Yield mapping methods for manually harvested crops

Lack of yield mapping solutions is currently a bottleneck for Precision Agriculture development and adoption in many manually harvested fruit and vegetable crops. In such systems, the handpicked produce is briefly stored in bags or boxes across the field before they are loaded and transported. This...
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Gespeichert in:

Autor*in:	Colaço, A.F. [verfasserIn] Trevisan, R.G. [verfasserIn] Karp, F.H.S. [verfasserIn] Molin, J.P. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Yield spatial variability Yield map Site-specific management Fruit crops

Übergeordnetes Werk:	Enthalten in: Computers and electronics in agriculture - Amsterdam [u.a.] : Elsevier Science, 1985, 177
Übergeordnetes Werk:	volume:177

DOI / URN:	10.1016/j.compag.2020.105693

Katalog-ID:	ELV00461139X

Internformat


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520			\|a Lack of yield mapping solutions is currently a bottleneck for Precision Agriculture development and adoption in many manually harvested fruit and vegetable crops. In such systems, the handpicked produce is briefly stored in bags or boxes across the field before they are loaded and transported. This study tested a simple yield mapping method based on georeferencing the bags used during harvest with local yield calculated based on the distribution of these points across the field. Virtual yield maps and real field data were used to validate different data processing methods under different scenarios; scenarios included different levels of yield spatial variability and bag positioning and mass errors. Method 1 calculated yield at each bag point by estimating the area needed to fill it; such area was based on the bag distance to its neighbours. Method 2 calculated local yield based on the distribution of bags across an area using a moving window approach. In normal field situations – with bag positioning and mass errors below 1 m and 5% – the approaches had similar performance with accuracy levels varying between 5 and 11 Mg ha−1, depending on the yield spatial variability. With increasing bag positioning error, method 2 outperformed method 1. Both approaches were little affected by error in bag mass estimation. Overall, the yield mapping methods are useful in supporting most applications in Precision Agriculture and can be easily implemented in a software tool to promote user adoption and site-specific management.
650		4	\|a Yield spatial variability
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700	1		\|a Molin, J.P. \|e verfasserin \|4 aut
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936	b	k	\|a 48.03 \|j Methoden und Techniken der Land- und Forstwirtschaft
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Indexfelder

