Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System

In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Z Kavoosi [verfasserIn] M. H Raoufat [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch ; Persisch

Erschienen:	2020

Schlagwörter:	crop residue cover (crc) multiple index miss index quality of feed index precision index

Übergeordnetes Werk:	In: Journal of Agricultural Machinery - Ferdowsi University of Mashhad, 2016, 10(2020), 1, Seite 23-35
Übergeordnetes Werk:	volume:10 ; year:2020 ; number:1 ; pages:23-35

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.22067/jam.v10i1.71498

Katalog-ID:	DOAJ075183242

Internformat


LEADER	01000caa a22002652 4500
001	DOAJ075183242
003	DE-627
005	20230501190047.0
007	cr uuu---uuuuu
008	230228s2020 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.22067/jam.v10i1.71498 \|2 doi
035			\|a (DE-627)DOAJ075183242
035			\|a (DE-599)DOAJaba1e63233c246128add715c281d8ac8
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng \|a per
050		0	\|a S1-972
050		0	\|a TA1-2040
100	0		\|a Z Kavoosi \|e verfasserin \|4 aut
245	1	0	\|a Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System
264		1	\|c 2020
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated by ground and air observations. The purpose of this study was to investigate the capability of aerial images captured by an unmanned aerial vehicle (UAV) in identifying the distances between corn seedlings and as a result, assessing the quality of planter performance. Collected data from ground and aerial imagery were used to calculate seed establishment indices including multiple index, miss index, quality of feed index, precision index and also emergence rate index (ERI), for each plot. Images captured from10 m altitude (4.5 mm pixel-1) could give satisfactory results in relation to our objectives. Our results show that acceptable correlations existed between terrestrial and aerial seedlings spacing data sets (0.94<R<0.98) suggesting the aerial imagery is a good choice for evaluating the seed establishment and estimating ERI. Aerial imagery data source underestimated quality of feed and precision indices, overestimated miss index and could not provide processed data range needed for computing multiple index due to low image resolution, weeds presence within crop rows and overlapping of leaves.
650		4	\|a crop residue cover (crc)
650		4	\|a multiple index
650		4	\|a miss index
650		4	\|a quality of feed index
650		4	\|a precision index
653		0	\|a Agriculture (General)
653		0	\|a Engineering (General). Civil engineering (General)
700	0		\|a M. H Raoufat \|e verfasserin \|4 aut
773	0	8	\|i In \|t Journal of Agricultural Machinery \|d Ferdowsi University of Mashhad, 2016 \|g 10(2020), 1, Seite 23-35 \|w (DE-627)1749281643 \|w (DE-600)3054989-9 \|x 24233943 \|7 nnns
773	1	8	\|g volume:10 \|g year:2020 \|g number:1 \|g pages:23-35
856	4	0	\|u https://doi.org/10.22067/jam.v10i1.71498 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/aba1e63233c246128add715c281d8ac8 \|z kostenfrei
856	4	0	\|u https://jame.um.ac.ir/article_34006_38b58fd10f9f04f7e39fa8ecff4d9c7b.pdf \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2228-6829 \|y Journal toc \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/2423-3943 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 10 \|j 2020 \|e 1 \|h 23-35

