28 research outputs found
The Effect of Different Planting Methods on Growth and Yield of Selected of Cassava (Manihot esculenta) Cultivars
No full textBackground: Cassava (Manihot esculenta Crantz) is a source of carbohydrate among the population after maize and rice and
highly contributes to food security and livelihood to majority of small scale farmers in Sri Lanka as well as in African continent. The
production of these starchy plants is declining due to the problem of low yield, high labor cost, pest and diseases damage and
shortage of land. However among the yield limiting factors of cassava, the planting method of stem cuttings which depend on plant
cultivar and environmental conditions. Therefore, the present study was carried out to reveal information on the effect of planting
methods on the growth and yield attributes of cassava.
Methods: The field experiment was conducted at the Farmer’s field in the Batticaloa and laboratory experiment was led in South
Eastern University of Sri Lanka which is located in Sri Lanka. The field trail was carried out over a period of four months during from
May to September in 2018. The treatments used were three planting positions (Angled, vertical and horizontal) and two cultivars (“cv.
Local” and “cv Kirikawadi”) were combined in factorial arrangement and laid out in randomized complete block design with three
replications.
Result: The result revealed that root yield was significantly (P < 0.05) affected by the interaction effects of the planting position and
varieties. Significant differences were observed among planting methods in all tested variables. Based on the study, storage roots
yield of cassava could be enhanced by planting method of angled position
Mitigation techniques for agricultural pollution by precision technologies with a focus on the Internet of Things (IoTs): a review
No full textThe widespread of Information and Communication Technology (ICT) in the past decades brought numerous advantages to many
individuals and most of the organizations everywhere in the world. In the 21st century, the most significant technology is the Internet
of Things (IoTs) which has developed rapidly covering most of applications in the health, civil, military and agriculture sectors also.
Precision Agriculture (PA), as the combination of information, communication and control technologies in agronomic practices, is
emerging time by time. Also, precision agriculture is considered a smart farming system on the basis of modern technologies to
regulate, examine and manage changes inside an agricultural field for cost-effectiveness, sustainability and optimal protection of
environment. Meanwhile, agricultural practices are contributing to environmental pollution due to poor management which is further
disturbing food security, health and climate. One of the best strategies to overcome this challenge can be introducing the deployment
of precision technologies for the development of agricultural productivity while reducing the environmental degradation. Therefore,
the key objective of this review was to discuss the mitigation techniques for agricultural pollution and enhance the agricultural production
by smart technologies like IoTs. This paper summarizes the main categories of IoTs, Precision Agriculture, agricultural pollution and
finally, mitigation practices on environmental degradation
UAV-Based Remote Sensing and Artificial Intelligence in Precision Agriculture and Biosecurity
No full textReduction techniques for consequences of climate change by Internet of Things (IoT) with an emphasis on the agricultural production: a review
No full textNowadays, Internet of Things (IoT) is one of the emerging technologies offers assistance in many important sectors. Agriculture is one of the important sectors globally which contributes to food security. The trends of IoT can be incorporated into various agricultural activities such as livestock monitoring and crop monitoring, etc. Various sensors and IoT gadgets are used in live monitoring, collection and processing of data. Processed data could be used to make predictions. Climate change is one of the critical environmental issue at the moment. We do not have our control over the occurrence of natural disasters, though the communication and forecasting offer a great support to us in protecting lives and to mend the ways towards a better future. Climate changes affect the land and water in many ways. The detrimental effect of continuous or unexpected flood, drought is some of the major concerns. The aim of this study was to introduce new technologies into agricultural production processes to improve those by reducing the impact of climate change over them. Therefore, this article discusses the applications of IoT in agriculture to mitigate the problems and consequences of climate change such as flood, drought, plant diseases, agricultural pollution and greenhouse gas emission with an emphasis on agricultural production
Impact of environmental factors on trend of milk production: a study based in Batticaloa district, Sri Lanka
No full textIn rural area milk production is playing vital role to maintain the part food security in
terms of the nutrition. Nowadays, all the countries are taking the policy making to
maintain the agricultural base production for small medium entrepreneur activities to
engage the fresh milk for healthy life. The milk contain all the nutrients, which are
easily absorbed and assimilated by the body. The milk universally consumes production.
