstract:

This paper put forward the potentialities of biomass for energy as divers means of sustainable economic development. The paper explains in brief the ways or methods that are used to generate energy from biomass, such as combustion, pyrolysis, anaerobic, and gasification, and also how biomass for energy can enhance the sustainable economic development of a Nation. Currently, the nation depends on fossil fuels as a sources of generating its energy which is finite and deflectable with time, while on the other hand, biomass is an alternative and endless product which consists of a forest biomass, agricultural residues, and energy crops. Finally, recommendations and conclusion were made on the role of biomass for energy in improving sustainable economic development.

Keywords: biomass, energy, sustainable, economic, development

Procedia PDF Downloads 128

Authors: Bryan Clark B. Hernandez, Eugene C. Herrera, Kazuo Nadaoka

Abstract:

Mangrove forests thriving in intertidal zones in tropical and subtropical regions of the world offer a range of ecosystem services including carbon storage and sequestration. They can regulate the detrimental effects of climate change due to carbon releases two to four times greater than that of mature tropical rainforests. Moreover, they are effective natural defenses against storm surges and tsunamis. However, their proliferation depends significantly on the prevailing hydroperiod at the coast. In the Philippines, these coastal ecosystems have been severely threatened with a 50% decline in areal extent observed from 1918 to 2010. The highest decline occurred in 1950 - 1972 when national policies encouraged the development of fisheries and aquaculture. With the intensive land use conversion upstream, changes in the freshwater-saltwater envelope at the coast may considerably impact mangrove growth conditions. This study investigates a developing urban watershed in Kalibo, Aklan province with a 220-hectare mangrove forest replanted for over 30 years from coastal mudflats. Since then, the mangrove forest was sustainably conserved and declared as protected areas. Hybrid land cover classification technique was used to classify Landsat images for years, 1990, 2010, and 2017. Digital elevation model utilized was Interferometric Synthetic Aperture Radar (IFSAR) with a 5-meter resolution to delineate the watersheds. Using numerical modelling techniques, the hydrologic and hydraulic analysis of the influence of land cover change to flow and sediment dynamics was simulated. While significant land cover change occurred upland, thereby increasing runoff and sediment loads, the mangrove forests abundance adjacent to the coasts for the urban watershed, was somehow sustained. However, significant alteration of the coastline was observed in Kalibo through the years, probably due to the massive land-use conversion upstream and significant replanting of mangroves downstream. Understanding the hydrologic-hydraulic response of these watersheds to change land cover is essential to helping local government and stakeholders facilitate better management of these mangrove ecosystems.

Keywords: coastal mangroves, hydrologic model, land cover change, Philippines

Procedia PDF Downloads 122

Authors: Mohamad R. Moshtagh, Ahmad Bagheri

Abstract:

Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime.

Keywords: fault detection, gearbox, machine learning, wiener method

Procedia PDF Downloads 80