Indexfelder

author_variant	z k zk m h r mhr
matchkey_str	article:24233943:2020----::esbltodoemgrfroioigefracoaoiidrlia
hierarchy_sort_str	2020
callnumber-subject-code	S
publishDate	2020
allfields	10.22067/jam.v10i1.71498 doi (DE-627)DOAJ075183242 (DE-599)DOAJaba1e63233c246128add715c281d8ac8 DE-627 ger DE-627 rakwb eng per S1-972 TA1-2040 Z Kavoosi verfasserin aut Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated by ground and air observations. The purpose of this study was to investigate the capability of aerial images captured by an unmanned aerial vehicle (UAV) in identifying the distances between corn seedlings and as a result, assessing the quality of planter performance. Collected data from ground and aerial imagery were used to calculate seed establishment indices including multiple index, miss index, quality of feed index, precision index and also emergence rate index (ERI), for each plot. Images captured from10 m altitude (4.5 mm pixel-1) could give satisfactory results in relation to our objectives. Our results show that acceptable correlations existed between terrestrial and aerial seedlings spacing data sets (0.94<R<0.98) suggesting the aerial imagery is a good choice for evaluating the seed establishment and estimating ERI. Aerial imagery data source underestimated quality of feed and precision indices, overestimated miss index and could not provide processed data range needed for computing multiple index due to low image resolution, weeds presence within crop rows and overlapping of leaves. crop residue cover (crc) multiple index miss index quality of feed index precision index Agriculture (General) Engineering (General). Civil engineering (General) M. H Raoufat verfasserin aut In Journal of Agricultural Machinery Ferdowsi University of Mashhad, 2016 10(2020), 1, Seite 23-35 (DE-627)1749281643 (DE-600)3054989-9 24233943 nnns volume:10 year:2020 number:1 pages:23-35 https://doi.org/10.22067/jam.v10i1.71498 kostenfrei https://doaj.org/article/aba1e63233c246128add715c281d8ac8 kostenfrei https://jame.um.ac.ir/article_34006_38b58fd10f9f04f7e39fa8ecff4d9c7b.pdf kostenfrei https://doaj.org/toc/2228-6829 Journal toc kostenfrei https://doaj.org/toc/2423-3943 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 1 23-35
spelling	10.22067/jam.v10i1.71498 doi (DE-627)DOAJ075183242 (DE-599)DOAJaba1e63233c246128add715c281d8ac8 DE-627 ger DE-627 rakwb eng per S1-972 TA1-2040 Z Kavoosi verfasserin aut Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated by ground and air observations. The purpose of this study was to investigate the capability of aerial images captured by an unmanned aerial vehicle (UAV) in identifying the distances between corn seedlings and as a result, assessing the quality of planter performance. Collected data from ground and aerial imagery were used to calculate seed establishment indices including multiple index, miss index, quality of feed index, precision index and also emergence rate index (ERI), for each plot. Images captured from10 m altitude (4.5 mm pixel-1) could give satisfactory results in relation to our objectives. Our results show that acceptable correlations existed between terrestrial and aerial seedlings spacing data sets (0.94<R<0.98) suggesting the aerial imagery is a good choice for evaluating the seed establishment and estimating ERI. Aerial imagery data source underestimated quality of feed and precision indices, overestimated miss index and could not provide processed data range needed for computing multiple index due to low image resolution, weeds presence within crop rows and overlapping of leaves. crop residue cover (crc) multiple index miss index quality of feed index precision index Agriculture (General) Engineering (General). Civil engineering (General) M. H Raoufat verfasserin aut In Journal of Agricultural Machinery Ferdowsi University of Mashhad, 2016 10(2020), 1, Seite 23-35 (DE-627)1749281643 (DE-600)3054989-9 24233943 nnns volume:10 year:2020 number:1 pages:23-35 https://doi.org/10.22067/jam.v10i1.71498 kostenfrei https://doaj.org/article/aba1e63233c246128add715c281d8ac8 kostenfrei https://jame.um.ac.ir/article_34006_38b58fd10f9f04f7e39fa8ecff4d9c7b.pdf kostenfrei https://doaj.org/toc/2228-6829 Journal toc kostenfrei https://doaj.org/toc/2423-3943 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 1 23-35