author_variant	a c ac r t rt f k fk j m jm
matchkey_str	article:18727107:2020----::ilmpigehdfraulya
hierarchy_sort_str	2020
bklnumber	48.03
publishDate	2020
allfields	10.1016/j.compag.2020.105693 doi (DE-627)ELV00461139X (ELSEVIER)S0168-1699(20)31545-3 DE-627 ger DE-627 rda eng 620 630 640 004 DE-600 48.03 bkl Colaço, A.F. verfasserin aut Yield mapping methods for manually harvested crops 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lack of yield mapping solutions is currently a bottleneck for Precision Agriculture development and adoption in many manually harvested fruit and vegetable crops. In such systems, the handpicked produce is briefly stored in bags or boxes across the field before they are loaded and transported. This study tested a simple yield mapping method based on georeferencing the bags used during harvest with local yield calculated based on the distribution of these points across the field. Virtual yield maps and real field data were used to validate different data processing methods under different scenarios; scenarios included different levels of yield spatial variability and bag positioning and mass errors. Method 1 calculated yield at each bag point by estimating the area needed to fill it; such area was based on the bag distance to its neighbours. Method 2 calculated local yield based on the distribution of bags across an area using a moving window approach. In normal field situations – with bag positioning and mass errors below 1 m and 5% – the approaches had similar performance with accuracy levels varying between 5 and 11 Mg ha−1, depending on the yield spatial variability. With increasing bag positioning error, method 2 outperformed method 1. Both approaches were little affected by error in bag mass estimation. Overall, the yield mapping methods are useful in supporting most applications in Precision Agriculture and can be easily implemented in a software tool to promote user adoption and site-specific management. Yield spatial variability Yield map Site-specific management Fruit crops Trevisan, R.G. verfasserin aut Karp, F.H.S. verfasserin aut Molin, J.P. verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 177 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:177 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.03 Methoden und Techniken der Land- und Forstwirtschaft AR 177
spelling	10.1016/j.compag.2020.105693 doi (DE-627)ELV00461139X (ELSEVIER)S0168-1699(20)31545-3 DE-627 ger DE-627 rda eng 620 630 640 004 DE-600 48.03 bkl Colaço, A.F. verfasserin aut Yield mapping methods for manually harvested crops 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lack of yield mapping solutions is currently a bottleneck for Precision Agriculture development and adoption in many manually harvested fruit and vegetable crops. In such systems, the handpicked produce is briefly stored in bags or boxes across the field before they are loaded and transported. This study tested a simple yield mapping method based on georeferencing the bags used during harvest with local yield calculated based on the distribution of these points across the field. Virtual yield maps and real field data were used to validate different data processing methods under different scenarios; scenarios included different levels of yield spatial variability and bag positioning and mass errors. Method 1 calculated yield at each bag point by estimating the area needed to fill it; such area was based on the bag distance to its neighbours. Method 2 calculated local yield based on the distribution of bags across an area using a moving window approach. In normal field situations – with bag positioning and mass errors below 1 m and 5% – the approaches had similar performance with accuracy levels varying between 5 and 11 Mg ha−1, depending on the yield spatial variability. With increasing bag positioning error, method 2 outperformed method 1. Both approaches were little affected by error in bag mass estimation. Overall, the yield mapping methods are useful in supporting most applications in Precision Agriculture and can be easily implemented in a software tool to promote user adoption and site-specific management. Yield spatial variability Yield map Site-specific management Fruit crops Trevisan, R.G. verfasserin aut Karp, F.H.S. verfasserin aut Molin, J.P. verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 177 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:177 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.03 Methoden und Techniken der Land- und Forstwirtschaft AR 177
allfields_unstemmed	10.1016/j.compag.2020.105693 doi (DE-627)ELV00461139X (ELSEVIER)S0168-1699(20)31545-3 DE-627 ger DE-627 rda eng 620 630 640 004 DE-600 48.03 bkl Colaço, A.F. verfasserin aut Yield mapping methods for manually harvested crops 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lack of yield mapping solutions is currently a bottleneck for Precision Agriculture development and adoption in many manually harvested fruit and vegetable crops. In such systems, the handpicked produce is briefly stored in bags or boxes across the field before they are loaded and transported. This study tested a simple yield mapping method based on georeferencing the bags used during harvest with local yield calculated based on the distribution of these points across the field. Virtual yield maps and real field data were used to validate different data processing methods under different scenarios; scenarios included different levels of yield spatial variability and bag positioning and mass errors. Method 1 calculated yield at each bag point by estimating the area needed to fill it; such area was based on the bag distance to its neighbours. Method 2 calculated local yield based on the distribution of bags across an area using a moving window approach. In normal field situations – with bag positioning and mass errors below 1 m and 5% – the approaches had similar performance with accuracy levels varying between 5 and 11 Mg ha−1, depending on the yield spatial variability. With increasing bag positioning error, method 2 outperformed method 1. Both approaches were little affected by error in bag mass estimation. Overall, the yield mapping methods are useful in supporting most applications in Precision Agriculture and can be easily implemented in a software tool to promote user adoption and site-specific management. Yield spatial variability Yield map Site-specific management Fruit crops Trevisan, R.G. verfasserin aut Karp, F.H.S. verfasserin aut Molin, J.P. verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 177 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:177 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.03 Methoden und Techniken der Land- und Forstwirtschaft AR 177
