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  • A single green plant leaf, black background - Stock ImageA single green plant leaf, black backgroundA single green plant leaf, black backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-a-single-green-plant-leaf-black-background-30080152.html
  • Close-up of a North Carolina tobacco leaf that has been pressed in a book as a WWII keepsake since 1943. - Stock ImageClose-up of a North Carolina tobacco leaf that has been pressed in a book as a WWII keepsake since 1943.Close-up of a North Carolina tobacco leaf that has been pressed in a book as a WWII keepsake since 1943.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/close-up-of-a-north-carolina-tobacco-leaf-that-has-been-pressed-in-a-book-as-a-wwii-keepsake-since-1943-image211075304.html
  • Tobacco leaves beginning to flower. - Stock ImageTobacco leaves beginning to flower.Tobacco leaves beginning to flower.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/tobacco-leaves-beginning-to-flower-image61900104.html
  • A single green plant leaf, white background - Stock ImageA single green plant leaf, white backgroundA single green plant leaf, white backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-a-single-green-plant-leaf-white-background-30080038.html
  • Defocused brown dry tobacco leaf closeup texture - Stock ImageDefocused brown dry tobacco leaf closeup textureDefocused brown dry tobacco leaf closeup texturehttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-defocused-brown-dry-tobacco-leaf-closeup-texture-52801041.html
  • tobacco leaf traditional dryer or green drying leaves background - Stock Imagetobacco leaf traditional dryer or green drying leaves backgroundtobacco leaf traditional dryer or green drying leaves backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-leaf-traditional-dryer-or-green-drying-leaves-background-123774703.html
  • Close-up of a green plant leaf - Stock ImageClose-up of a green plant leafClose-up of a green plant leafhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-close-up-of-a-green-plant-leaf-30079797.html
  • Cut-out image of a tobacco plant leaf. - Stock ImageCut-out image of a tobacco plant leaf.Cut-out image of a tobacco plant leaf.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-cut-out-image-of-a-tobacco-plant-leaf-28740155.html
  • Tobacco leaves dry whilst hung in a drier during the tobacco harvest at Dion Tou village, near Shaxi in Yunnan province, - Stock ImageTobacco leaves dry whilst hung in a drier during the tobacco harvest at Dion Tou village, near Shaxi in Yunnan province, China Wednesday August 17, 20Tobacco leaves dry whilst hung in a drier during the tobacco harvest at Dion Tou village, near Shaxi in Yunnan province, China Wednesday August 17, 20https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-leaves-dry-whilst-hung-in-a-drier-during-the-tobacco-harvest-135658350.html
  • The end of a green plant leaf, black background - Stock ImageThe end of a green plant leaf, black backgroundThe end of a green plant leaf, black backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-the-end-of-a-green-plant-leaf-black-background-30079700.html
  • Wet tobacco leaf at tobacco plantation in the Vinales Valley, Cuba. - Stock ImageWet tobacco leaf at tobacco plantation in the Vinales Valley, Cuba.Wet tobacco leaf at tobacco plantation in the Vinales Valley, Cuba.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-wet-tobacco-leaf-at-tobacco-plantation-in-the-vinales-valley-cuba-39974414.html
  • Tropical green plant leaf and stem, orange background - Stock ImageTropical green plant leaf and stem, orange backgroundTropical green plant leaf and stem, orange backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tropical-green-plant-leaf-and-stem-orange-background-30079565.html
  • Bust of Sir Walter Raleigh (Ralegh), draped with a large tobacco leaf and evergreen garland for the Christmas holidays. Deck the Doors Competition. - Stock ImageBust of Sir Walter Raleigh (Ralegh), draped with a large tobacco leaf and evergreen garland for the Christmas holidays. Deck the Doors Competition.Bust of Sir Walter Raleigh (Ralegh), draped with a large tobacco leaf and evergreen garland for the Christmas holidays. Deck the Doors Competition.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/bust-of-sir-walter-raleigh-ralegh-draped-with-a-large-tobacco-leaf-and-evergreen-garland-for-the-christmas-holidays-deck-the-doors-competition-image336579074.html
  • Common Tobacco, Tobacco (Nicotiana tabacum), dried leaf, studio picture - Stock ImageCommon Tobacco, Tobacco (Nicotiana tabacum), dried leaf, studio pictureCommon Tobacco, Tobacco (Nicotiana tabacum), dried leaf, studio picturehttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-common-tobacco-tobacco-nicotiana-tabacum-dried-leaf-studio-picture-25150946.html
  • A tobacco leaf drying shed on a tobacco producing farm in the Valle de Vinales, a UNESCO world cultural landscape in Pinar del Río Province, west Cuba - Stock ImageA tobacco leaf drying shed on a tobacco producing farm in the Valle de Vinales, a UNESCO world cultural landscape in Pinar del Río Province, west CubaA tobacco leaf drying shed on a tobacco producing farm in the Valle de Vinales, a UNESCO world cultural landscape in Pinar del Río Province, west Cubahttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/a-tobacco-leaf-drying-shed-on-a-tobacco-producing-farm-in-the-valle-de-vinales-a-unesco-world-cultural-landscape-in-pinar-del-ro-province-west-cuba-image332007359.html
  • Dried tobacco leaves Andorra - Stock ImageDried tobacco leaves AndorraDried tobacco leaves Andorrahttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-dried-tobacco-leaves-andorra-35788167.html
  • Stacks of dried tobacco awaits the auction block at the Hughesville, MD annual tobacco auctions in Hughesville, - Stock ImageStacks of dried tobacco awaits the auction block at the Hughesville, MD annual tobacco auctions in Hughesville, MD, USAStacks of dried tobacco awaits the auction block at the Hughesville, MD annual tobacco auctions in Hughesville, MD, USAhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-stacks-of-dried-tobacco-awaits-the-auction-block-at-the-hughesville-27978171.html
  • Dried fermented Virginia tobacco leaf (Nicotiana tabacum). Clipping path - Stock ImageDried fermented Virginia tobacco leaf (Nicotiana tabacum). Clipping pathDried fermented Virginia tobacco leaf (Nicotiana tabacum). Clipping pathhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-dried-fermented-virginia-tobacco-leaf-nicotiana-tabacum-clipping-path-130645065.html
  • Tobacco leaves hanging out to dry at the Duke Homestead and Tobacco Museum, Durham, North Carolina, USA - Stock ImageTobacco leaves hanging out to dry at the Duke Homestead and Tobacco Museum, Durham, North Carolina, USATobacco leaves hanging out to dry at the Duke Homestead and Tobacco Museum, Durham, North Carolina, USAhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/tobacco-leaves-hanging-out-to-dry-at-the-duke-homestead-and-tobacco-image62748484.html
  • Golden tobacco leaves drying out on an old barn door. - Stock ImageGolden tobacco leaves drying out on an old barn door.Golden tobacco leaves drying out on an old barn door.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/golden-tobacco-leaves-drying-out-on-an-old-barn-door-image60202796.html
  • Dried tobacco leaves over white background - Stock ImageDried tobacco leaves over white backgroundDried tobacco leaves over white backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-dried-tobacco-leaves-over-white-background-88742024.html
  • tobacco leaf vector illustration - Stock Imagetobacco leaf vector illustrationtobacco leaf vector illustrationhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-leaf-vector-illustration-138339590.html
  • Drying tobacco leaf in a tobacco producing farm in Vinales Cuba - Stock ImageDrying tobacco leaf in a tobacco producing farm in Vinales CubaDrying tobacco leaf in a tobacco producing farm in Vinales Cubahttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-drying-tobacco-leaf-in-a-tobacco-producing-farm-in-vinales-cuba-75507120.html