Even though the environmental factors involve the milk yield production and
management practice of the farmers. Mostly The smallholder farmers’ livestock are a
living bank that serves as a financial source for period of economic distress. Motivate
the small scale dairy farmers invest in this filed to profitable enterprise with commercial
dairy farming. The secondary data collected from Divisional veterinary regional office
in Batticaloa. Furthermore, special interview with veterinary hospital doctors and
livestock farmers were arranged to identify the major problems of decreasing the milk
production and death of livestock during this period between beginning of 2018 and
up to now. Regarding the discussion with above people who suggested that sudden
dropped in milk production around 40% compared with past years and increase in
death of cattle and buffalo due to several reasons and also stated, the drought is not
that much reduce the milk production because this study found the inverse relationship
with rainfall and above 28oC temperature but the major reason was lack of awareness
on livestock management practices. Therefore, the present study was aimed to determine
the correlation between environmental conditions such as atmospheric temperature
and rainfall on milk production, influence of management factors on cattle and buffalos
in Batticaloa District in Sri Lanka by using descriptive analysis. This paper
recommended that, farmers should be considered the proper management practices to
decrease the death of animals while increase the production based on environmental
conditions. The policy makers should consider provide more awareness programs related
to current issues among famers in this study area
An IoT based low-cost weather monitoring system for smart farming
No full textNABackground: Weather monitoring is an important aspect of crop cultivation for reducing economic loss while increasing productivity.
Weather is the combination of current meteorological components, such as temperature, wind direction and speed, amount and kind
of precipitation, sunshine hours and so on. The weather defines a time span ranging from a few hours to several days. The periodic
or continuous surveillance or the analysis of the status of the atmosphere and the climate, including parameters such as temperature,
moisture, wind velocity and barometric pressure, is known as weather monitoring. Because of the increased usage of the internet,
weather monitoring has been upgraded to smart weather monitoring. The Internet of Things (IoT) is one of the new technology that
can help with many precision farming operations. Smart weather monitoring is one of the precision agriculture technologies that use
sensors to monitor correct weather. The main objective of the research is to design a smart weather monitoring and real-time alert
system to overcome the issue of monitoring weather conditions in agricultural farms in order for farmers to make better decisions.
Methods: Different sensors were used in this study to detect temperature and humidity, pressure, rain, light intensity, CO2
level,
wind speed and direction in an agricultural farm and real time clock sensor was used to measured real time weather data. The
major component of this system was an Arduino Uno microcontroller and the system ran according to a program written in the
Arduino Uno software.
Result: This is a low-cost smart weather monitoring system. This system’s output unit were a liquid crystal display and a GSM900A
module. The weather data was displayed on a liquid crystal display and the GSM900A module was used to send the data to a mobile
phone. This smart weather station was used to monitor real-time weather coN
Assessment on consequences and benefits of the smart farming techniques in Batticaloa district, Sri Lanka
No full textICT in agriculture (e-Agriculture) is an emerging field focused on improving agricultural production and rural
development. The study was aimed to identify the consequences, promotion and benefits of farmer community towards
the e-agriculture. Therefore, primary data were collected from the randomly selected 158 farmers by means of a well designed questionnaire survey during the period of February to April, 2019. The demographic characteristics of the farming community showed that only 5.1% of respondents were illiterate in this area. According to the study, 36.1% of respondents used telephone as ICT tool for agriculture.0% of respondents used any ICT tools. Consequences index (CI) ranged from 114 to 586, where 114 indicated that the farmers strongly disagreed that there would be some consequences by not using ICT and 586 indicated that the farmers accepted that they would suffer in the future by not using ICT in their agricultural activities. Promotion measures index (PMI) ranged from 508 to 618, where 508 indicated the farmers’ response on the provision of a computer, Internet access, and technician to each village was comparatively less whereas 618 indicated that the farmers accept the provision of incentives and finance may promote the use of ICT by a greater
extent. Benefits of usage index (BUI) ranged from 86 to 140, where 86 indicated that the response of farmers on the option “cheaper” was less and 140 indicated that the farmers accepted the use of ICT in Agriculture helps them to acquire timely information related to their particular agricultural activities. Limiting factors index (LFI) ranged from 100 to 454,
where 100 indicated that a high number of farmers strongly disagreed on “no perceived economic benefit” by using ICT and 454 indicated that a high number of farmers accept that the lack of training is the main limiting factor of using ICT in their agricultural activities
Understanding Atrocities: Remembering, Representing and Teaching Genocide
No full textUnderstanding Atrocities is a wide-ranging collection of essays bridging scholarly and community-based efforts to understand and respond to the global, transhistorical problem of genocide. The essays in this volume investigate how evolving, contemporary views on mass atrocity frame and complicate the possibilities for the understanding and prevention of genocide. The contributors ask, among other things, what are the limits of the law, of history, of literature, and of education in understanding and representing genocidal violence? What are the challenges we face in teaching and learning about extreme events such as these, and how does the language we use contribute to or impair what can be taught and learned about genocide? Who gets to decide if it's genocide and who its victims are? And how does the demonization of perpetrators of atrocity prevent us from confronting the complicity of others, or of ourselves? Through a multi-focused and multidisciplinary investigation of these questions, Understanding Atrocities demonstrates the vibrancy and breadth of the contemporary state of genocide studies.
