Automatic Plant Counting using Deep Neural Networks

dc.contributor.author	Pidhirniak, Oleh
dc.date.accessioned	2019-02-19T15:25:28Z
dc.date.available	2019-02-19T15:25:28Z
dc.date.issued	2019
dc.identifier.citation	Pidhirniak, Oleh. Automatic Plant Counting using Deep Neural Networks : Master Thesis : manuscript / Oleh Pidhirniak ; Supervisor Orest Kupyn ; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2019. – 23 p. : ill.	uk
dc.identifier.uri	http://er.ucu.edu.ua/handle/1/1334
dc.language.iso	en	uk
dc.subject	Automatic Plant Counting	uk
dc.subject	Deep Neural Networks	uk
dc.title	Automatic Plant Counting using Deep Neural Networks	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	Crop counting is a challenging task for today’s agriculture. Increasing demand for food supplies creates a necessity to perform farming activities more efficiently and precisely. Usage of remote sensing images can help to better control the population of the plants grown and forecast future yields, profits and disasters. In this study we offer a series of approaches for plant counting using foreground extraction algorithms, deep neural networks. The study introduces innovative to the field approach of densely distributed plants counting using density map regression with the accuracy of 98.9% on palm oil trees dataset.	uk