International Conference on Advancements in Computing [ICAC]

Now showing 1 - 3 of 3

Embargo
Energy and Operations Optimization for Effective Greenhouse Management
(IEEE, 2022-12-09) Prihan Nimsara, K. I.; Bodaragama, J.; Roshan Maduwantha, K. A.; Fernando, S. D.
IoT technology-based process automation that can be applied to a greenhouse leads to making condition management and status monitoring more robust while leading to saving energy and resources. The proposed system which is based on IoT technology and MQTT protocol can set optimal growth conditions for plant and seed growth within the greenhouse. The sensor-based inputs are to be transformed into the processed values based on the defined logic and the standard benchmarks gathered from the local agricultural authorities. The key areas of condition monitoring to be done via temperature, humidity, soil moisture, and lighting can ultimately yield an increased harvest having supported both the plant and seeds-based implementations for multiple types of plants. One of the most important factors to consider is that the farmers can have energy savings through the proposed solution by controlling the actuators in an optimal manner and reducing manual intervention by a considerable amount. The excess usage of electricity by lights and cooling fan usage in the greenhouse can be controlled with real-time data tracking and better analytics. The use of water can be properly maintained for the plants by putting only the required amount will make the soil wet and spraying the required amount to air will make better humidity control. Thus, the real-time condition-based controlling of the actuators leads to making the greenhouse operations more optimal and better utilization of resources and energy which ultimately results in financial benefits for the greenhouse owner. Based on the evaluated power consumption of the greenhouse power usage before and after the system was installed, the newly introduced system can save energy by having optimal control of actuators by performing algorithmic calculations to meet only the required level of weather conditions. This is to be proven experimentally by implementing the proposed system for a defined period of time under the monitoring of energy usage.
Embargo
An Automated System for Employee Recruitment Management
(IEEE, 2022-12-09) Silva, G.L.L.I.; Jayasinghe, T.L; Rangalla, R.H.M; Gunarathna, W.K.L; Tissera, W
Recruitment of employees is an important process in the human resource management of a company. Currently, most of the recruitment process is done manually in many companies. This manual process may be time-consuming and possibly may be erroneous in employing inappropriate individuals. This may result in the loss of time, money, and efficiency of a company. As a solution to the above problem, we are considering developing an automated process for recruitment. The scope of the system is to cover not only the recruitment process but also to provide job seekers a platform to identify their current skills, help them identify the current skill trends that are required by companies, and provide the ability to automatically generate their resumes through the system. On the other hand, employers will save a lot of time and money since the system will automate the processes such as skill matching of the employee and the company, shortlisting of resumes, and scheduling interviews. The platform involves features such as online mock interview hosting, automated scheduling, and a pre-interview quiz with a monitoring background. To achieve the above components, machine learning algorithms are used along with other technologies such as web scraping.
Embargo
Predictive Analytics Platform for Organic Cultivation Management
(IEEE, 2019-12-05) Rathnayake, R. M. S. M; Ekanayake, E. W. L. M. B; Kahandawala, K. A. I. P; de Silva, W. G. S. C; Nawinna, D. P; Kasthurirathna, D
There is an increasing demand for organic farming as an environmentally friendly alternative to industrial agricultural system. It is a method of farming that does not involve pesticides, fertilizers, genetically modified organisms, and growth hormones. Organic farming yields vital benefits such as preservation of soil's organic composition, fertility, structure and biodiversity, reduce erosion and reduce the risks of human, animal, and environmental exposure to toxic materials. This paper presents design and development of a software platform for supporting sustainability of organic agriculture system, which has been implemented as a proof of concept in Sri Lanka. The predictive analytics based service platform that not only supports farming decisions of organic farmers but also offers an electronic market place for organic foods. The proposed system is capable of predicting organic harvests, prices and provide decision support on crop selection for upcoming cultivations. To implement this system, machine learning and optimization techniques have been used. In addition, it uses block chain technology to maintain authentication and identity management of organic farmers so that the consumers can trust they get genuine organic food.

International Conference on Advancements in Computing [ICAC]

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