allfieldsGer	10.1016/j.compag.2020.105693 doi (DE-627)ELV00461139X (ELSEVIER)S0168-1699(20)31545-3 DE-627 ger DE-627 rda eng 620 630 640 004 DE-600 48.03 bkl Colaço, A.F. verfasserin aut Yield mapping methods for manually harvested crops 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lack of yield mapping solutions is currently a bottleneck for Precision Agriculture development and adoption in many manually harvested fruit and vegetable crops. In such systems, the handpicked produce is briefly stored in bags or boxes across the field before they are loaded and transported. This study tested a simple yield mapping method based on georeferencing the bags used during harvest with local yield calculated based on the distribution of these points across the field. Virtual yield maps and real field data were used to validate different data processing methods under different scenarios; scenarios included different levels of yield spatial variability and bag positioning and mass errors. Method 1 calculated yield at each bag point by estimating the area needed to fill it; such area was based on the bag distance to its neighbours. Method 2 calculated local yield based on the distribution of bags across an area using a moving window approach. In normal field situations – with bag positioning and mass errors below 1 m and 5% – the approaches had similar performance with accuracy levels varying between 5 and 11 Mg ha−1, depending on the yield spatial variability. With increasing bag positioning error, method 2 outperformed method 1. Both approaches were little affected by error in bag mass estimation. Overall, the yield mapping methods are useful in supporting most applications in Precision Agriculture and can be easily implemented in a software tool to promote user adoption and site-specific management. Yield spatial variability Yield map Site-specific management Fruit crops Trevisan, R.G. verfasserin aut Karp, F.H.S. verfasserin aut Molin, J.P. verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 177 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:177 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.03 Methoden und Techniken der Land- und Forstwirtschaft AR 177
allfieldsSound	10.1016/j.compag.2020.105693 doi (DE-627)ELV00461139X (ELSEVIER)S0168-1699(20)31545-3 DE-627 ger DE-627 rda eng 620 630 640 004 DE-600 48.03 bkl Colaço, A.F. verfasserin aut Yield mapping methods for manually harvested crops 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lack of yield mapping solutions is currently a bottleneck for Precision Agriculture development and adoption in many manually harvested fruit and vegetable crops. In such systems, the handpicked produce is briefly stored in bags or boxes across the field before they are loaded and transported. This study tested a simple yield mapping method based on georeferencing the bags used during harvest with local yield calculated based on the distribution of these points across the field. Virtual yield maps and real field data were used to validate different data processing methods under different scenarios; scenarios included different levels of yield spatial variability and bag positioning and mass errors. Method 1 calculated yield at each bag point by estimating the area needed to fill it; such area was based on the bag distance to its neighbours. Method 2 calculated local yield based on the distribution of bags across an area using a moving window approach. In normal field situations – with bag positioning and mass errors below 1 m and 5% – the approaches had similar performance with accuracy levels varying between 5 and 11 Mg ha−1, depending on the yield spatial variability. With increasing bag positioning error, method 2 outperformed method 1. Both approaches were little affected by error in bag mass estimation. Overall, the yield mapping methods are useful in supporting most applications in Precision Agriculture and can be easily implemented in a software tool to promote user adoption and site-specific management. Yield spatial variability Yield map Site-specific management Fruit crops Trevisan, R.G. verfasserin aut Karp, F.H.S. verfasserin aut Molin, J.P. verfasserin aut Enthalten in Computers and electronics in agriculture Amsterdam [u.a.] : Elsevier Science, 1985 177 Online-Ressource (DE-627)320567826 (DE-600)2016151-7 (DE-576)090955684 1872-7107 nnns volume:177 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-FOR GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.03 Methoden und Techniken der Land- und Forstwirtschaft AR 177
language	English
source	Enthalten in Computers and electronics in agriculture 177 volume:177
sourceStr	Enthalten in Computers and electronics in agriculture 177 volume:177
format_phy_str_mv	Article
bklname	Methoden und Techniken der Land- und Forstwirtschaft
institution	findex.gbv.de
topic_facet	Yield spatial variability Yield map Site-specific management Fruit crops
dewey-raw	620
isfreeaccess_bool	false
container_title	Computers and electronics in agriculture
authorswithroles_txt_mv	Colaço, A.F. @@aut@@ Trevisan, R.G. @@aut@@ Karp, F.H.S. @@aut@@ Molin, J.P. @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	320567826
dewey-sort	3620
id	ELV00461139X
language_de	englisch
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author	Colaço, A.F.
spellingShingle	Colaço, A.F. ddc 620 bkl 48.03 misc Yield spatial variability misc Yield map misc Site-specific management misc Fruit crops Yield mapping methods for manually harvested crops
