干旱区生态环境知识资源中心(Arid): Deep learning with aerial surveys for extensive livestock hotspot recognition in the Brazilian Semi-arid Region

Arid

DOI	10.1590/1413-7054202347010922
	Deep learning with aerial surveys for extensive livestock hotspot recognition in the Brazilian Semi-arid Region
	Lima, Mayara Lopes de Freitas; de Souza, Samara Maria Farias; de Sa, Isabelle Ventura; Santana, Otacilio Antunes
通讯作者	Santana, OA
来源期刊	CIENCIA E AGROTECNOLOGIA
ISSN	1413-7054
EISSN	1981-1829
出版年	2023
卷号	47
英文摘要	In the Brazilian Semi-arid Region, extensive livestock farming with ecoproductive management is the most efficient way to maintain and increase the production of goat products (e.g., meat) with of not depleting environmental resources. This set of actions (induced goat migration and pasture closure) is part of Livestock 4.0, in which Industry 4.0 feed areas are efficiently managed using artificial intelligence and deep learning properly monitored by the producer and the consumer. The objective of this work was to identify pasture areas with Opuntia ficus-indica (Mill, Cactaceae) forage palm species for breeding and production of Capra aegagrus-hircus goats (Lineu, Bovidae) using aerial survey images captured by drones classified using deep learning techniques. The methodological steps of the Industry Architecture Reference Model 4.0 were adapted to the field situation (Semi-arid Region) including (A) study area delimitation, (B) image collection (by drones), (C) deep learning training, convolutional neural network (CNN) training, (D) training accuracy analysis, and (E) automatic goat production evaluation and validation. The area classification based on the forage palm density allowed us to measure the environmental degradation caused by livestock. Stimulated goat migration reduced this degradation as well as increased goat biomass and volume production.
英文关键词	Industry 4 0 convolutional neural network sustainable farming smart factory
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000954044100001
WOS关键词	TECHNOLOGY ; SCIENCE
WOS类目	Agriculture, Multidisciplinary ; Agronomy
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395757
推荐引用方式 GB/T 7714	Lima, Mayara Lopes de Freitas,de Souza, Samara Maria Farias,de Sa, Isabelle Ventura,et al. Deep learning with aerial surveys for extensive livestock hotspot recognition in the Brazilian Semi-arid Region[J],2023,47.
APA	Lima, Mayara Lopes de Freitas,de Souza, Samara Maria Farias,de Sa, Isabelle Ventura,&Santana, Otacilio Antunes.(2023).Deep learning with aerial surveys for extensive livestock hotspot recognition in the Brazilian Semi-arid Region.CIENCIA E AGROTECNOLOGIA,47.
MLA	Lima, Mayara Lopes de Freitas,et al."Deep learning with aerial surveys for extensive livestock hotspot recognition in the Brazilian Semi-arid Region".CIENCIA E AGROTECNOLOGIA 47(2023).