Search

Your search keyword '"Irfan Fitriawan"' showing total 2 results
Start Over "Irfan Fitriawan" Database Complementary Index
2 results on '"Irfan Fitriawan"'

1. Bird repeller for agriculture lands and orchards.

Author

Krishnan, T., Nandhu, M., Kumar, R. Kishore, Asokan, J., Chellaswamy, C., and Ramasubramanian, B.

Subjects
GRAIN farming, AGRICULTURE, OBJECT recognition (Computer vision), MOTION detectors, BERRIES, FIELD crops, WHEAT, CORN
Abstract

The economy of the countries like India, China, Nepal, and Indonesia has a huge dependency on agriculture. So it is important to increase or keep a stable production of food commodities like rice, wheat, corn, barley, and fruits like apples, berries, grapes in vineyards and etc. But apart from climatic changes and plant disease bird pest is one of the problems in crop field and fruit farms. Because every year a considerable amount of production of crops and fruits is consumed and wasted by birds. The existing systems are utilizing motion sensors, IR sensors, and PIR sensors are used in the existing systems. The problem with the existing system they can't identify the object. Because of that no matter what interferes with the sensors it will alarm and starts sounding. So this paper has been providing a solution to the problems of existing systems in the way of neural networks and deep learning to protect the grains and fruits in the farming environment from birds. In this method, the system utilizes a visual unit, power unit, acoustic speaker unit, and control and processing unit. The processing unit contains a program that is based on the DNN object detection algorithm and a dataset called COCO-Common Objects in Context. With the help of peripherals and programs, it can able to detects birds in its range. The DNN Detection model is detecting the moving subject. If any birds are detected from the video feed a threat sound is broadcasted throughout the surrounding. This could be an effective solution for bird pest problems in agriculture fields. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

2. Estimating bird detection distances in sound recordings for standardizing detection ranges and distance sampling.

Author

Darras, Kevin, Furnas, Brett, Fitriawan, Irfan, Mulyani, Yeni, and Tscharntke, Teja

Subjects
BIRD ecology, SPECIES diversity, FOREST products, POPULATION density, SOUND recordings
Abstract

Abstract: Autonomous sound recorders are increasingly used to survey birds, and other wildlife taxa. Species richness estimates from sound recordings are usually compared with estimates obtained from established methods like point counts, but so far the comparisons were biased: Detection ranges usually differ between the survey methods, and bird detection distance data are needed for standardizing data from sound recordings. We devised and tested a method for estimating bird detection distances from sound recordings, using a reference recording of test sounds at different frequencies, emitted from known distances. We used our method to estimate bird detection distances in sound recordings from tropical forest sites where point counts were also used. We derived bird abundance and richness measures and compared them between point counts and sound recordings using unlimited radius and fixed radius counts, as well as distance sampling modelling. First we show that it is possible to accurately estimate bird detection distances in sound recordings. We then demonstrate that these data can be used to standardize the detection ranges between point counts and sound recordings with a fixed‐radius approach, leading to higher abundance and richness estimates for sound recordings. Our distance‐sampling approach also revealed that sound recorders sampled significantly higher bird densities than human point counts. We show for the first time that it is possible to standardize detection ranges in sound recordings and that distance sampling can successfully be used too. We revealed that birds were flushed by human observers and that this possibly leads to lower density estimates in point counts, although sound recorders could also have sampled more birds because of their earlier deployment times. Sound recordings are more amenable to distance‐sampling modelling than point counts as they do not exhibit an observer‐induced avoidance effect, and they can easily collect more replicates for obtaining more accurate bird density estimates. Quantifying bird detection distances was so far one important shortcoming that hindered the adoption of modern autonomous sound recording methods for ecological surveys. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results