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Yıl 2023, Cilt: 6 Sayı: 5, 511 - 515, 01.09.2023

Hayrettin Karadöl Hamza Kuzu Mualla Keten

https://doi.org/10.47115/bsagriculture.1324253

Öz

Kaynakça

  • Acikgoz E, Sincik M, Wietgrefe G, Surmen M, Cecen S, Yavuz T, Erdurmus C, Goksoy AT. 2013. Dry matter accumulation and forage quality characteristics of different soybean genotypes. Turkish j Agri Forest, 37(1): 22-32.
  • Agin O, Malasli MZ. 2016. The Place and importance of image processing techniques in sustainable agriculture. J Agri Machin Sci, 12(3): 199-206.
  • Arioglu HH. 2007. The oil seed crops growing and breeding. University of Cukurova Publications, No: 220, Adana, Türkiye, pp: 142.
  • Balci M, Altun AA, Tasdemir S. 2016. Classification for Napoleon type cherries by using image processing techniques. J Selcuk-Tech, 15(3): 221-237.
  • Balkir P, Kemahlioglu K, Yucel UM. 2019. Machine vision system: food industry applications and practices. Turkish J Agri Food Sci Tech, 7: 989-999.
  • Cai X, Sun Y, Zhao Y, Damerow L, Lammers, PS, Sun W, Lin J, Zheng L, Tang Y. 2013. Smart detection of leaf wilting by 3D image processing and 2D Fourier transform. Comput Elect Agri, 90: 68-75.
  • Candogan BN. 2009. Water-yield relationships of soybean. PhD Thesis, Bursa Uludag University Institute of Science, Bursa, Türkiye, pp: 121.
  • Chen X, Xun Y, Li W, Zhang J. 2010. Combining discriminant analysis and neural networks for corn variety identification. Comput Elect Agri, 71: 48-53.
  • Demir B, Cetin N, Kus ZA. 2016. Determination of color properties of weed using image processing. Alinteri J Agri Sci, 2(31B): 59-64.
  • Foroud N, Mundel HH, Saindon G, Entz T. 1993. Effect of level and timing of moisture stress on soybean yield components. Irrigat Sci, 13: 149-155.
  • Huck MG, Ishihara K, Peterson CM, Ushijima T. 1983. Soybean adaptation to water stress at selected stages of growth. Plant Physiol, 73: 422-427.
  • James LG. 1988. Principles of farm irrigation system design. John Wiley & Sons, Inc., New York, US.
  • Karadol H. 2017. Determination of weeds by using image processing techniques in corn production and variable rate application. PhD Thesis, Kahramanmaras Sutcu Imam University, Institute of Science, Department of Biosystems Engineering, Kahramanmaras, Türkiye, pp: 119.
  • Karam F, Masaad R, Sfeir T, Mounzer O, Rouphael Y. 2005. Evapotranspiration and seed yield of field grown soybean under deficit irrigation conditions. Agri Water Manag, 75: 226-244.
  • Kilic K, Boyaci IH, Koksel H, Kusmenoglu I. 2007. A classification system for beans using computer vision system and artificial neural networks. J Food Eng, 78(3): 897-904.
  • Lopez FB, Chauhan YS, Johansen C. 1996b. Effects of timing of drought stress on abscission and dry matter partitioning of short-duration pigeon pea. Agronomy J, 177: 327-338.
  • Lopez FB, Johansen C, Chauhan YS. 1996a. Effects of timing of drought stress on phenology, yield and yield components of short-duration pigeon pea. Agronomy J, 177: 311-320.
  • Oya T, Nepomuceno AL, Neumaier N, Farias JRB, Tobita S, Ito O. 2004. Drought tolerance characteristics of Brazilian soybean cultivars: evaluation and characterization of drought tolerance of various Brazilian soybean cultivars in the field. Plant Prod Sci, 7: 129-137.
  • Ozel A, Acar R. 2020. Effects of sowing norm on yield in soybean (Glycine max L. Merrill). National Environ Sci Res J, 3(3): 141-147.
  • Paap AJ. 2014. Development of an optical sensor for real-time weed detection using laser based spectroscopy. URL: http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=2284&context=theses (accessed date: September 10, 2022).