allfields_unstemmed	10.22067/jam.v10i1.71498 doi (DE-627)DOAJ075183242 (DE-599)DOAJaba1e63233c246128add715c281d8ac8 DE-627 ger DE-627 rakwb eng per S1-972 TA1-2040 Z Kavoosi verfasserin aut Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated by ground and air observations. The purpose of this study was to investigate the capability of aerial images captured by an unmanned aerial vehicle (UAV) in identifying the distances between corn seedlings and as a result, assessing the quality of planter performance. Collected data from ground and aerial imagery were used to calculate seed establishment indices including multiple index, miss index, quality of feed index, precision index and also emergence rate index (ERI), for each plot. Images captured from10 m altitude (4.5 mm pixel-1) could give satisfactory results in relation to our objectives. Our results show that acceptable correlations existed between terrestrial and aerial seedlings spacing data sets (0.94<R<0.98) suggesting the aerial imagery is a good choice for evaluating the seed establishment and estimating ERI. Aerial imagery data source underestimated quality of feed and precision indices, overestimated miss index and could not provide processed data range needed for computing multiple index due to low image resolution, weeds presence within crop rows and overlapping of leaves. crop residue cover (crc) multiple index miss index quality of feed index precision index Agriculture (General) Engineering (General). Civil engineering (General) M. H Raoufat verfasserin aut In Journal of Agricultural Machinery Ferdowsi University of Mashhad, 2016 10(2020), 1, Seite 23-35 (DE-627)1749281643 (DE-600)3054989-9 24233943 nnns volume:10 year:2020 number:1 pages:23-35 https://doi.org/10.22067/jam.v10i1.71498 kostenfrei https://doaj.org/article/aba1e63233c246128add715c281d8ac8 kostenfrei https://jame.um.ac.ir/article_34006_38b58fd10f9f04f7e39fa8ecff4d9c7b.pdf kostenfrei https://doaj.org/toc/2228-6829 Journal toc kostenfrei https://doaj.org/toc/2423-3943 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 1 23-35
allfieldsGer	10.22067/jam.v10i1.71498 doi (DE-627)DOAJ075183242 (DE-599)DOAJaba1e63233c246128add715c281d8ac8 DE-627 ger DE-627 rakwb eng per S1-972 TA1-2040 Z Kavoosi verfasserin aut Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated by ground and air observations. The purpose of this study was to investigate the capability of aerial images captured by an unmanned aerial vehicle (UAV) in identifying the distances between corn seedlings and as a result, assessing the quality of planter performance. Collected data from ground and aerial imagery were used to calculate seed establishment indices including multiple index, miss index, quality of feed index, precision index and also emergence rate index (ERI), for each plot. Images captured from10 m altitude (4.5 mm pixel-1) could give satisfactory results in relation to our objectives. Our results show that acceptable correlations existed between terrestrial and aerial seedlings spacing data sets (0.94<R<0.98) suggesting the aerial imagery is a good choice for evaluating the seed establishment and estimating ERI. Aerial imagery data source underestimated quality of feed and precision indices, overestimated miss index and could not provide processed data range needed for computing multiple index due to low image resolution, weeds presence within crop rows and overlapping of leaves. crop residue cover (crc) multiple index miss index quality of feed index precision index Agriculture (General) Engineering (General). Civil engineering (General) M. H Raoufat verfasserin aut In Journal of Agricultural Machinery Ferdowsi University of Mashhad, 2016 10(2020), 1, Seite 23-35 (DE-627)1749281643 (DE-600)3054989-9 24233943 nnns volume:10 year:2020 number:1 pages:23-35 https://doi.org/10.22067/jam.v10i1.71498 kostenfrei https://doaj.org/article/aba1e63233c246128add715c281d8ac8 kostenfrei https://jame.um.ac.ir/article_34006_38b58fd10f9f04f7e39fa8ecff4d9c7b.pdf kostenfrei https://doaj.org/toc/2228-6829 Journal toc kostenfrei https://doaj.org/toc/2423-3943 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 1 23-35
allfieldsSound	10.22067/jam.v10i1.71498 doi (DE-627)DOAJ075183242 (DE-599)DOAJaba1e63233c246128add715c281d8ac8 DE-627 ger DE-627 rakwb eng per S1-972 TA1-2040 Z Kavoosi verfasserin aut Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated by ground and air observations. The purpose of this study was to investigate the capability of aerial images captured by an unmanned aerial vehicle (UAV) in identifying the distances between corn seedlings and as a result, assessing the quality of planter performance. Collected data from ground and aerial imagery were used to calculate seed establishment indices including multiple index, miss index, quality of feed index, precision index and also emergence rate index (ERI), for each plot. Images captured from10 m altitude (4.5 mm pixel-1) could give satisfactory results in relation to our objectives. Our results show that acceptable correlations existed between terrestrial and aerial seedlings spacing data sets (0.94<R<0.98) suggesting the aerial imagery is a good choice for evaluating the seed establishment and estimating ERI. Aerial imagery data source underestimated quality of feed and precision indices, overestimated miss index and could not provide processed data range needed for computing multiple index due to low image resolution, weeds presence within crop rows and overlapping of leaves. crop residue cover (crc) multiple index miss index quality of feed index precision index Agriculture (General) Engineering (General). Civil engineering (General) M. H Raoufat verfasserin aut In Journal of Agricultural Machinery Ferdowsi University of Mashhad, 2016 10(2020), 1, Seite 23-35 (DE-627)1749281643 (DE-600)3054989-9 24233943 nnns volume:10 year:2020 number:1 pages:23-35 https://doi.org/10.22067/jam.v10i1.71498 kostenfrei https://doaj.org/article/aba1e63233c246128add715c281d8ac8 kostenfrei https://jame.um.ac.ir/article_34006_38b58fd10f9f04f7e39fa8ecff4d9c7b.pdf kostenfrei https://doaj.org/toc/2228-6829 Journal toc kostenfrei https://doaj.org/toc/2423-3943 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2020 1 23-35
