Almas Samin Hasan

Abstract: Agrivoltaics is the dual use of land by combining agricultural crop production with a photovoltaic (PV) system. In this work, the impact of shading has been analyzed on a particular crop to understand the future prospects of the agrivoltaic system on that land. A set up is made on land situated in the Bogura district of Bangladesh. A total of 15 sample crops have been considered from both the shaded and unshaded regions to investigate the grain number, height of the crops, perimeter, and number of small branches. It is evident from the study that, unshaded sample crops have a higher number of grains, have a greater height, and have a larger perimeter than shaded region crops. Though there are some dissimilarities, such as the shaded region sample crop showing a higher grain number, the total grain number shows that unshaded region crops have a higher number of grains. All the data did not exceed the average 30% difference between shaded and unshaded data, which ensures the future implementation prospects of agrivoltaic in that field.

Abstract: This study proposes an agrivoltaic monitoring system that is designed based on land in Bangladesh. The simulation part of the study considers the rainy condition of the location to understand the impact on the energy harvesting of the system. The required amount of water for irrigation and the number of solar photovoltaic (PV) panels are calculated considering the rainfall and shadow impact, respectively. The study shows that the designed monitoring system utilizes a small amount of energy from the agrivoltaic system, where around 2665 PV modules can be set up on 1 hectare of land. A 1.5 kW pump is required, which has a flow rate of 120 L/min considering the rainfall of the area, to irrigate the land. The designed system can automatically turn on the irrigation based on the sensor data and can be controlled and monitored remotely.

Abstract: Big Data, cloud-based operations, Internet of Things and crowdsourcing are the recently developed information and web-based technologies. However finding data sets for these Earth observation (EO) data processing systems requires tremendous labor. In addition data acquisition, communication, processing, dissemination, and archiving seems to be difficult and time-consuming as classified datasets are difficult to find. Some novel approaches must be found for taking preparation for data analysis, extracting information from large data sets, and promptly making the information much more accessible to researchers. We developed a method for processing EO data for machine learning that includes cutting-edge technology with the infrastructure of EO data processing. The website we created offers tools like in-depth search choices, data filtering, and data previewing to help users identify and select the most relevant and superior data for their use in research projects. In order to facilitate the researcher's work, we have created a Python script and a Chrome extension that allow users to search for and download data sets according to certain criteria. Despite these there are a tremendous number of data that gets corrupted and becomes futile because of the image quality, focus, solar are radiation and cloud coverage; welcoming these data alongside the clear perfect images occupies ample space and prevent sending useful images to earth station for analysis as there is an onboard space shortage for storing. To solve this problem we developed an algorithm latched with ML which not only is capable of comparing effectively but also sends flawless images by excluding the faulty images automatically consequently saving time as well as storage and boosting the efficiency of the research process. In a nutshell, our methodology provides a modern solution to the elderly problem of handling EO data where we use machine learning in the context of developing information and web-based technology. In addition, we can manage EO data processing systems and connect the general public to these applications much more effectively by utilizing contemporary technology and comprehending their disruptive potential. Our approach with data collecting, integration, and distribution makes it a useful tool for researchers, practitioners, and decision-makers in environmental and catastrophe management as it is scalable and customizable while finding datasets for Machine Learning applications particularly for Earth Observation