  • Head on shot of a pressed virginia tobacco leaf - Stock ImageHead on shot of a pressed virginia tobacco leafHead on shot of a pressed virginia tobacco leafhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-head-on-shot-of-a-pressed-virginia-tobacco-leaf-52801038.html
  • Indonesia, Lombok, Agriculture, tobacco growing in field - Stock ImageIndonesia, Lombok, Agriculture, tobacco growing in fieldIndonesia, Lombok, Agriculture, tobacco growing in fieldhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-indonesia-lombok-agriculture-tobacco-growing-in-field-122533548.html
  • Drying tobacco in an open barn france europe - Stock ImageDrying tobacco in an open barn france europeDrying tobacco in an open barn france europehttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-drying-tobacco-in-an-open-barn-france-europe-81388104.html
  • Cut-out image of a tobacco plant leaf. - Stock ImageCut-out image of a tobacco plant leaf.Cut-out image of a tobacco plant leaf.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-cut-out-image-of-a-tobacco-plant-leaf-28741578.html
  • Yiliang, China - March 1, 2021: eldelry woman sorting out tobacco leaves. - Stock ImageYiliang, China - March 1, 2021: eldelry woman sorting out tobacco leaves.Yiliang, China - March 1, 2021: eldelry woman sorting out tobacco leaves.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/yiliang-china-march-1-2021-eldelry-woman-sorting-out-tobacco-leaves-image430931456.html
  • Close up view of tobacco leaves in a plantation - Stock ImageClose up view of tobacco leaves in a plantationClose up view of tobacco leaves in a plantationhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-close-up-view-of-tobacco-leaves-in-a-plantation-17618688.html
  • Tobacco leaf cutting machines - Stock ImageTobacco leaf cutting machinesTobacco leaf cutting machineshttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-leaf-cutting-machines-176798697.html
  • Tobacco leafs in the cigar factory of El Sitio, Brena Alta, La Palma, Spain, Canary Islands, Europe, Atlantic Ocean - Stock ImageTobacco leafs in the cigar factory of El Sitio, Brena Alta, La Palma, Spain, Canary Islands, Europe, Atlantic OceanTobacco leafs in the cigar factory of El Sitio, Brena Alta, La Palma, Spain, Canary Islands, Europe, Atlantic Oceanhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-leafs-in-the-cigar-factory-of-el-sitio-brena-alta-la-palma-48710892.html
  • Golden leaf background. dry tobacco leaf under the sun. Cigarette ingredient or raw material. Tobacco leaf pile. Bunch of raw tobacco leaves. - Stock ImageGolden leaf background. dry tobacco leaf under the sun. Cigarette ingredient or raw material. Tobacco leaf pile. Bunch of raw tobacco leaves.Golden leaf background. dry tobacco leaf under the sun. Cigarette ingredient or raw material. Tobacco leaf pile. Bunch of raw tobacco leaves.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/golden-leaf-background-dry-tobacco-leaf-under-the-sun-cigarette-ingredient-or-raw-material-tobacco-leaf-pile-bunch-of-raw-tobacco-leaves-image343180274.html
  • Pile of dried tobacco isolated on white - Stock ImagePile of dried tobacco isolated on whitePile of dried tobacco isolated on whitehttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-pile-of-dried-tobacco-isolated-on-white-55795533.html
  • tobacco leaf closeup on the white background - Stock Imagetobacco leaf closeup on the white backgroundtobacco leaf closeup on the white backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-image-tobacco-leaf-closeup-on-the-white-background-167225434.html
  • Cuba, Republic of Cuba, Central America, Caribbean Island. Havana district. Tobacco farm in Pinal dal Rio - Stock ImageCuba, Republic of Cuba, Central America, Caribbean Island. Havana district. Tobacco farm in Pinal dal RioCuba, Republic of Cuba, Central America, Caribbean Island. Havana district. Tobacco farm in Pinal dal Riohttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-image-cuba-republic-of-cuba-central-america-caribbean-island-havana-district-164854922.html
  • dry tobacco leaves Lhasa market sepia - Stock Imagedry tobacco leaves Lhasa market sepiadry tobacco leaves Lhasa market sepiahttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/dry-tobacco-leaves-lhasa-market-sepia-image62603452.html
  • Classical way of drying tobacco. Cuba - Stock ImageClassical way of drying tobacco. CubaClassical way of drying tobacco. Cubahttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/classical-way-of-drying-tobacco-cuba-image69579760.html
  • Tobacco leafs, hanging for drying in front of an old barn - Stock ImageTobacco leafs, hanging for drying in front of an old barnTobacco leafs, hanging for drying in front of an old barnhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-leafs-hanging-for-drying-in-front-of-an-old-barn-31849559.html
  • tobacco isolated on a white background - Stock Imagetobacco isolated on a white backgroundtobacco isolated on a white backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-isolated-on-a-white-background-105081021.html
  • Tobacco Leaf - Stock ImageTobacco LeafTobacco Leafhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-leaf-13115432.html
  • The end of a green plant leaf, white background - Stock ImageThe end of a green plant leaf, white backgroundThe end of a green plant leaf, white backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-the-end-of-a-green-plant-leaf-white-background-30079525.html
  • picture of a smoking issues, tobacco and nicotine addiction , health theme - Stock Imagepicture of a smoking issues, tobacco and nicotine addiction , health themepicture of a smoking issues, tobacco and nicotine addiction , health themehttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-image-picture-of-a-smoking-issues-tobacco-and-nicotine-addiction-health-162828372.html
  • Process of drying a nicotine leaf on a cloth - Stock ImageProcess of drying a nicotine leaf on a clothProcess of drying a nicotine leaf on a clothhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-process-of-drying-a-nicotine-leaf-on-a-cloth-52801102.html
  • The end of a green plant leaf, black background - Stock ImageThe end of a green plant leaf, black backgroundThe end of a green plant leaf, black backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-the-end-of-a-green-plant-leaf-black-background-30079707.html
  • Tobacco mosaic virus( TMV ) symptoms of islands of chlorosis seen with back lighting an infected tobacco leaf, - Stock ImageTobacco mosaic virus( TMV ) symptoms of islands of chlorosis seen with back lighting an infected tobacco leaf,Tobacco mosaic virus( TMV ) symptoms of islands of chlorosis seen with back lighting an infected tobacco leaf,https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/tobacco-mosaic-virus-tmv-symptoms-of-islands-of-chlorosis-seen-with-back-lighting-an-infected-tobacco-leaf-image355551052.html
  • Cut-out image of a tobacco plant leaf. - Stock ImageCut-out image of a tobacco plant leaf.Cut-out image of a tobacco plant leaf.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-cut-out-image-of-a-tobacco-plant-leaf-28741031.html
  • Simple green plant leaf and stem, orange background - Stock ImageSimple green plant leaf and stem, orange backgroundSimple green plant leaf and stem, orange backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-simple-green-plant-leaf-and-stem-orange-background-30079488.html
  • Heap of smoking tobacco - Stock ImageHeap of smoking tobaccoHeap of smoking tobaccohttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-heap-of-smoking-tobacco-22619483.html
  • Tobacco leaf blocks stored in the Primary Department at Imperial Tobacco, Nottingham, England. - Stock ImageTobacco leaf blocks stored in the Primary Department at Imperial Tobacco, Nottingham, England.Tobacco leaf blocks stored in the Primary Department at Imperial Tobacco, Nottingham, England.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-leaf-blocks-stored-in-the-primary-department-at-imperial-tobacco-176798759.html