  • Pedreschi F, Mery D, Mendoza F, Aguilera JM. 2004. Classification of potato chips using pattern recognition. J Food Sci, 69: 264-270.
  • Rahman M, Blackwell B, Banerjee N, Saraswat D. 2015. Smartphone-based hierarchical crowdsourcing for weed identification. Comput Elect Agri, 113: 14-23.
  • Rzanny M, Seeland M, Wäldchen J, Mäder P. 2017. Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain. Plant Methods, 13(1): 97.
  • Sabanci K, Aydin C, Unlersen MF. 2012. Determination of classification parameters of potatoes with the help of image processing and artificial neural network. Iğdır Univ J Inst Sci Tech, 2(2, Ek:A): 59-62.
  • Sabanci K, Aydin C. 2014. Using image processing and artificial neural networks to determine classification parameters of olives. J Agri Machin Sci, 10(3): 243-246.
  • Sabanci K, Ekinci S, Karahan A. M, Aydin C. 2016. Weight estimation of wheat by using image processing techniques. J Image Graph, 4(1): 51-54.
  • Sahar AK. 2017. The effect of silage additives on the silage quality and different harvesting stages on the herbage yield at soybean cultivars grown as second crop in Cukurova conditions. PhD Thesis, Yuzuncu Yil University, Institute of Science, Department of Field Crops, Van, Türkiye, pp: 138.
  • Scott HD, Ferguson JA, Wood LS. 1987. Water use, yield, and dry matter accumulation by determinate soybean grown in a humid region. Agronomy J, 79: 870-875.
  • Seckin Dinler B, Tasci E. 2020. The importance of fatty acids in oilseeds, soybean and salt tolerance. The importance of vegetable oils as valuable nutritional sources. Iksad Publications, Ankara, Türkiye, pp: 25-46.
  • Sert E. 2018. Apple classification and dimensioning system based that runs on FPGA hardware. Sci Eng J Fırat Univ, 30(2): 155-164.
  • Sharif M, Khan MA, Iqbal Z, Azam MF, Lali MIU, Javed MY. 2018. Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Elect Agri, 150: 220-234.
  • Sharma R, Kumar M, Alam MS. 2021. Image processing techniques to estimate weight and morphological parameters for selected wheat refractions. Scientific Rep, 11(1): 20953.
  • Shaw RH, Laing DR. 1966. Moisture stress and plant response. In: Pierre, W.H. (Ed.), Plant Environment and Efficient Water Use. ASA and SSSA, Madison, Wiktionary, US, pp: 73-94.
  • Sincik M, Candogan BN, Demirtas C, Buyukcangaz H, Yazgan S, Goksoy T. 2008. Deficit irrigation of soya bean [Glycine max (L.) Merr.] in a sub-humid climate. J Agronomy Crop Sci, 194(3): 200-205.
  • Sofu MM, Er O, Kayacan MC, Cetisli B. 2013. Image processing method for determination of classification and stain on apples. E-J Food Tech, 8(1): 12-25.
  • Solak S, Altinisik U. 2018. Detection and classification of hazelnut fruit by using image processing techniques and clustering methods. Sakarya Univ J Sci, 22(1): 56-65.
  • Taiz L, Zeiger E. 2008. Plant physiology. Oxford University Press, oxford, UK, pp: 782.
  • Turgut B. 2021. The effects of seed coating on yield and quality characteristics and water usage in soybean. MSc Thesis, Aydin Adnan Menderes University, Institute of Science, Department of Farm Structures and Irrigation, Aydin, Türkiye, pp: 70.
  • Turkoglu M, Hanbay K, Sarac Sivrikaya I, Hanbay D. 2020. Classification of apricot diseases by using deep convolution neural network. BEU J Sci, 9(1): 334-345.
  • Yilmaz M. 2016. Image processing techniques with identify of number of insect. MSc Thesis, Maltepe University, Institute of Science, Computer Engineering Department, Istanbul, Türkiye, pp: 53.

