Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, Cilt: 13 Sayı: 3, 8 - 13, 26.09.2024
https://doi.org/10.46810/tdfd.1415444

Öz

Teşekkür

I extend my gratitude to Alperen Kaan Bütüner and Teledyne FLIR Türkiye for their technical support during the thermal imaging phase.

Kaynakça

  • Gundersen C, Ziliak JP. Food insecurity and health outcomes. Health affairs. 2015; 34(11): 1830-1839.
  • Zurek M, Hebinck A, Selomane O. Climate change and the urgency to transform food systems. Science 2022; 376(6600): 1416-1421.
  • Meier MS, Stoessel F, Jungbluth N, Juraske R, Schader C, Stolze M. Environmental impacts of organic and conventional agricultural products–Are the differences captured by life cycle assessment?. Journal of Environmental Management. 2015; 149: 193-208.
  • Clark M, Tilman D. Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice. Environmental Research Letters. 2017; 12(6): 064016.
  • Arora NK. Impact of climate change on agriculture production and its sustainable solutions. Environmental Sustainability. 2019; 2(2): 95-96.
  • Alengebawy A, Abdelkhalek ST, Qureshi SR, Wang MQ. Heavy metals and pesticides toxicity in agricultural soil and plants: Ecological risks and human health implications. Toxics. 2021; 9(3): 42.
  • Şahin YS, Erdinç A, Bütüner AK, Erdoğan H. Detection of Tuta absoluta larvae and their damages in tomatoes with deep learning-based algorithm. International Journal of Next-Generation Computing. 2023a.; 14(3): 555-565
  • Kurtulmuş F, Sefil K, Kargacı K, Arslan S. Bilgisayarlı görme esaslı değişken oranlı bir alev makinası için görüntü alma sisteminin optimizasyonu. Bursa Uludağ Üniversitesi Ziraat Fakültesi Dergisi. 2020; 34(1): 135-147.
  • Erdoğan H, Ünal H, Susurluk İA, Lewis EE. Precision application of the entomopathogenic nematode Heterorhabditis bacteriophora as a biological control agent through the Nemabot. Crop Protection. 2023a.; 106429.
  • Nawaz M, Mabubu JI, Hua H. Current status and advancement of biopesticides: microbial and botanical pesticides. Journal of Entomology and Zoology Studies. 2016; 4(2): 241-246.
  • Duhan JS, Kumar R, Kumar N, Kaur P, Nehra K, Duhan S. Nanotechnology: The new perspective in precision agriculture. Biotechnology Reports. 2017; 15: 11-23.
  • Samada LH, Tambunan USF. Biopesticides as promising alternatives to chemical pesticides: A review of their current and future status. Online Journal of Biological Sciences. 2020; 20(2): 66-76.
  • Puri V, Nayyar A, Raja L. Agriculture drones: A modern breakthrough in precision agriculture. Journal of Statistics and Management Systems. 2017; 20(4): 507-518.
  • Sishodia RP, Ray RL, Singh SK. Applications of remote sensing in precision agriculture: A review. Remote Sensing. 2020; 12(19): 3136.
  • Ballester C, Jiménez-Bello MA, Castel JR, Intrigliolo DS. Usefulness of thermography for plant water stress detection in citrus and persimmon trees. Agricultural and forest Meteorology. 2013; 168, 120-129.
  • Khanal S, Fulton J, Shearer S. An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture. 2017; 139, 22-32.
  • Kranner I, Kastberger G, Hartbauer M, Pritchard HW. Noninvasive diagnosis of seed viability using infrared thermography. Proceedings of the National Academy of Sciences. 2010; 107(8), 3912-3917.