  • Green plant leaf on a branch, black background - Stock ImageGreen plant leaf on a branch, black backgroundGreen plant leaf on a branch, black backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-green-plant-leaf-on-a-branch-black-background-30080585.html
  • Cuba, Havana, Corona cigars factory, tobacco leaf - Stock ImageCuba, Havana, Corona cigars factory, tobacco leafCuba, Havana, Corona cigars factory, tobacco leafhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-cuba-havana-corona-cigars-factory-tobacco-leaf-35341923.html
  • Macro of tobacco isolated on a white background - Stock ImageMacro of tobacco isolated on a white backgroundMacro of tobacco isolated on a white backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-macro-of-tobacco-isolated-on-a-white-background-34472240.html
  • Green plant leaf on a branch, black background - Stock ImageGreen plant leaf on a branch, black backgroundGreen plant leaf on a branch, black backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-green-plant-leaf-on-a-branch-black-background-30080720.html
  • tobacco pile isolated - Stock Imagetobacco pile isolatedtobacco pile isolatedhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-pile-isolated-86653087.html
  • Detail of tobacco leaf - Stock ImageDetail of tobacco leafDetail of tobacco leafhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-detail-of-tobacco-leaf-97919350.html
  • Green plant leaf with strong structure on a white background - Stock ImageGreen plant leaf with strong structure on a white backgroundGreen plant leaf with strong structure on a white backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-green-plant-leaf-with-strong-structure-on-a-white-background-32534234.html
  • Drying barn for tobacco leaf in Vinales valley with hills mountains, tobacco field, Vinales, Cuba, Pinar del Río, - Stock ImageDrying barn for tobacco leaf in Vinales valley with hills mountains, tobacco field, Vinales, Cuba, Pinar del Río, CubaDrying barn for tobacco leaf in Vinales valley with hills mountains, tobacco field, Vinales, Cuba, Pinar del Río, Cubahttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-drying-barn-for-tobacco-leaf-in-vinales-valley-with-hills-mountains-90259140.html
  • Close-up of cigar box with tobacco leaf remains - Stock ImageClose-up of cigar box with tobacco leaf remainsClose-up of cigar box with tobacco leaf remainshttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-close-up-of-cigar-box-with-tobacco-leaf-remains-21662638.html
  • Green plant leaf with strong structure on a black background - Stock ImageGreen plant leaf with strong structure on a black backgroundGreen plant leaf with strong structure on a black backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-green-plant-leaf-with-strong-structure-on-a-black-background-32534199.html
  • Fresh tobacco leaves - Stock ImageFresh tobacco leavesFresh tobacco leaveshttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-fresh-tobacco-leaves-114430980.html
  • Luxury cigars on gold background. - Stock ImageLuxury cigars on gold background.Luxury cigars on gold background.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-luxury-cigars-on-gold-background-24391860.html
  • Green plant leaf with strong structure on a black background - Stock ImageGreen plant leaf with strong structure on a black backgroundGreen plant leaf with strong structure on a black backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-green-plant-leaf-with-strong-structure-on-a-black-background-30081755.html
  • Loose shredded tobacco isolated on white background with copyspace - Stock ImageLoose shredded tobacco isolated on white background with copyspaceLoose shredded tobacco isolated on white background with copyspacehttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-loose-shredded-tobacco-isolated-on-white-background-with-copyspace-19972015.html
  • Tobacco leaves, tobacco plant (Nicotiana), tobacco plantation, farming in the Valle de Vinales national park - Stock ImageTobacco leaves, tobacco plant (Nicotiana), tobacco plantation, farming in the Valle de Vinales national parkTobacco leaves, tobacco plant (Nicotiana), tobacco plantation, farming in the Valle de Vinales national parkhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-leaves-tobacco-plant-nicotiana-tobacco-plantation-farming-48823180.html
  • Cut-out image of a tobacco plant. - Stock ImageCut-out image of a tobacco plant.Cut-out image of a tobacco plant.https://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-cut-out-image-of-a-tobacco-plant-28740188.html
  • Farmer holding dried tobacco leaf, Vinales, Cuba - Stock ImageFarmer holding dried tobacco leaf, Vinales, CubaFarmer holding dried tobacco leaf, Vinales, Cubahttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-farmer-holding-dried-tobacco-leaf-vinales-cuba-39835266.html
  • Heap of smoking tobacco - Stock ImageHeap of smoking tobaccoHeap of smoking tobaccohttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-heap-of-smoking-tobacco-22619434.html
  • taking off vein of tobacco leaf,La Palma,Canary Islands, Spanish archipelago of Atlantic Ocean - Stock Imagetaking off vein of tobacco leaf,La Palma,Canary Islands, Spanish archipelago of Atlantic Oceantaking off vein of tobacco leaf,La Palma,Canary Islands, Spanish archipelago of Atlantic Oceanhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/taking-off-vein-of-tobacco-leafla-palmacanary-islands-spanish-archipelago-image61495642.html
  • tobacco leaf traditional dryer or green drying leaves background - Stock Imagetobacco leaf traditional dryer or green drying leaves backgroundtobacco leaf traditional dryer or green drying leaves backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-leaf-traditional-dryer-or-green-drying-leaves-background-123774740.html
  • tobacco leaf leaves dry cigar brown plant isolated - Stock Imagetobacco leaf leaves dry cigar brown plant isolatedtobacco leaf leaves dry cigar brown plant isolatedhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-tobacco-leaf-leaves-dry-cigar-brown-plant-isolated-88329822.html
  • Green plant leaves on a branch, white background - Stock ImageGreen plant leaves on a branch, white backgroundGreen plant leaves on a branch, white backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-green-plant-leaves-on-a-branch-white-background-30080381.html
  • Tobacco leaf drying in the town of Prilep. Macedonia, Europe - Stock ImageTobacco leaf drying in the town of Prilep. Macedonia, EuropeTobacco leaf drying in the town of Prilep. Macedonia, Europehttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/tobacco-leaf-drying-in-the-town-of-prilep-macedonia-europe-image234767741.html
  • Cropped Image Of Vendor With Tobacco Leaf At Table - Stock ImageCropped Image Of Vendor With Tobacco Leaf At TableCropped Image Of Vendor With Tobacco Leaf At Tablehttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/cropped-image-of-vendor-with-tobacco-leaf-at-table-image266310975.html
  • Rural worker holding tobacco leaf in the countryside - the rough region - Stock ImageRural worker holding tobacco leaf in the countryside - the rough regionRural worker holding tobacco leaf in the countryside - the rough regionhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-rural-worker-holding-tobacco-leaf-in-the-countryside-the-rough-region-97922143.html
  • Green plant leaves on a branch, black background - Stock ImageGreen plant leaves on a branch, black backgroundGreen plant leaves on a branch, black backgroundhttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-green-plant-leaves-on-a-branch-black-background-30081081.html
  • Drying barn for tobacco leaf in Vinales valley with hills mountains, tobacco field, Vinales, Cuba, Pinar del Río, - Stock ImageDrying barn for tobacco leaf in Vinales valley with hills mountains, tobacco field, Vinales, Cuba, Pinar del Río, CubaDrying barn for tobacco leaf in Vinales valley with hills mountains, tobacco field, Vinales, Cuba, Pinar del Río, Cubahttps://www.alamy.com/licenses-and-pricing/?v=1https://www.alamy.com/stock-photo-drying-barn-for-tobacco-leaf-in-vinales-valley-with-hills-mountains-90259142.html
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Data Research on Tobacco Leaf Image Collection Based on Computer Vision Sensor