language	English Persian
source	In Journal of Agricultural Machinery 10(2020), 1, Seite 23-35 volume:10 year:2020 number:1 pages:23-35
sourceStr	In Journal of Agricultural Machinery 10(2020), 1, Seite 23-35 volume:10 year:2020 number:1 pages:23-35
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	crop residue cover (crc) multiple index miss index quality of feed index precision index Agriculture (General) Engineering (General). Civil engineering (General)
isfreeaccess_bool	true
container_title	Journal of Agricultural Machinery
authorswithroles_txt_mv	Z Kavoosi @@aut@@ M. H Raoufat @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	1749281643
id	DOAJ075183242
language_de	englisch persisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ075183242</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230501190047.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.22067/jam.v10i1.71498</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ075183242</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJaba1e63233c246128add715c281d8ac8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield><subfield code="a">per</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">S1-972</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA1-2040</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Z Kavoosi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated by ground and air observations. The purpose of this study was to investigate the capability of aerial images captured by an unmanned aerial vehicle (UAV) in identifying the distances between corn seedlings and as a result, assessing the quality of planter performance. Collected data from ground and aerial imagery were used to calculate seed establishment indices including multiple index, miss index, quality of feed index, precision index and also emergence rate index (ERI), for each plot. Images captured from10 m altitude (4.5 mm pixel-1) could give satisfactory results in relation to our objectives. Our results show that acceptable correlations existed between terrestrial and aerial seedlings spacing data sets (0.94<R<0.98) suggesting the aerial imagery is a good choice for evaluating the seed establishment and estimating ERI. Aerial imagery data source underestimated quality of feed and precision indices, overestimated miss index and could not provide processed data range needed for computing multiple index due to low image resolution, weeds presence within crop rows and overlapping of leaves.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">crop residue cover (crc)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multiple index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">miss index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">quality of feed index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">precision index</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Agriculture (General)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering (General). Civil engineering (General)</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">M. H Raoufat</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Agricultural Machinery</subfield><subfield code="d">Ferdowsi University of Mashhad, 2016</subfield><subfield code="g">10(2020), 1, Seite 23-35</subfield><subfield code="w">(DE-627)1749281643</subfield><subfield code="w">(DE-600)3054989-9</subfield><subfield code="x">24233943</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:23-35</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.22067/jam.v10i1.71498</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/aba1e63233c246128add715c281d8ac8</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://jame.um.ac.ir/article_34006_38b58fd10f9f04f7e39fa8ecff4d9c7b.pdf</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2228-6829</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2423-3943</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="h">23-35</subfield></datafield></record></collection>
callnumber-first	S - Agriculture
author	Z Kavoosi
spellingShingle	Z Kavoosi misc S1-972 misc TA1-2040 misc crop residue cover (crc) misc multiple index misc miss index misc quality of feed index misc precision index misc Agriculture (General) misc Engineering (General). Civil engineering (General) Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System
authorStr	Z Kavoosi
ppnlink_with_tag_str_mv	@@773@@(DE-627)1749281643
format	electronic Article
delete_txt_mv	keep
author_role	aut aut
collection	DOAJ
remote_str	true
callnumber-label	S1-972
illustrated	Not Illustrated
issn	24233943