Estimation of Soybean Seeds Weight Using Image Processing

Yıl 2023, Cilt: 6 Sayı: 5, 511 - 515, 01.09.2023

Hayrettin Karadöl Hamza Kuzu Mualla Keten

https://doi.org/10.47115/bsagriculture.1324253

Öz

Today, image processing techniques are frequently used in irrigation, fertilization and spraying applications in order to increase agricultural input efficiency and product quality. In this study, the relationship between the image and weight of soybeans was investigated. For this purpose, some image processing applications were carried out on the images of soybeans grown with the deficit irrigation (100%, 75, 50 and 25) method. In the study, the relationship between the weight of soybeans and the number of pixels occupied on the images was 88.78%. The weights belonging to the displayed soybean grains decreased from 100% watered to 50% watered, in the 25% irrigated area, it increased again. The 25% irrigated case created significant stress for soybeans. However, as in some plants, this situation caused an increase in grain weight in soybeans.

Anahtar Kelimeler

Image processing HSV color space Soybean

Kaynakça

  • Acikgoz E, Sincik M, Wietgrefe G, Surmen M, Cecen S, Yavuz T, Erdurmus C, Goksoy AT. 2013. Dry matter accumulation and forage quality characteristics of different soybean genotypes. Turkish j Agri Forest, 37(1): 22-32.
  • Agin O, Malasli MZ. 2016. The Place and importance of image processing techniques in sustainable agriculture. J Agri Machin Sci, 12(3): 199-206.
  • Arioglu HH. 2007. The oil seed crops growing and breeding. University of Cukurova Publications, No: 220, Adana, Türkiye, pp: 142.
  • Balci M, Altun AA, Tasdemir S. 2016. Classification for Napoleon type cherries by using image processing techniques. J Selcuk-Tech, 15(3): 221-237.
  • Balkir P, Kemahlioglu K, Yucel UM. 2019. Machine vision system: food industry applications and practices. Turkish J Agri Food Sci Tech, 7: 989-999.
  • Cai X, Sun Y, Zhao Y, Damerow L, Lammers, PS, Sun W, Lin J, Zheng L, Tang Y. 2013. Smart detection of leaf wilting by 3D image processing and 2D Fourier transform. Comput Elect Agri, 90: 68-75.
  • Candogan BN. 2009. Water-yield relationships of soybean. PhD Thesis, Bursa Uludag University Institute of Science, Bursa, Türkiye, pp: 121.
  • Chen X, Xun Y, Li W, Zhang J. 2010. Combining discriminant analysis and neural networks for corn variety identification. Comput Elect Agri, 71: 48-53.
  • Demir B, Cetin N, Kus ZA. 2016. Determination of color properties of weed using image processing. Alinteri J Agri Sci, 2(31B): 59-64.
  • Foroud N, Mundel HH, Saindon G, Entz T. 1993. Effect of level and timing of moisture stress on soybean yield components. Irrigat Sci, 13: 149-155.
  • Huck MG, Ishihara K, Peterson CM, Ushijima T. 1983. Soybean adaptation to water stress at selected stages of growth. Plant Physiol, 73: 422-427.
  • James LG. 1988. Principles of farm irrigation system design. John Wiley & Sons, Inc., New York, US.
  • Karadol H. 2017. Determination of weeds by using image processing techniques in corn production and variable rate application. PhD Thesis, Kahramanmaras Sutcu Imam University, Institute of Science, Department of Biosystems Engineering, Kahramanmaras, Türkiye, pp: 119.
  • Karam F, Masaad R, Sfeir T, Mounzer O, Rouphael Y. 2005. Evapotranspiration and seed yield of field grown soybean under deficit irrigation conditions. Agri Water Manag, 75: 226-244.
  • Kilic K, Boyaci IH, Koksel H, Kusmenoglu I. 2007. A classification system for beans using computer vision system and artificial neural networks. J Food Eng, 78(3): 897-904.
  • Lopez FB, Chauhan YS, Johansen C. 1996b. Effects of timing of drought stress on abscission and dry matter partitioning of short-duration pigeon pea. Agronomy J, 177: 327-338.
  • Lopez FB, Johansen C, Chauhan YS. 1996a. Effects of timing of drought stress on phenology, yield and yield components of short-duration pigeon pea. Agronomy J, 177: 311-320.
  • Oya T, Nepomuceno AL, Neumaier N, Farias JRB, Tobita S, Ito O. 2004. Drought tolerance characteristics of Brazilian soybean cultivars: evaluation and characterization of drought tolerance of various Brazilian soybean cultivars in the field. Plant Prod Sci, 7: 129-137.
  • Ozel A, Acar R. 2020. Effects of sowing norm on yield in soybean (Glycine max L. Merrill). National Environ Sci Res J, 3(3): 141-147.