  • Men S, Yan L, Liu J, Qian H, Luo Q. A classification method for seed viability assessment with infrared thermography. Sensors. 2017; 17(4), 845.
  • ElMasry G, ElGamal R, Mandour N, Gou P, Al-Rejaie S, Belin E, Rousseau D. Emerging thermal imaging techniques for seed quality evaluation: Principles and applications. Food Research International. 2020; 131, 109025.
  • Grant OM, Chaves MM, Jones HG. Optimizing thermal imaging as a technique for detecting stomatal closure induced by drought stress under greenhouse conditions. Physiologia Plantarum. 2006; 127(3), 507-518.
  • Wiriya-Alongkorn W, Spreer W, Ongprasert S, Spohrer K, Pankasemsuk T, Mueller J. Detecting drought stress in longan tree using thermal imaging. Maejo International Journal of Science and Technology. 2013; 7(1), 166.
  • Hong M, Bremer DJ, van der Merwe D. Thermal imaging detects early drought stress in turfgrass utilizing small unmanned aircraft systems. Agrosystems, Geosciences & Environment. 2019; 2(1), 1-9.
  • Bilgili A. Thermal Image Processing for Automatic Detection of Fusarium Root and Crown Rot Disease In Tomato Plants. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi. 2023; 14(4), 611-619.
  • Erdoğan H, Bütüner AK, Şahin YS. Detection of Cucurbit Powdery Mildew, Sphaerotheca fuliginea (Schlech.) Polacci by Thermal Imaging in Field Conditions. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development. 2023b; 23(1): 189-192.
  • Vadivambal R, ve Jayas DS. Applications of thermal imaging in agriculture and food industry—a review. Food and Bioprocess Technology. 2011; 4: 186-199.
  • Meola C, Carlomagno GM. Recent advances in the use of infrared thermography. Measurement Science and Technology. 2004; 15(9): R27.
  • Mutka AM, Bart RS. Image-based phenotyping of plant disease symptoms. Frontiers in plant science. 2015; 5: 734.
  • Kim J, Kweon SG, Park J, Lee H, Kim KW. Digital infrared thermal imaging of crape myrtle leaves infested with sooty mold. The Plant Pathology Journal. 2016; 32(6): 563.
  • Singh RN, Krishnan P, Singh VK, Das B. Estimation of yellow rust severity in wheat using visible and thermal imaging coupled with machine learning models. Geocarto International. 2023; 38(1): 2160831.
  • Sandlin CM, Steadman JR, Araya CM, Coyne DP. Isolates of Uromyces appendiculatus with specific virulence to landraces of Phaseolus vulgaris of Andean origin. Plant disease. 1999; 83(2): 108-113.
  • Araya CM, Alleyne AT, Steadman JR, Eskridge KM, Coyne DP. Phenotypic and genotypic characterization of Uromyces appendiculatus from Phaseolus vulgaris in the Americas. Plant Disease. 2004; 88(8): 830-836.
  • Acevedo M, Steadman JR, Rosas JC. Uromyces appendiculatus in Honduras: pathogen diversity and host resistance screening. Plant disease. 2013; 97(5): 652-661.
  • Bhairi SM, Staples RC, Freve P, Voder OC. Characterization of an infection structure-specific gene from the rust fungus Uromyces appendiculatus. Gene. 1989; 81(2): 237-243.
  • Mersha Z, Hau B. Effects of bean rust (Uromyces appendiculatus) epidemics on host dynamics of common bean (Phaseolus vulgaris). Plant pathology. 2008; 57(4): 674-686.
  • Abo-Elyousr KA, Abdel-Rahim IR, Almasoudi NM, Alghamdi SA. Native endophytic Pseudomonas putida as a biocontrol agent against common bean rust caused by Uromyces appendiculatus. Journal of Fungi. 2021; 7(9): 745.