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topic_title	620 630 640 004 DE-600 48.03 bkl Yield mapping methods for manually harvested crops Yield spatial variability Yield map Site-specific management Fruit crops
topic	ddc 620 bkl 48.03 misc Yield spatial variability misc Yield map misc Site-specific management misc Fruit crops
topic_unstemmed	ddc 620 bkl 48.03 misc Yield spatial variability misc Yield map misc Site-specific management misc Fruit crops
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title	Yield mapping methods for manually harvested crops
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title_full	Yield mapping methods for manually harvested crops
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title_sort	yield mapping methods for manually harvested crops
title_auth	Yield mapping methods for manually harvested crops
abstract	Lack of yield mapping solutions is currently a bottleneck for Precision Agriculture development and adoption in many manually harvested fruit and vegetable crops. In such systems, the handpicked produce is briefly stored in bags or boxes across the field before they are loaded and transported. This study tested a simple yield mapping method based on georeferencing the bags used during harvest with local yield calculated based on the distribution of these points across the field. Virtual yield maps and real field data were used to validate different data processing methods under different scenarios; scenarios included different levels of yield spatial variability and bag positioning and mass errors. Method 1 calculated yield at each bag point by estimating the area needed to fill it; such area was based on the bag distance to its neighbours. Method 2 calculated local yield based on the distribution of bags across an area using a moving window approach. In normal field situations – with bag positioning and mass errors below 1 m and 5% – the approaches had similar performance with accuracy levels varying between 5 and 11 Mg ha−1, depending on the yield spatial variability. With increasing bag positioning error, method 2 outperformed method 1. Both approaches were little affected by error in bag mass estimation. Overall, the yield mapping methods are useful in supporting most applications in Precision Agriculture and can be easily implemented in a software tool to promote user adoption and site-specific management.
abstractGer	Lack of yield mapping solutions is currently a bottleneck for Precision Agriculture development and adoption in many manually harvested fruit and vegetable crops. In such systems, the handpicked produce is briefly stored in bags or boxes across the field before they are loaded and transported. This study tested a simple yield mapping method based on georeferencing the bags used during harvest with local yield calculated based on the distribution of these points across the field. Virtual yield maps and real field data were used to validate different data processing methods under different scenarios; scenarios included different levels of yield spatial variability and bag positioning and mass errors. Method 1 calculated yield at each bag point by estimating the area needed to fill it; such area was based on the bag distance to its neighbours. Method 2 calculated local yield based on the distribution of bags across an area using a moving window approach. In normal field situations – with bag positioning and mass errors below 1 m and 5% – the approaches had similar performance with accuracy levels varying between 5 and 11 Mg ha−1, depending on the yield spatial variability. With increasing bag positioning error, method 2 outperformed method 1. Both approaches were little affected by error in bag mass estimation. Overall, the yield mapping methods are useful in supporting most applications in Precision Agriculture and can be easily implemented in a software tool to promote user adoption and site-specific management.
abstract_unstemmed	Lack of yield mapping solutions is currently a bottleneck for Precision Agriculture development and adoption in many manually harvested fruit and vegetable crops. In such systems, the handpicked produce is briefly stored in bags or boxes across the field before they are loaded and transported. This study tested a simple yield mapping method based on georeferencing the bags used during harvest with local yield calculated based on the distribution of these points across the field. Virtual yield maps and real field data were used to validate different data processing methods under different scenarios; scenarios included different levels of yield spatial variability and bag positioning and mass errors. Method 1 calculated yield at each bag point by estimating the area needed to fill it; such area was based on the bag distance to its neighbours. Method 2 calculated local yield based on the distribution of bags across an area using a moving window approach. In normal field situations – with bag positioning and mass errors below 1 m and 5% – the approaches had similar performance with accuracy levels varying between 5 and 11 Mg ha−1, depending on the yield spatial variability. With increasing bag positioning error, method 2 outperformed method 1. Both approaches were little affected by error in bag mass estimation. Overall, the yield mapping methods are useful in supporting most applications in Precision Agriculture and can be easily implemented in a software tool to promote user adoption and site-specific management.
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title_short	Yield mapping methods for manually harvested crops
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author2	Trevisan, R.G. Karp, F.H.S. Molin, J.P.
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doi_str	10.1016/j.compag.2020.105693
up_date	2024-07-06T23:34:28.289Z
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Yield mapping methods for manually harvested crops

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