  • Paap AJ. 2014. Development of an optical sensor for real-time weed detection using laser based spectroscopy. URL: http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=2284&context=theses (accessed date: September 10, 2022).
  • Pedreschi F, Mery D, Mendoza F, Aguilera JM. 2004. Classification of potato chips using pattern recognition. J Food Sci, 69: 264-270.
  • Rahman M, Blackwell B, Banerjee N, Saraswat D. 2015. Smartphone-based hierarchical crowdsourcing for weed identification. Comput Elect Agri, 113: 14-23.
  • Rzanny M, Seeland M, Wäldchen J, Mäder P. 2017. Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain. Plant Methods, 13(1): 97.
  • Sabanci K, Aydin C, Unlersen MF. 2012. Determination of classification parameters of potatoes with the help of image processing and artificial neural network. Iğdır Univ J Inst Sci Tech, 2(2, Ek:A): 59-62.
  • Sabanci K, Aydin C. 2014. Using image processing and artificial neural networks to determine classification parameters of olives. J Agri Machin Sci, 10(3): 243-246.
  • Sabanci K, Ekinci S, Karahan A. M, Aydin C. 2016. Weight estimation of wheat by using image processing techniques. J Image Graph, 4(1): 51-54.
  • Sahar AK. 2017. The effect of silage additives on the silage quality and different harvesting stages on the herbage yield at soybean cultivars grown as second crop in Cukurova conditions. PhD Thesis, Yuzuncu Yil University, Institute of Science, Department of Field Crops, Van, Türkiye, pp: 138.
  • Scott HD, Ferguson JA, Wood LS. 1987. Water use, yield, and dry matter accumulation by determinate soybean grown in a humid region. Agronomy J, 79: 870-875.
  • Seckin Dinler B, Tasci E. 2020. The importance of fatty acids in oilseeds, soybean and salt tolerance. The importance of vegetable oils as valuable nutritional sources. Iksad Publications, Ankara, Türkiye, pp: 25-46.
  • Sert E. 2018. Apple classification and dimensioning system based that runs on FPGA hardware. Sci Eng J Fırat Univ, 30(2): 155-164.
  • Sharif M, Khan MA, Iqbal Z, Azam MF, Lali MIU, Javed MY. 2018. Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput Elect Agri, 150: 220-234.
  • Sharma R, Kumar M, Alam MS. 2021. Image processing techniques to estimate weight and morphological parameters for selected wheat refractions. Scientific Rep, 11(1): 20953.
  • Shaw RH, Laing DR. 1966. Moisture stress and plant response. In: Pierre, W.H. (Ed.), Plant Environment and Efficient Water Use. ASA and SSSA, Madison, Wiktionary, US, pp: 73-94.
  • Sincik M, Candogan BN, Demirtas C, Buyukcangaz H, Yazgan S, Goksoy T. 2008. Deficit irrigation of soya bean [Glycine max (L.) Merr.] in a sub-humid climate. J Agronomy Crop Sci, 194(3): 200-205.
  • Sofu MM, Er O, Kayacan MC, Cetisli B. 2013. Image processing method for determination of classification and stain on apples. E-J Food Tech, 8(1): 12-25.
  • Solak S, Altinisik U. 2018. Detection and classification of hazelnut fruit by using image processing techniques and clustering methods. Sakarya Univ J Sci, 22(1): 56-65.
  • Taiz L, Zeiger E. 2008. Plant physiology. Oxford University Press, oxford, UK, pp: 782.
  • Turgut B. 2021. The effects of seed coating on yield and quality characteristics and water usage in soybean. MSc Thesis, Aydin Adnan Menderes University, Institute of Science, Department of Farm Structures and Irrigation, Aydin, Türkiye, pp: 70.
  • Turkoglu M, Hanbay K, Sarac Sivrikaya I, Hanbay D. 2020. Classification of apricot diseases by using deep convolution neural network. BEU J Sci, 9(1): 334-345.
  • Yilmaz M. 2016. Image processing techniques with identify of number of insect. MSc Thesis, Maltepe University, Institute of Science, Computer Engineering Department, Istanbul, Türkiye, pp: 53.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Biyosistem
Bölüm Research Articles
Yazarlar

Hayrettin Karadöl 0000-0002-5062-0887

Hamza Kuzu 0000-0001-8585-4467

Mualla Keten 0000-0001-7741-922X

Yayımlanma Tarihi 1 Eylül 2023
Gönderilme Tarihi 8 Temmuz 2023
Kabul Tarihi 4 Ağustos 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 6 Sayı: 5

Kaynak Göster

APA Karadöl, H., Kuzu, H., & Keten, M. (2023). Estimation of Soybean Seeds Weight Using Image Processing. Black Sea Journal of Agriculture, 6(5), 511-515. https://doi.org/10.47115/bsagriculture.1324253
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