  • Makhumbila P, Rauwane ME, Muedi HH, Madala NE, Figlan S. Metabolome profile variations in common bean (Phaseolus vulgaris L.) resistant and susceptible genotypes incited by rust (Uromyces appendiculatus). Frontiers in Genetics. 2023; 14: 1141201.
  • Sharma N, Sharma S, Gupta SK, Sharma M. Evaluation of fungicides against bean rust (Uromyces appendiculatus). Plant Disease Research. 2018; 33(2): 174-179.
  • Ishimwe R, Abutaleb K, Ahmed F. Applications of thermal imaging in agriculture—A review. Advances in Remote Sensing. 2014; 3(03): 128.
  • Şahin YS, Bütüner AK, Erdoğan H. Potentıal For Early Detectıon Of Powdery Mildew In Okra Under Field Conditions Using Thermal Imaging. Scientific Papers Series Management, Economic Engineering in Agriculture & Rural Development. 2023b; 23(3), 863-870.
  • Shaik M, Steadman JR. The effect of leaf developmental stage on the variation of resistant and susceptible reactions of Phaseolus vulgaris to Uromyces appendiculatus. Phytopathology. 1989; 79(10), 1028-1035.
  • Miller SA, Beed FD, Harmon CL. Plant disease diagnostic capabilities and networks. Annual review of phytopathology. 2009; 47, 15-38.
  • Kumari HMPS, Pastor Corrales MA, Rajapaksha RGAS, Bandaranayake PCG, Weebadde C. Characterization of Uromyces appendiculatus First Races in Sri Lanka and Identification of Genes for the Development of Rust-Resistant Snap Beans. Plant Disease. 2023; 107(8), 2431-2439.
  • Faye E, Dangles O, Pincebourde S. Distance makes the difference in thermography for ecological studies. Journal of Thermal Biology. 2016; 56: 1-9.
  • Lioy S, Bianchi E, Biglia A, Bessone M, Laurino D, Porporato M. Viability of thermal imaging in detecting nests of the invasive hornet Vespa velutina. Insect Science. 2021; 28: 271-277.
  • Chelle M. Phylloclimate or the climate perceived by individual plant organs: what is it? How to model it? What for?. New Phytologist. 2005; 166(3):781-90.
  • Chaerle L, Leinonen I, Jones HG, Van Der Straeten, D. Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. Journal of experimental botany. 2007; 58(4): 773-784.
  • Murchie EH, Lawson T. Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications. Journal of Experimental Botany. 2013; 64(13): 3983-3998.
  • Bhakta I, Phadikar S, Majumder K, Mukherjee H, Sau A. A novel plant disease prediction model based on thermal images using modified deep convolutional neural network. Precision Agriculture. 2023; 24(1), 23-39.
  • Zhu W, Chen H, Ciechanowska I, Spaner D. Application of infrared thermal imaging for the rapid diagnosis of crop disease. IFAC-PapersOnLine. 2018; 51(17): 424-430.
  • Pineda M, Barón M, Pérez-Bueno ML. Thermal imaging for plant stress detection and phenotyping. Remote Sensing. 2020; 13(1): 68-88.
  • Farber C, Mahnke M, Sanchez L, Kurouski D. Advanced spectroscopic techniques for plant disease diagnostics. A review. TrAC Trends in Analytical Chemistry. 2019; 118: 43-49.
  • Hellebrand HJ, Herppich WB, Beuche H, Dammer KH, Linke M, Flath K. Investigations of plant infections by thermal vision and NIR imaging. International Agrophysics. 2006; 20: 1-10.
  • Oerke EC, Steiner U, Dehne HW, Lindenthal M. Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. Journal of Experimental Botany. 2006; 57(9): 2121-2132.