With contributions by: Amarnath Amarasingam, Andrew R. Basso, Kristin Burnett, Lori Chambers, Laura Beth Cohen, Travis Hay, Steven Leonard Jacobs, Lorraine Markotic, Sarah Minslow, Donia Mounsef, Adam Muller, Scott W. Murray, Christopher Powell, and Raffi SarkissianCanadian Federation for the Humanities and Social Sciences, Awards to Scholarly Publications ProgramLibrary OA Fun
Robinson Ridge Manual and Semi-Automated Vegetation Labels with UAV Orthomosaics
No full textProgress Code: completedStatement: During data collection, poor image quality in certain sections of the orthomosaic—caused by lighting conditions and snow—reduced the visual clarity necessary for reliable interpretation.
In the analysis stage, accurately identifying vegetation species was challenging due to suboptimal image resolution. Furthermore, the lack of sufficient ground truth data made it difficult to validate segmentation results and assess accuracy.<b>Purpose</b><br/>This dataset supports the development of machine learning models for vegetation segmentation in Antarctic ecosystems and enables reproducible remote sensing analyses by providing both imagery and associated ground-truth labels in standard GIS-compatible formatsThis dataset supports the development of machine learning models for vegetation segmentation in Antarctic ecosystems and enables reproducible remote sensing analyses by providing both imagery and associated ground-truth labels in standard GIS-compatible formats. <br/><br/>1. Ground Truth Labels This record contains manually and semi-automatically generated pixelwise shapefiles, used to annotate vegetation types with high confidence. This component contains both manually and semi-automatically generated pixel-wise shapefiles classifying four vegetation classes: Usnea spp., black lichen, moss, and non-vegetation. The purpose of these labels is to provide high-quality reference data for training and validating machine learning models for vegetation segmentation. <br/><br/>Manual labelling was conducted using ground truth points gathered during field campaigns. Polygons were drawn around confidently identified vegetation patches, focusing on central areas to minimise edge misclassification. (Files: .shp, .dbf, .prj, .shx, and readme.txt) <br/><br/>Semi-automatic labelling was introduced to address the limitations of manual annotation at a GSD of 2.93 cm/pixel. A suite of 28 vegetation indices (VIs) were evaluated, and three (MSAVI and GNDVI) were selected for their spectral separability between classes. (Files: .shp, .dbf, .prj, .shx, and readme.txt) <br/><br/>2. Orthomosaic The RGB and multispectral (MS) images captured by UAVs were processed into high-resolution orthomosaics using Agisoft Metashape 1.6.6 and georeferenced via QGIS 3.2.0. These orthomosaics serve as the base imagery for generating labels and training machine learning models. Output formats include .tif for orthomosaics, .ovr for overviews, and .pdf reports documenting the image processing steps. <br/><br/>Data Collection and Analysis Imagery was captured in January 2023 at Robinson Ridge, Antarctica, using a BMR3.9RTK UAV equipped with a MicaSense Altum sensor and Sony Alpha 5100 camera, flown at 70 m altitude (GSD ≈ 2.93 cm/pixel). Over 2,800 images were collected over ~5.15 ha. <br/><br/>Usage Notes · Embargoed files require permission for access. · Refer to the included readme.txt files in each record for file structure, formats, and usage instructions. · The dataset is optimised for developing and validating deep learning models for remote sensing classification in polar environments
Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments
No full textThe proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML) models for mapping plant species in natural environments. However, a critical gap exists in the literature regarding the use of deep learning (DL) techniques that integrate MS data and vegetation indices (VIs) with different feature extraction techniques to map invasive species in complex natural environments. This research addresses this gap by focusing on mapping the distribution of the Broad-leaved pepper (BLP) along the coastal strip in the Sunshine Coast region of Southern Queensland in Australia. The methodology employs a dual approach, utilising classical ML models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) in conjunction with the U-Net DL model. This comparative analysis allows for an in-depth evaluation of the performance and effectiveness of both classical ML and advanced DL techniques in mapping the distribution of BLP along the coastal strip. Results indicate that the DL U-Net model outperforms classical ML models, achieving a precision of 83%, recall of 81%, and F1–score of 82% for BLP classification during training and validation. The DL U-Net model attains a precision of 86%, recall of 76%, and F1–score of 81% for BLP classification, along with an Intersection over Union (IoU) of 68% on the separate test dataset not used for training. These findings contribute valuable insights to environmental conservation efforts, emphasising the significance of integrating MS data with DL techniques for the accurate mapping of invasive plant species