topic_title	S1-972 TA1-2040 Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System crop residue cover (crc) multiple index miss index quality of feed index precision index
topic	misc S1-972 misc TA1-2040 misc crop residue cover (crc) misc multiple index misc miss index misc quality of feed index misc precision index misc Agriculture (General) misc Engineering (General). Civil engineering (General)
topic_unstemmed	misc S1-972 misc TA1-2040 misc crop residue cover (crc) misc multiple index misc miss index misc quality of feed index misc precision index misc Agriculture (General) misc Engineering (General). Civil engineering (General)
topic_browse	misc S1-972 misc TA1-2040 misc crop residue cover (crc) misc multiple index misc miss index misc quality of feed index misc precision index misc Agriculture (General) misc Engineering (General). Civil engineering (General)
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Journal of Agricultural Machinery
hierarchy_parent_id	1749281643
hierarchy_top_title	Journal of Agricultural Machinery
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)1749281643 (DE-600)3054989-9
title	Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System
ctrlnum	(DE-627)DOAJ075183242 (DE-599)DOAJaba1e63233c246128add715c281d8ac8
title_full	Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System
author_sort	Z Kavoosi
journal	Journal of Agricultural Machinery
journalStr	Journal of Agricultural Machinery
callnumber-first-code	S
lang_code	eng per
isOA_bool	true
recordtype	marc
publishDateSort	2020
contenttype_str_mv	txt
container_start_page	23
author_browse	Z Kavoosi M. H Raoufat
container_volume	10
class	S1-972 TA1-2040
format_se	Elektronische Aufsätze
author-letter	Z Kavoosi
doi_str_mv	10.22067/jam.v10i1.71498
author2-role	verfasserin
title_sort	feasibility of drone imagery for monitoring performance of a modified drill in a conservation farming system
callnumber	S1-972
title_auth	Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System
abstract	In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated by ground and air observations. The purpose of this study was to investigate the capability of aerial images captured by an unmanned aerial vehicle (UAV) in identifying the distances between corn seedlings and as a result, assessing the quality of planter performance. Collected data from ground and aerial imagery were used to calculate seed establishment indices including multiple index, miss index, quality of feed index, precision index and also emergence rate index (ERI), for each plot. Images captured from10 m altitude (4.5 mm pixel-1) could give satisfactory results in relation to our objectives. Our results show that acceptable correlations existed between terrestrial and aerial seedlings spacing data sets (0.94<R<0.98) suggesting the aerial imagery is a good choice for evaluating the seed establishment and estimating ERI. Aerial imagery data source underestimated quality of feed and precision indices, overestimated miss index and could not provide processed data range needed for computing multiple index due to low image resolution, weeds presence within crop rows and overlapping of leaves.
abstractGer	In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated by ground and air observations. The purpose of this study was to investigate the capability of aerial images captured by an unmanned aerial vehicle (UAV) in identifying the distances between corn seedlings and as a result, assessing the quality of planter performance. Collected data from ground and aerial imagery were used to calculate seed establishment indices including multiple index, miss index, quality of feed index, precision index and also emergence rate index (ERI), for each plot. Images captured from10 m altitude (4.5 mm pixel-1) could give satisfactory results in relation to our objectives. Our results show that acceptable correlations existed between terrestrial and aerial seedlings spacing data sets (0.94<R<0.98) suggesting the aerial imagery is a good choice for evaluating the seed establishment and estimating ERI. Aerial imagery data source underestimated quality of feed and precision indices, overestimated miss index and could not provide processed data range needed for computing multiple index due to low image resolution, weeds presence within crop rows and overlapping of leaves.
abstract_unstemmed	In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated by ground and air observations. The purpose of this study was to investigate the capability of aerial images captured by an unmanned aerial vehicle (UAV) in identifying the distances between corn seedlings and as a result, assessing the quality of planter performance. Collected data from ground and aerial imagery were used to calculate seed establishment indices including multiple index, miss index, quality of feed index, precision index and also emergence rate index (ERI), for each plot. Images captured from10 m altitude (4.5 mm pixel-1) could give satisfactory results in relation to our objectives. Our results show that acceptable correlations existed between terrestrial and aerial seedlings spacing data sets (0.94<R<0.98) suggesting the aerial imagery is a good choice for evaluating the seed establishment and estimating ERI. Aerial imagery data source underestimated quality of feed and precision indices, overestimated miss index and could not provide processed data range needed for computing multiple index due to low image resolution, weeds presence within crop rows and overlapping of leaves.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	1