In the process of tobacco silk making, how to better improve the quality of stem and leaf separation has become an issue of concern. His research mainly discusses the data collected from tobacco leaf images based on computer vision sensors. In LM (Levenberg-Marquarelt) as a training function, the algorithm uses threshing effect samples for training and learning. This paper is aimed at extracting the shape characteristic parameters of tobacco leaves and obtains the shape parameters of the length, width, area, circumference, and roundness of the tobacco leaves. In this paper, boundary tracking is used to obtain the coordinate and direction information of the tobacco leaf boundary pixels in the image, which provides a basis for obtaining the subsequent extraction of tobacco leaf characteristic parameters. In the tobacco leaf grading system, the tobacco leaf feature parameter extraction module displays the geometric characteristics of tobacco leaf, such as length, width, area, aspect ratio, rectangularity, and color characteristic, hue , saturation , channel, and channel in detail through the computer vision sensor. Finally, the subjective and objective combination weighting method is used to combine and weight the indicators of the threshing effect of the first-level threshing machine, which not only considers the quantity of information provided by the indicators but also takes into account the subjective view of the experts, which increases the weight of the indicators, accuracy, and scientificity. The approximation accuracy of the training samples of the threshing effect prediction model based on the BP neural network LM algorithm is 99.495%, the approximation accuracy of the validation set is 96.535%, and the approximation accuracy of the test set is 98.392%. This research will greatly improve production efficiency and meet the enterprise’s requirements for high efficiency and low cost.

