{"id":922,"date":"2020-06-01T15:00:55","date_gmt":"2020-06-01T18:00:55","guid":{"rendered":"http:\/\/pressreleases.scielo.org\/en\/?p=922"},"modified":"2023-03-28T13:37:07","modified_gmt":"2023-03-28T16:37:07","slug":"innovative-method-measures-chlorophyll-content-in-millet-leaves","status":"publish","type":"post","link":"https:\/\/pressreleases.scielo.org\/en\/2020\/06\/01\/innovative-method-measures-chlorophyll-content-in-millet-leaves\/","title":{"rendered":"Innovative method measures chlorophyll content in millet leaves"},"content":{"rendered":"

By Maria Luiza De Grandi, journalist of Ci\u00eancia Rural, Santa Maria, Rio Grande do Sul, Brazil and\u00a0Xiaoyan Wang, Shanxi Agricultural University, Jinzhong, China<\/strong><\/p>\n

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Chlorophyll, responsible for the green color of plants, is a vital pigment for photosynthesis in plants. This pigment is related to the nutritional status of nitrogen at different stages of plant growth and can be an essential indicator in monitoring plant nutrients. Traditional methods of measuring chlorophyll are time-consuming and damage the leaves and cannot be used over large areas. In search of an alternative, researchers at Shanxi Agricultural University in Jinzhong, China, established a model for detecting chlorophyll content for millet leaves in different growth stages, based on hyperspectral data. The results of the study were published in the article \u201cEstimation of chlorophyll content for millet leaves using hyperspectral images and an attention-convolutional neural network<\/a>\u201d was published in the Ci\u00eancia Rural<\/em> journal (vol. 50, no. 3).<\/p>\n

Currently, the chlorophyll content in plants is measured mainly by chemical methods (EVANS et al<\/em>., 2012; LOH et al<\/em>., 2012; SCOTTER, 2011), however there is an increasing use of hyperspectral imaging technology, being highly efficient, non-destructive and non-polluting. In this research, the method for millet leaves was adopted, a forage of tropical climate that has good nutritional quality when used as feed for birds, pigs and ruminants. The characteristic parameters were extracted based on spectral and image information and the correlation analysis was performed with the chlorophyll content, to select characteristic parameters.<\/p>\n

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Image: Pixabay<\/p><\/div>\n

Based on the fusion data of the parameters, the attention-CNN (a convolutional neural network), a model adopted by the researchers, produced more accurate results, becoming more effective than the compared models.<\/p>\n

According to researcher Xiaoyan Wang, based on the research results, a portable chlorophyll detection device can be designed and may be implanted in agricultural machines in the future, for online monitoring of the chlorophyll content of plants. \u201cCNN can autonomously learn and extract local resources from data, and the attention mechanism can effectively highlight important resources,\u201d he adds.<\/p>\n

References<\/h3>\n

EVANS, T., et al<\/em>. Micro-scale chlorophyll analysis and developmental expression of a cytokinin oxidase\/dehydrogenase gene during leaf development and senescence. Plant Growth Regulation<\/em> [online]. 2012, vol. 66, no. 1, pp. 95-99, e-ISSN: 0167-6903 [viewed 01 June 2020]. DOI: 10.1007\/s10725-011-9627-5<\/a>. Available from: https:\/\/link.springer.com\/article\/10.1007\/s10725-011-9627-5<\/a><\/p>\n

LOH, C.H., et al<\/em>. Determination of Chlorophylls in Taraxacum formosanum by High-Performance Liquid Chromatography\u2013Diode Array Detection\u2013Mass Spectrometry and Preparation by Column Chromatography. Journal of Agricultural and Food Chemistry <\/em>[online]. 2012, vol. 60, no. 24, pp. 6108-6115, e-ISSN: 1520-5118 [viewed 01 June 2020]. DOI: 10.1021\/jf301422m<\/a>. Available from: https:\/\/pubs.acs.org\/doi\/10.1021\/jf301422m<\/a><\/p>\n

SCOTTER, M.J. Methods for the determination of European union-permitted added natural colours in foods: a review. Food Additives and Contaminants<\/em> [online]. 2011, vol. 28, no. 5, pp. 527-596, ISSN: 0265-203X [viewed 01 June 2020]. DOI: 10.1080\/19440049.2011.555844<\/a>. Available from: https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/19440049.2011.555844<\/a><\/p>\n

To read the article, access it<\/h3>\n

XIAOYAN, W., et al<\/em>. Chlorophyll content for millet leaf using hyperspectral imaging and an attention-convolutional neural network. Cienc. Rural<\/em> [online]. 2020, vol. 50, n. 3, e20190731, ISSN: 0103-8478 [viewed 01 June 2020]. DOI: 10.1590\/0103-8478cr20190731<\/a>. Available from: http:\/\/ref.scielo.org\/3kkmry<\/a><\/p>\n

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External links<\/h3>\n

Ci\u00eancia Rural \u2013 CR: <http:\/\/www.scielo.br\/cr<\/a>><\/p>\n

Ci\u00eancia Rural <http:\/\/coral.ufsm.br\/ccr\/cienciarural\/<\/a>><\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Responsible for the green color of plants, chlorophyll is an essential indicator in monitoring plant nutrients during their growth. Traditional methods for measuring chlorophyll, in addition to taking a long time, damage the leaves. Alternatively, a model based on hyperspectral images was developed to detect the chlorophyll content in millet leaves. …<\/span> Read More →<\/span><\/a><\/span><\/p>\n","protected":false},"author":106,"featured_media":927,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[3,20,12],"tags":[192,21],"class_list":["post-922","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agricultural-sciences","category-cr","category-press-releases","tag-agronomy","tag-ciencia-rural"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/posts\/922","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/users\/106"}],"replies":[{"embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/comments?post=922"}],"version-history":[{"count":1,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/posts\/922\/revisions"}],"predecessor-version":[{"id":929,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/posts\/922\/revisions\/929"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/media\/927"}],"wp:attachment":[{"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/media?parent=922"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/categories?post=922"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pressreleases.scielo.org\/en\/wp-json\/wp\/v2\/tags?post=922"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}