Detection of Bean Rust (Uromyces appendiculatus) Disease Under Field Conditions Using Thermal Imaging

Yıl 2024, Cilt: 13 Sayı: 3, 8 - 13, 26.09.2024
https://doi.org/10.46810/tdfd.1415444

Öz

Among the factors causing yield losses in agricultural fields, plant diseases are known to be one of the most significant. For many years, pesticides have been used to combat these diseases. However, due to the unintended toxic effects of pesticides on non-target organisms in recent years, there have been restrictions on their usage. Therefore, there has been an increased interest in alternative methods to chemical control in combating plant diseases. Among these alternative methods, thermal imaging, widely used within the scope of precision agriculture practices, holds a significant position. This study aims to detect bean rust disease (Agent: Uromyces appendiculatus) at an early stage using thermal imaging methods. According to the obtained results, it has been determined that leaves infected with the pathogen have a temperature approximately 2 ºC lower than healthy leaves. Surface temperatures of healthy and infected leaves were measured at 60-minute intervals for three weeks. Throughout this three-week period, it was observed that the average daily temperatures of infected leaves and healthy leaves were below ambient temperatures. Thermal imaging is considered to play a crucial role in the potential early detection of plant diseases.

Kaynakça

  • Gundersen C, Ziliak JP. Food insecurity and health outcomes. Health affairs. 2015; 34(11): 1830-1839.
  • Zurek M, Hebinck A, Selomane O. Climate change and the urgency to transform food systems. Science 2022; 376(6600): 1416-1421.
  • Meier MS, Stoessel F, Jungbluth N, Juraske R, Schader C, Stolze M. Environmental impacts of organic and conventional agricultural products–Are the differences captured by life cycle assessment?. Journal of Environmental Management. 2015; 149: 193-208.
  • Clark M, Tilman D. Comparative analysis of environmental impacts of agricultural production systems, agricultural input efficiency, and food choice. Environmental Research Letters. 2017; 12(6): 064016.
  • Arora NK. Impact of climate change on agriculture production and its sustainable solutions. Environmental Sustainability. 2019; 2(2): 95-96.
  • Alengebawy A, Abdelkhalek ST, Qureshi SR, Wang MQ. Heavy metals and pesticides toxicity in agricultural soil and plants: Ecological risks and human health implications. Toxics. 2021; 9(3): 42.
  • Şahin YS, Erdinç A, Bütüner AK, Erdoğan H. Detection of Tuta absoluta larvae and their damages in tomatoes with deep learning-based algorithm. International Journal of Next-Generation Computing. 2023a.; 14(3): 555-565
  • Kurtulmuş F, Sefil K, Kargacı K, Arslan S. Bilgisayarlı görme esaslı değişken oranlı bir alev makinası için görüntü alma sisteminin optimizasyonu. Bursa Uludağ Üniversitesi Ziraat Fakültesi Dergisi. 2020; 34(1): 135-147.
  • Erdoğan H, Ünal H, Susurluk İA, Lewis EE. Precision application of the entomopathogenic nematode Heterorhabditis bacteriophora as a biological control agent through the Nemabot. Crop Protection. 2023a.; 106429.
  • Nawaz M, Mabubu JI, Hua H. Current status and advancement of biopesticides: microbial and botanical pesticides. Journal of Entomology and Zoology Studies. 2016; 4(2): 241-246.
  • Duhan JS, Kumar R, Kumar N, Kaur P, Nehra K, Duhan S. Nanotechnology: The new perspective in precision agriculture. Biotechnology Reports. 2017; 15: 11-23.
  • Samada LH, Tambunan USF. Biopesticides as promising alternatives to chemical pesticides: A review of their current and future status. Online Journal of Biological Sciences. 2020; 20(2): 66-76.
  • Puri V, Nayyar A, Raja L. Agriculture drones: A modern breakthrough in precision agriculture. Journal of Statistics and Management Systems. 2017; 20(4): 507-518.