1. Introduction

At present, tobacco leaves face many problems in the production of wind separation, such as improving the utilization of tobacco leaves during the process of threshing and retrying tobacco leaves, that is, maximizing the wind separation to produce qualified pieces of tobacco, and the visualization of the stem-leaf separation process is at home and abroad. It is still a research blank. At the same time, domestic and foreign scholars researching tobacco wind fraction numerical simulation technology are all empirical judgments and have not passed relevant theoretical and technical verification.

In the tobacco curing process, the change of the color and shape of the tobacco leaves is still the main basis for people to judge the curing. Using a computer to process the digital image of the tobacco leaves cannot only solve the problem of the roasting staff due to the low quality of the tobacco. It can also realize intelligent baking and reduce the production cost of tobacco leaves.

The realization of tobacco leaf classification automation technology is very promising. Harjoko believes that in Indonesia, tobacco grading is done by hand, relying on the skills and experience of the tobacco grading staff. Large tobacco plantations require many graders, and workers need to be trained to become skilled graders. Therefore, he proposed a method of grading tobacco leaves based on color and quality using image processing technology. He uses image processing techniques such as image thresholding, morphological operation, spot detection, and tobacco leaf color analysis to determine tobacco leaf grade. Although the method he proposed can detect blade defects, the accuracy of the detection is not clearly stated [1]. Camlica believes that tobacco is important to the agricultural sector in Turkey, and different regions of the country produce high-quality varieties. His research aims to examine the important morphological characteristics and yields of a cultivar and some genotypes of tobacco under the conditions of Bolu Province, Turkey, in 2015 and 2016. Genetic variation parameters such as GCV (%), PCV (%), GA, and heritability are performed to provide the best picture of tobacco variety and genotypes. He also conducted a correlation analysis of these traits of tobacco. Although his research is highly significant and positively correlated, the amount of data in the research is too small [2]. Pereira believes that although tobacco (Nicotiana tabacum) is the experimental host of Trichoderma fastidious, it is an excellent plant model that can be used for biological and functional genomics studies involving the host-pathogen interaction of Trichoderma fastidious. He designed a Standard Area Atlas (SAD) to help visually estimate the percentage of affected areas (percentage of severity) and conducted multilaboratory validation on tobacco. Monitor the inoculated plants over time and record digital images of symptoms. Three different software programs (APS, Asses, ImageJ, and Leaf Doctor) are used to segment the image and calculate the severity percentage. 10 true color images make up 10 image SADs (0.5, 5, 10, 15, 25, 35, 45, 55, 65, and 75%). Although his research methods are comprehensive, there is still a lack of contrast between images [3, 4]. Moeys believes that applications that need to detect small visual contrast require high sensitivity. He presented the results of the 180-nanometer Towerjazz CIS process vision sensor named SDAVIS192. SDAVIS192 improves the previous DAVIS sensor with higher temporal contrast sensitivity. He believes that this goal can be achieved by using a preamplification stage within the pixel. The preamplifier reduces the effective inscene DR of the sensor (70 dB when off and 50 dB when on), but the automatic operating area control allows at least 110 dB DR for offevents. Although he developed a characterization method for measuring DV, the research lacks innovation [5].

In this study, the LM algorithm is selected as the training function, and the threshing effect samples are used for training and learning. This paper is aimed at extracting the shape characteristic parameters of tobacco leaves and obtains the shape parameters of the length, width, area, circumference, and roundness of the tobacco leaves. In this paper, boundary tracking is used to obtain the coordinate and direction information of the tobacco leaf boundary pixels in the image, which provides a basis for obtaining the subsequent extraction of tobacco leaf characteristic parameters. In the tobacco leaf grading system, the tobacco leaf feature parameter extraction module displays the geometric characteristics of the tobacco leaf such as length, width, area, and aspect ratio in detail through the computer vision sensor. Finally, the subjective and objective combination weighting method is used to combine and weight the indicators of the threshing effect of the first-level threshing machine, which not only considers the quantity of information provided by the indicators but also takes into account the subjective view of the experts, which improves the accuracy and scientificity of the indicator weights.

2. Research Method

2.1. Image Preprocessing

Due to the complex factors affecting tobacco leaves during the production process, the tobacco leaves produced by tobacco farmers are often of different quality. Only through grading processing can tobacco leaves with relatively consistent quality be included in the same grade.

Image preprocessing is the key link in the extraction of characteristic parameters of tobacco leaves, which directly determines whether the subsequent identification of tobacco leaves is accurate and reliable. Because the acquired tobacco leaf images are often affected by the light and environment, as well as the optical characteristics of the image acquisition equipment and other objective factors, the captured tobacco leaf images are not very clear. The original photo taken by the camera is shown in Figure 1.


The tobacco leaf image collected in the tobacco factory will inevitably have some differences with the actual tobacco leaf, and in severe cases, the image will also be degraded, distorted, or contain noise. Image preprocessing can improve and improve the quality of the tobacco leaf images collected in this experiment, remove some useless information during the shooting process of the image, and restore useful real information, thereby improving the testability of the image, simplifying the image data information, and improving the testability of the image. Obtain characteristic information such as the shape of the tobacco leaf and analyze and recognize the image to prepare. The preprocessing of tobacco leaf images is the first step of tobacco leaf identification, and it is also a key step that must be carried out in this research [6, 7].

2.1.1. Image Grayscale

Grayscale image is a unique image that keeps the same value of . In other words, the change scale of any pixel in the image is only 256 patterns. That is, the central guideline of image graying is , and this value is also called gray value. Therefore, in the image processing process, to make the calculation workload of the following image, it is common to convert different types of images into grayscale images in a unified manner. Therefore, under normal circumstances, the computer is used to first realize the gray-scale conversion of the original image and then to remove the noise and other follow-up tasks, which can reduce the workload and strengthen the characteristic information of the fault. The gray value of a certain point corresponds to the temperature value of that point in the infrared image before gray conversion [8]. The gray image uses the difference in brightness to represent different gray values. The gray value is simply the depth of the color in the black and white gray image, the value range is 0-255, where the corresponding value of white is 255 that is the upper limit of the interval, and the corresponding value of black is 0 that is the lower limit of the interval; so, each pixel in the grayscale the image will have a value from 0 to 255 corresponding to it. Figure 2 shows the comparison between average method, weighted average method, and maximum value method. After actual analysis, the grayscale effect of the maximum value method is the best [9].


2.1.2. Image Denoising

The linear filter represented by median filter has also been widely used in the field of image denoising because of its simple algorithm and fast running speed. However, the conventional median filter will cause loss of detail and blurring of edges; so, researchers are improving the median filter.

Statistical median filtering first determines a filter window and position (usually containing an odd number of pixels), then sorts the pixel values in the window according to the gray scale, and finally takes the median to replace the pixel value in the center of the original window. In this study, the median filter method was used to denoise the image.

2.1.3. Image Binarization

Binarization can be understood as turning a picture into a picture with only two colors. For example, first define a value. When a certain gray value of the picture is greater than this value, it will be converted to white, and when it is less than this value, it will become black. This method turns the entire picture into an image with only two color patches [10].

and are points on the image. In the tobacco leaf recognition in this article, the most fundamental purpose of using the image binarization method is to effectively segment the tobacco leaf area and other irrelevant areas in the original image. The automatic threshold rule can overcome the defect that the manual selection method cannot meet the basic requirements of most applications. The maximum gray value and the minimum gray value of the image can be obtained, so that [11]

According to the threshold , the image is divided into two regions, R1 and R2, and the average gray value of the two regions is calculated using the following formula [12].

