Technologies and Data Analytics to Manage Grain Quality On-Farm—A Review
Grains intended for human consumption or feedstock are typically high-value commodities that are marketed based on either their visual characteristics or compositional properties. The combination of visual traits, chemical composition and contaminants is generally referred to as grain quality. Curre...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/13/4/1129 |
| _version_ | 1797606787370713088 |
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| author | Cassandra K. Walker Sahand Assadzadeh Ashley J. Wallace Audrey J. Delahunty Alexander B. Clancy Linda S. McDonald Glenn J. Fitzgerald James G. Nuttall Joe F. Panozzo |
| author_facet | Cassandra K. Walker Sahand Assadzadeh Ashley J. Wallace Audrey J. Delahunty Alexander B. Clancy Linda S. McDonald Glenn J. Fitzgerald James G. Nuttall Joe F. Panozzo |
| author_sort | Cassandra K. Walker |
| collection | DOAJ |
| description | Grains intended for human consumption or feedstock are typically high-value commodities that are marketed based on either their visual characteristics or compositional properties. The combination of visual traits, chemical composition and contaminants is generally referred to as grain quality. Currently, the market value of grain is quantified at the point of receival, using trading standards defined in terms of visual criteria of the bulk grain and chemical constituency. The risk for the grower is that grain prices can fluctuate throughout the year depending on world production, quality variation and market needs. The assessment of grain quality and market value on-farm, rather than post-farm gate, may identify high- and low-quality grain and inform a fair price for growers. The economic benefits include delivering grain that meets specifications maximizing the aggregate price, increasing traceability across the supply chain from grower to consumer and identifying greater suitability of differentiated products for high-value niche markets, such as high protein product ideal for plant-based proteins. This review focuses on developments that quantify grain quality with a range of spectral sensors in an on-farm setting. If the application of sensor technologies were expanded and adopted on-farm, growers could identify the impact and manage the harvesting operation to meet a range of quality targets and provide an economic advantage to the farming enterprise. |
| first_indexed | 2024-03-11T05:20:02Z |
| format | Article |
| id | doaj.art-88e765e19fb447859b729f5e74014f82 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-11T05:20:02Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-88e765e19fb447859b729f5e74014f822023-11-17T17:58:01ZengMDPI AGAgronomy2073-43952023-04-01134112910.3390/agronomy13041129Technologies and Data Analytics to Manage Grain Quality On-Farm—A ReviewCassandra K. Walker0Sahand Assadzadeh1Ashley J. Wallace2Audrey J. Delahunty3Alexander B. Clancy4Linda S. McDonald5Glenn J. Fitzgerald6James G. Nuttall7Joe F. Panozzo8Agriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, AustraliaAgriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, AustraliaAgriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, AustraliaAgriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, AustraliaAgriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, AustraliaAgriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, AustraliaAgriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, AustraliaAgriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, AustraliaAgriculture Victoria Research, Grains Innovation Park, Horsham, VIC 3400, AustraliaGrains intended for human consumption or feedstock are typically high-value commodities that are marketed based on either their visual characteristics or compositional properties. The combination of visual traits, chemical composition and contaminants is generally referred to as grain quality. Currently, the market value of grain is quantified at the point of receival, using trading standards defined in terms of visual criteria of the bulk grain and chemical constituency. The risk for the grower is that grain prices can fluctuate throughout the year depending on world production, quality variation and market needs. The assessment of grain quality and market value on-farm, rather than post-farm gate, may identify high- and low-quality grain and inform a fair price for growers. The economic benefits include delivering grain that meets specifications maximizing the aggregate price, increasing traceability across the supply chain from grower to consumer and identifying greater suitability of differentiated products for high-value niche markets, such as high protein product ideal for plant-based proteins. This review focuses on developments that quantify grain quality with a range of spectral sensors in an on-farm setting. If the application of sensor technologies were expanded and adopted on-farm, growers could identify the impact and manage the harvesting operation to meet a range of quality targets and provide an economic advantage to the farming enterprise.https://www.mdpi.com/2073-4395/13/4/1129in-fieldspectroscopyimage analysismachine learningproteingrain size |
| spellingShingle | Cassandra K. Walker Sahand Assadzadeh Ashley J. Wallace Audrey J. Delahunty Alexander B. Clancy Linda S. McDonald Glenn J. Fitzgerald James G. Nuttall Joe F. Panozzo Technologies and Data Analytics to Manage Grain Quality On-Farm—A Review Agronomy in-field spectroscopy image analysis machine learning protein grain size |
| title | Technologies and Data Analytics to Manage Grain Quality On-Farm—A Review |
| title_full | Technologies and Data Analytics to Manage Grain Quality On-Farm—A Review |
| title_fullStr | Technologies and Data Analytics to Manage Grain Quality On-Farm—A Review |
| title_full_unstemmed | Technologies and Data Analytics to Manage Grain Quality On-Farm—A Review |
| title_short | Technologies and Data Analytics to Manage Grain Quality On-Farm—A Review |
| title_sort | technologies and data analytics to manage grain quality on farm a review |
| topic | in-field spectroscopy image analysis machine learning protein grain size |
| url | https://www.mdpi.com/2073-4395/13/4/1129 |
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