  • Sishodia RP, Ray RL, Singh SK. Applications of remote sensing in precision agriculture: A review. Remote Sensing. 2020; 12(19): 3136.
  • Ballester C, Jiménez-Bello MA, Castel JR, Intrigliolo DS. Usefulness of thermography for plant water stress detection in citrus and persimmon trees. Agricultural and forest Meteorology. 2013; 168, 120-129.
  • Khanal S, Fulton J, Shearer S. An overview of current and potential applications of thermal remote sensing in precision agriculture. Computers and Electronics in Agriculture. 2017; 139, 22-32.
  • Kranner I, Kastberger G, Hartbauer M, Pritchard HW. Noninvasive diagnosis of seed viability using infrared thermography. Proceedings of the National Academy of Sciences. 2010; 107(8), 3912-3917.
  • Men S, Yan L, Liu J, Qian H, Luo Q. A classification method for seed viability assessment with infrared thermography. Sensors. 2017; 17(4), 845.
  • ElMasry G, ElGamal R, Mandour N, Gou P, Al-Rejaie S, Belin E, Rousseau D. Emerging thermal imaging techniques for seed quality evaluation: Principles and applications. Food Research International. 2020; 131, 109025.
  • Grant OM, Chaves MM, Jones HG. Optimizing thermal imaging as a technique for detecting stomatal closure induced by drought stress under greenhouse conditions. Physiologia Plantarum. 2006; 127(3), 507-518.
  • Wiriya-Alongkorn W, Spreer W, Ongprasert S, Spohrer K, Pankasemsuk T, Mueller J. Detecting drought stress in longan tree using thermal imaging. Maejo International Journal of Science and Technology. 2013; 7(1), 166.
  • Hong M, Bremer DJ, van der Merwe D. Thermal imaging detects early drought stress in turfgrass utilizing small unmanned aircraft systems. Agrosystems, Geosciences & Environment. 2019; 2(1), 1-9.
  • Bilgili A. Thermal Image Processing for Automatic Detection of Fusarium Root and Crown Rot Disease In Tomato Plants. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi. 2023; 14(4), 611-619.
  • Erdoğan H, Bütüner AK, Şahin YS. Detection of Cucurbit Powdery Mildew, Sphaerotheca fuliginea (Schlech.) Polacci by Thermal Imaging in Field Conditions. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development. 2023b; 23(1): 189-192.
  • Vadivambal R, ve Jayas DS. Applications of thermal imaging in agriculture and food industry—a review. Food and Bioprocess Technology. 2011; 4: 186-199.
  • Meola C, Carlomagno GM. Recent advances in the use of infrared thermography. Measurement Science and Technology. 2004; 15(9): R27.
  • Mutka AM, Bart RS. Image-based phenotyping of plant disease symptoms. Frontiers in plant science. 2015; 5: 734.
  • Kim J, Kweon SG, Park J, Lee H, Kim KW. Digital infrared thermal imaging of crape myrtle leaves infested with sooty mold. The Plant Pathology Journal. 2016; 32(6): 563.
  • Singh RN, Krishnan P, Singh VK, Das B. Estimation of yellow rust severity in wheat using visible and thermal imaging coupled with machine learning models. Geocarto International. 2023; 38(1): 2160831.
  • Sandlin CM, Steadman JR, Araya CM, Coyne DP. Isolates of Uromyces appendiculatus with specific virulence to landraces of Phaseolus vulgaris of Andean origin. Plant disease. 1999; 83(2): 108-113.
  • Araya CM, Alleyne AT, Steadman JR, Eskridge KM, Coyne DP. Phenotypic and genotypic characterization of Uromyces appendiculatus from Phaseolus vulgaris in the Americas. Plant Disease. 2004; 88(8): 830-836.
  • Acevedo M, Steadman JR, Rosas JC. Uromyces appendiculatus in Honduras: pathogen diversity and host resistance screening. Plant disease. 2013; 97(5): 652-661.
  • Bhairi SM, Staples RC, Freve P, Voder OC. Characterization of an infection structure-specific gene from the rust fungus Uromyces appendiculatus. Gene. 1989; 81(2): 237-243.