Use the following formula to find the new threshold.

2.1.4. Morphological Operation of Image

In the research, due to the tobacco leaves during the binarization operation, some of the tobacco leaves will be broken and holes, which will greatly affect the subsequent image analysis. For this reason, the closed operation should be used for the tobacco leaf image.

2.1.5. Dot Multiplication Operation

In the work of collecting tobacco leaf images, some small tobacco leaves will be scattered on the white cardboard. If they are not processed, it will greatly affect the feature extraction of subsequent images, resulting in inaccurate results. For this reason, the image should be multiplied by dots to eliminate the image background and only keep the tobacco leaf area within the white cardboard. Binary image processing is shown in Figure 3.


From the binary image, it can be seen that there are still holes in the tobacco leaf, which will affect the subsequent analysis, and the tobacco leaf should be filled again.

2.1.6. Image Edge Extraction

This paper is aimed at extracting the shape characteristic parameters of tobacco leaves and obtains the shape parameters of the length, width, area, circumference, and roundness of the tobacco leaves. Therefore, the primary goal is to extract a complete tobacco leaf profile. At present, the commonly used methods of edge extraction include edge detection, contour extraction, and boundary tracking.

In this paper, boundary tracking is used to obtain the coordinate and direction information of the tobacco leaf boundary pixels in the image, which provides a basis for obtaining the subsequent extraction of tobacco leaf characteristic parameters.

2.1.7. Extraction of Image Features of Tobacco Leaves

The shape of the tobacco leaf after threshing is an important analysis content of the threshing effect of the threshing machine. The tobacco leaf image can be further extracted after the steps of image graying, smoothing and decrying, and binarization. There is no uniform regulation for the selection of the shape characteristic parameters of tobacco leaves. As long as they can reflect the shape of tobacco leaves conveniently and quickly, they can be used as the shape characteristic parameters of tobacco leaves. In this experiment, five shape characteristic parameters of tobacco leaves after threshing treatment were selected, namely, area, circumference, long diameter, short diameter, and roundness coefficient.

Division: tobacco leaves with an area greater than 645.16mm2 are considered large, between 645.16mm2 and 161.29mm2 that are medium ones, between 161.29mm2 and 40.32mm2 that are small ones, and less than 40.32mm2 that are fragments, and roundness 3 is round-like tobacco leaves. The leaf rate in medium is divided into large leaf rate, medium leaf rate, small leaf rate, broken leaf rate, and round leaf rate. The stem leaves are tobacco stems with tobacco leaves, and the stems are the only tobacco stems without tobacco leaves.

2.2. Prediction Model of Threshing Effect Based on BP Neural Network
2.2.1. The Learning Steps of BP Algorithm

(1)Set variables and parameters, including training samples, weight, learning matrix, and learning rate(2)Initialize and give each weight matrix a small random nonzero vector(3)Enter a random sample(4)Forward calculation of the input signal and output signal of each layer of the BP network(5)Obtain the error from the actual output and the expected output(6)Determine whether the maximum number of iterations has been reached, if it has been reached, go to step 8; otherwise, calculate the local gradient of each layer of neurons in reverse(7)Modify the weight of each matrix according to the local gradient(8)Judge whether all samples have been learned, if they have been learned, then end; otherwise, go to step 3. The first consideration in this research is to set the number of neurons in the hidden layer to construct the network

2.2.2. Network Training Function

In the LM algorithm, the full name is Levenberg-Marquard algorithm, it can be used to solve the problem of nonlinear least squares, and it is mostly used for curve fitting and other fields. The LM algorithm needs to find the partial derivative of each parameter to be estimated.

The LM algorithm is a fast algorithm that can be used to solve nonlinear least squares problems. It is an improvement on the basis of the Gauss-Newton algorithm, combining the Gauss-Newton method with the gradient descent method, which has the local characteristics of the Gauss-Newton method. Because the LM algorithm uses the second-order derivative information and adds a β parameter correction algorithm based on the LM-BP, the convergence speed is much faster than the traditional BP network using the gradient descent method and the algorithm is stable. Therefore, the LM algorithm is selected as the training function, and the threshing effect samples are used for training and learning. After the output of times, the total error data is obtained [13].

It can be seen that as long as it is necessary to use human vision to judge the quality of agricultural products, most of the machine vision has its place. Even in some aspects, such as the internal quality inspection of agricultural products, and the judgment of small differences, machine vision has surpassed human vision. Therefore, it can be concluded [14].

Then, is obtained by the Gauss-Newton method [15].

According to the improved method, the LM algorithm of the Gauss-Newton method, replacing with , the learning algorithm becomes [16].

After the network training is completed, when recognizing the tobacco leaf image, only the forward propagation process of the information is needed, and the back propagation process is not needed. This is also the BP neural network in tobacco leaf image recognition that is much faster than the template matching method. Main reason: the range of learning rate is usually chosen between 0.01 and 0.5 based on experience [17].

2.3. Flue-Cured Tobacco Leaf Classification System

In this paper, a flue-cured tobacco leaf grading system is established based on MATLAB software. According to the realized functions, it can be divided into the following modules: tobacco leaf image opening and display module, tobacco leaf image preprocessing module (image filtering, image binarization, image segmentation), tobacco leaf characteristics parameter extraction module (divided into two parts: geometric feature extraction and color feature extraction), and tobacco leaf grade fuzzy mode reading module and tobacco leaf grading module (display the final tobacco leaf grade). The software interface of the tobacco leaf automatic grading system is shown in Figure 4.The advantages of digital image processing and analysis technology are high processing accuracy, rich processing content, complex nonlinear processing, and flexible flexibility. Generally speaking, the processing content can be changed as long as the software is changed.


The characteristic of the tobacco grading system is to improve the flexibility and automation of production. In some dangerous working environments that are not suitable for manual operations or occasions where artificial vision is difficult to meet the requirements, machine vision is often used to replace artificial vision; at the same time, in the mass industrial production process, manual visual inspection of product quality is inefficient and inaccurate. The use of machine vision inspection methods can greatly improve production efficiency and production automation.