title_short	Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System
url	https://doi.org/10.22067/jam.v10i1.71498 https://doaj.org/article/aba1e63233c246128add715c281d8ac8 https://jame.um.ac.ir/article_34006_38b58fd10f9f04f7e39fa8ecff4d9c7b.pdf https://doaj.org/toc/2228-6829 https://doaj.org/toc/2423-3943
remote_bool	true
author2	M. H Raoufat
author2Str	M. H Raoufat
ppnlink	1749281643
callnumber-subject	S - General Agriculture
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.22067/jam.v10i1.71498
callnumber-a	S1-972
up_date	2024-07-03T13:28:35.518Z
_version_	1803564682343088128
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ075183242</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230501190047.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230228s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.22067/jam.v10i1.71498</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ075183242</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJaba1e63233c246128add715c281d8ac8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield><subfield code="a">per</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">S1-972</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TA1-2040</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Z Kavoosi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this paper, performance of a no-till corn planter in a soil covered with previous wheat residue was evaluated. Three levels of crop residue cover (CRC): 30, 45 and 60%, two planting schemes; on-bed and in-furrow and two forward speed: (4 and 8 km h-1) were considered as treatments. The field was evaluated by ground and air observations. The purpose of this study was to investigate the capability of aerial images captured by an unmanned aerial vehicle (UAV) in identifying the distances between corn seedlings and as a result, assessing the quality of planter performance. Collected data from ground and aerial imagery were used to calculate seed establishment indices including multiple index, miss index, quality of feed index, precision index and also emergence rate index (ERI), for each plot. Images captured from10 m altitude (4.5 mm pixel-1) could give satisfactory results in relation to our objectives. Our results show that acceptable correlations existed between terrestrial and aerial seedlings spacing data sets (0.94<R<0.98) suggesting the aerial imagery is a good choice for evaluating the seed establishment and estimating ERI. Aerial imagery data source underestimated quality of feed and precision indices, overestimated miss index and could not provide processed data range needed for computing multiple index due to low image resolution, weeds presence within crop rows and overlapping of leaves.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">crop residue cover (crc)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multiple index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">miss index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">quality of feed index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">precision index</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Agriculture (General)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Engineering (General). Civil engineering (General)</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">M. H Raoufat</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Agricultural Machinery</subfield><subfield code="d">Ferdowsi University of Mashhad, 2016</subfield><subfield code="g">10(2020), 1, Seite 23-35</subfield><subfield code="w">(DE-627)1749281643</subfield><subfield code="w">(DE-600)3054989-9</subfield><subfield code="x">24233943</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:23-35</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.22067/jam.v10i1.71498</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/aba1e63233c246128add715c281d8ac8</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://jame.um.ac.ir/article_34006_38b58fd10f9f04f7e39fa8ecff4d9c7b.pdf</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2228-6829</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/2423-3943</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2020</subfield><subfield code="e">1</subfield><subfield code="h">23-35</subfield></datafield></record></collection>
score	7.4009523

Nicht das Richtige dabei?

Schreiben Sie uns!

Feasibility of Drone Imagery for Monitoring Performance of a Modified Drill in a Conservation Farming System

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?