Image opening and display module: It is mainly realized by the MATLAB functions imread() and imshow(). This module allows the software operator to clearly see which tobacco leaf is being graded.

Image preprocessing module: it mainly consists of three parts: image filtering, image binarization, and image segmentation, and each step of preprocessing is completed; the corresponding preprocessing results will be displayed on the interface. This makes it easy for the operator to observe the effect of image preprocessing good or bad.

Tobacco leaf feature parameter extraction module: this module is divided into geometric feature extraction and color feature extraction. The geometric features of tobacco leaf such as length, width, area, aspect ratio, rectangularity, color feature hue , and saturation are displayed in detail through the computer vision sensor , channel, and channel.

Tobacco leaf grade fuzzy mode reading module: it can realize the fuzzy mode reading of the tobacco leaf automatic grading model, and it can be supplemented and adjusted according to the tobacco leaf sample collection.

Tobacco leaf grading module: taking 22 levels of tobacco leaf samples as the domain of discussion, 5 geometric features, 4 color features, and a total of 9 appearance features, the average of the appearance feature vector of each level is the fuzzy mode, the realization based on fuzzy pattern recognition tobacco leaves are automatically graded, and the final grading result is displayed on the interface [18].

2.4. User Interface Design

The user interface is mainly composed of 4 parts: the image acquisition display interface, the pattern recognition system method selection interface, the tobacco leaf characteristic parameter interface, and the result display interface. (1)Acquisition image display interface: this interface allows users to observe the acquired images in real time and determines the integrity of the scanned images, the reasonableness of the illumination, etc., to determine whether the acquired images are suitable for further processing. If there is a problem with the acquisition system, the hardware system can be maintained and improved in time(2)Pattern recognition system method selection interface: interface can be used to select the method of each module of the pattern recognition system. Among them, image denoising includes: median filtering, wavelet denoising, and contour let transform; image segmentation includes iterative method and OTSU threshold method; image edge extraction includes Roberts edge operator and Prewitt edge operator, and Canny edge operator classifier includes BP neural network, extreme learning machine, and regular extreme learning machine. If you need to add other image processing methods, you only need to add the program of the method directly in the corresponding place of each module(3)Tobacco leaf characteristic parameter interface: this interface has the main characteristic values of fresh tobacco leaves, including 3 characteristic values of color characteristic and 5 characteristic values of texture characteristic. Users can determine whether the characteristics of tobacco leaves obtained by pattern recognition are reasonable based on experience and provide an important reference for tobacco leaf classification(4)The result display interface: interface is the tobacco leaf grade identified by each characteristic parameter. The tobacco grower can judge whether the grade is correct based on experience, and it also provides a basis for the correct sorting of the subsequent implementing agencies

2.5. Evaluation Index of Threshing Effect

Each module of the system simulates the thinking intelligence, perception intelligence, and behavior intelligence of graded experts. It has multiple thinking functions such as learning and memory, judgment and fuzzy reasoning, and graded decision-making, as well as coordination and control functions such as automatic image collection and communication between upper and lower computers.

When experts think that a certain index is important, they often fail to get ideal evaluation results. Therefore, this article decides to use the subjective and objective combination weighting method to combine and weight the indicators of the threshing effect of the first-level threshing machine. It not only considers the amount of information provided by each indicator but also takes into account the subjective views of experts, which improves the accuracy and scientificity of the indicator weights. Suppose is the index weight after the combination of AHP-entropy weighting method, is [19]

When using the subjective and objective combination weighting method, there are usually the following two combination models, namely [20],

Subjective weight is determined by the analytic hierarchy process; objective weight is determined by entropy weight method. Construct the objective function, take the minimum sum of squares of “the difference between the subjective weight and the combined weight” and the “the difference between the objective weight and the combined weight” as the goal, and obtain the proportion of the subjective weight and the objective weight in the combined weight proportion [21].

You can get as [22]

3. Results

In addition, with the promotion of threshing and retrying, the tobacco leaves purchased and used by cigarette factories will gradually become slivers, and the difficulty of grading slabs will increase, because many characteristics of tobacco leaves such as size, leaf shape, and veins are in the slabs of leaves. No longer exists in the computer vision, and when the computer vision recognizes the tobacco leaf, it is more dependent on the color and surface characteristics of the tobacco leaf to detect the leaf. Determine the final closeness of each index, and some data are shown in Table 1. It can be seen from Table 1 that the comprehensive score obtained by subjective and objective combination weighting of various indicators and the TOPSIS method of gray correlation degree can evaluate the pros and cons of threshing effect. In this test, the test program of the 9th group has the highest score of closeness, which is 0.622777; that is, under the process parameters of the feed amount of 10000, the speed of the beater is 47, the opening of the frame is 3.2, and the beater of the threshing machine leaf effect is the best. Under the technological conditions with a feed rate of 10000 kg/hr, a frame opening of 2.8, and a batting speed of 47 hz, when only changing the batting speed to 50hz, the closeness increased from 0.493842 to 0549903, an increase of 11.2%. Under the technological conditions that the feed rate is 1000 kg/hr and the frame opening is 2.8, increasing the speed of the beater will increase the threshing effect. In the same way, under the process conditions of a feed rate of 12,500 kg/hr, a frame opening of 2.8, and a batting speed of 47hz, when only the batting speed is changed to 50 hz, the closeness drops from 0.504675 to 0.425982, which is 15.6% decrease. Which shows that under the technological conditions of the feed rate of 12500 kg/hr and the frame opening of 2.8, increasing the speed of the beater will cause the effect of threshing to decrease. It can be seen that through the comprehensive evaluation system of the threshing effect, the threshing effect under the threshing process parameters can be evaluated and analyzed. However, the influence of the process parameters of the threshing machine on the threshing effect is complicated, and the comprehensive evaluation method can only the evaluation and analysis of the test plan cannot deeply understand the influence of process parameters on the effect of threshing.


TestFeed amountRotating speedFrame openingClosenessRank

TI10000472.80.49384214
T210000482.80.5354659
T30000492.80.52088910
T410000502.80.5490036
T5100004730.5601463
T6100004830.542148
T7100004930.5549415
T8100005030.52087811
T910000473.20.6227771
T2112500472.80.50467512
T2212 500482.80.37031140
T2312500492.80.43226419
T2412500502.80.42598222

Artificial neural network classification technology fully absorbs the characteristics of human understanding of things. In addition to using the spectral characteristics of the image itself, it can also apply features such as the geometric space of the image. More importantly, it utilizes the accumulation of people in the past when recognizing images. Experience, under the guidance of the information of the classified image, through self-learning (i.e., training), modifies its own structure and recognition method, thereby improving the classification accuracy and classification speed of the image to obtain satisfactory classification results.

Use the trained BP neural network to predict and analyze the threshing effect under the technical parameters of the threshing machine to obtain each index value and then use the subjective and objective combination weight coefficient to weight and integrate the index value. The five indexes of stem rate, stem rate, round leaf rate, and fragment rate are transformed into a total index, namely, leaf threshing rate, which is used to evaluate the effect of threshing, as shown in Table 2.


Feed amountRotating speedFrame opening>161.29 mm blade rateRoundnessStem leaf rateStalk rateBroken leaf rate

10000472.80.3412075380.4037994510.3249954350.115241460.002398413
10000482.80.3558889780.4284602770.3557742780.0844164140.003106001
10000492.80.3215214140.3872923370.3621926490.103360990.0024444
10000502.80.379391950.4251594170.3714486640.0752013310.002177149
100004730.3815325980.419735560.376671620.0580014920.00221401
100004830.3360025070.3955691150.399567210.0505845660.003017036
100004930.3342210770.376195270.4079725570.059515420.002322965
12500472.80.2718016320.3904788930.4119601330.067807560.003916224
12500482.80.2884686730.4193669980.3925245250.0644106270.004343416
12500492.80.3072283560.4217869140.4081871350.051065960.003539021
12500502.80.2743909850.4031123460.4269527480.0500326240.005095157

The running process of BP neural network is shown in Figure 5. Choose 80% as the training set, 10% as the test set, and 10% as the validation set.


The approximation accuracy of the training samples of the threshing effect prediction model based on the BP neural network LM algorithm is 99.495%, the approximation accuracy of the validation set is 96.535%, the approximation accuracy of the test set is 98.392%, which is greater than 95%, and the total has also reached 98.864%, which proves that the network model is effective and the accuracy of the approximation is extremely high. The training effect of the model is shown in Figure 6.


The residual values are mainly distributed within -0.00355 and -0.001553, indicating that the approximation accuracy of the network model is good. It can be seen that the prediction accuracy using neural network is very high. The residual difference between the actual value and the predicted value in the prediction model is shown in Figure 7.


The influence of single factor effect on threshing rate is shown in Figure 8. It can be seen from Figure 8 that within the test range, there is a negative correlation between feed rate, beater speed, frame opening, and threshing rate. The increase in the value of the feed rate, the speed of the beater, and the opening of the frame will reduce the threshing rate.


To express the relationship between the process parameters and the threshing rate, the process parameters: the feed amount , the beater speed , the frame opening , and the threshing rate are subjected to multiple regression analysis, and the regression equation is established [23].

Multiple linear regression equation is as follows:

The analysis of variance of the multiple linear regression equation is shown in Table 3.


FactorDegree of freedomSum of squareMean squareF value value

Model314.0414.6804850.15<0.001
19 8859.88501105.91<0.001
11.4071.4074915.08<0.001
12.7492.7489529.45<0.001
Error363.3600.09333
Total3917.401

,.

Multiple nonlinear regression equation [24] is as follows.

The analysis of the multiple nonlinear regression equation is shown in Table 4.


FactorDegree of freedomSum of squareMean square value value

Model616.13162. 6885969.87<0.001
19. 85509.88501256.90<0.001
11.40751.4074935.58<0.001
12.74902.7489571.44<0.001
10.24520.245236.37<0.001
11.72201.7220544.75<0.001
Sours: https://www.hindawi.com/journals/js/2021/4920212/
  1. Bellevue one bedroom apartments
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An approach to classify flue-cured tobacco leaves using deep convolutional neural networks

Abstract: Convolutional Neural Network (CNN) is a Multi-Layer Perceptron Neural Network (MLP), specially designed for classification and identification of image data. MLPs are very useful but very slow for learning image features. Even for small images MLPs takes a lot of time to learn the features. On contrary, Convnets detects the features locally and propagate them to the neighboring layer so that the learning process is easier and efficient. Image reduction is a process normally used to reduce the number of learning parameters. The present paper is aimed at designing a new technique to convolve the input image, using Deep CNN algorithm and then reduce the image dimension by pooling techniques. The new technique is applied for image classification of flue-cured tobacco leaves. About 120 samples of cured tobacco leaves are taken for training the CNN and reduced the image dimensions from 1450×1680 to 256×256 RGB. Here four hidden layer CNN is considered and performed convolution and pooling on input images with sixteen, thirty two and sixty four feature kernels on first three hidden layers and fourth layer is connected to output layer. Max pooling technique is used in the model and classified them into three major classes' class-1, class-2 and class-3 with a global efficiency of 85.10% on the test set consisting about fifteen images of each group. Results from the proposed model are compared with other existing models and shown that the model performs better even with small training set.

Published in: 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS)

Article #:

Date of Conference: 24-26 Nov. 2017

Date Added to IEEE Xplore: 23 April 2018

ISBN Information:

Electronic ISBN: 978-1-5386-0497-7

CD: 978-1-5386-0496-0

Print on Demand(PoD) ISBN: 978-1-5386-0498-4

ISSN Information:

Electronic ISSN: 2327-0594

Sours: /document/
Sours: https://www.123rf.com/stock-photo/tobacco_leaf.html

Leaves images tobacco

The coordinates are: Be careful, we are near the pirate base. We will transmit at maximum power. Every 8-10 hours I will watch the fuel level in the gas generator. Sitting in the cave is too disgusting and we are going out into the air. The sun is already clear going to sit down.

Color Curing fresh picked Tobacco leaves, Part 6 of \

Petya timidly cut off pieces of bread and asked permission to take each piece of sausage. -What are you, in the end, huh. Why are you asking me about each piece, Sing.

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Really great, welcoming dream city. Sperm coffee In a cafe I watched an interesting menu item for girls. Coffee with semen. All the benefits and benefits of this drink are described in great detail. And it is prepared in three versions: 1.



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