  • Mersha Z, Hau B. Effects of bean rust (Uromyces appendiculatus) epidemics on host dynamics of common bean (Phaseolus vulgaris). Plant pathology. 2008; 57(4): 674-686.
  • Abo-Elyousr KA, Abdel-Rahim IR, Almasoudi NM, Alghamdi SA. Native endophytic Pseudomonas putida as a biocontrol agent against common bean rust caused by Uromyces appendiculatus. Journal of Fungi. 2021; 7(9): 745.
  • Makhumbila P, Rauwane ME, Muedi HH, Madala NE, Figlan S. Metabolome profile variations in common bean (Phaseolus vulgaris L.) resistant and susceptible genotypes incited by rust (Uromyces appendiculatus). Frontiers in Genetics. 2023; 14: 1141201.
  • Sharma N, Sharma S, Gupta SK, Sharma M. Evaluation of fungicides against bean rust (Uromyces appendiculatus). Plant Disease Research. 2018; 33(2): 174-179.
  • Ishimwe R, Abutaleb K, Ahmed F. Applications of thermal imaging in agriculture—A review. Advances in Remote Sensing. 2014; 3(03): 128.
  • Şahin YS, Bütüner AK, Erdoğan H. Potentıal For Early Detectıon Of Powdery Mildew In Okra Under Field Conditions Using Thermal Imaging. Scientific Papers Series Management, Economic Engineering in Agriculture & Rural Development. 2023b; 23(3), 863-870.
  • Shaik M, Steadman JR. The effect of leaf developmental stage on the variation of resistant and susceptible reactions of Phaseolus vulgaris to Uromyces appendiculatus. Phytopathology. 1989; 79(10), 1028-1035.
  • Miller SA, Beed FD, Harmon CL. Plant disease diagnostic capabilities and networks. Annual review of phytopathology. 2009; 47, 15-38.
  • Kumari HMPS, Pastor Corrales MA, Rajapaksha RGAS, Bandaranayake PCG, Weebadde C. Characterization of Uromyces appendiculatus First Races in Sri Lanka and Identification of Genes for the Development of Rust-Resistant Snap Beans. Plant Disease. 2023; 107(8), 2431-2439.
  • Faye E, Dangles O, Pincebourde S. Distance makes the difference in thermography for ecological studies. Journal of Thermal Biology. 2016; 56: 1-9.
  • Lioy S, Bianchi E, Biglia A, Bessone M, Laurino D, Porporato M. Viability of thermal imaging in detecting nests of the invasive hornet Vespa velutina. Insect Science. 2021; 28: 271-277.
  • Chelle M. Phylloclimate or the climate perceived by individual plant organs: what is it? How to model it? What for?. New Phytologist. 2005; 166(3):781-90.
  • Chaerle L, Leinonen I, Jones HG, Van Der Straeten, D. Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. Journal of experimental botany. 2007; 58(4): 773-784.
  • Murchie EH, Lawson T. Chlorophyll fluorescence analysis: a guide to good practice and understanding some new applications. Journal of Experimental Botany. 2013; 64(13): 3983-3998.
  • Bhakta I, Phadikar S, Majumder K, Mukherjee H, Sau A. A novel plant disease prediction model based on thermal images using modified deep convolutional neural network. Precision Agriculture. 2023; 24(1), 23-39.
  • Zhu W, Chen H, Ciechanowska I, Spaner D. Application of infrared thermal imaging for the rapid diagnosis of crop disease. IFAC-PapersOnLine. 2018; 51(17): 424-430.
  • Pineda M, Barón M, Pérez-Bueno ML. Thermal imaging for plant stress detection and phenotyping. Remote Sensing. 2020; 13(1): 68-88.
  • Farber C, Mahnke M, Sanchez L, Kurouski D. Advanced spectroscopic techniques for plant disease diagnostics. A review. TrAC Trends in Analytical Chemistry. 2019; 118: 43-49.
  • Hellebrand HJ, Herppich WB, Beuche H, Dammer KH, Linke M, Flath K. Investigations of plant infections by thermal vision and NIR imaging. International Agrophysics. 2006; 20: 1-10.
  • Oerke EC, Steiner U, Dehne HW, Lindenthal M. Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. Journal of Experimental Botany. 2006; 57(9): 2121-2132.
Toplam 53 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Biyosistem
Bölüm Makaleler
Yazarlar

Hilal Erdoğan 0000-0002-0387-2600

Yayımlanma Tarihi 26 Eylül 2024
Gönderilme Tarihi 5 Ocak 2024
Kabul Tarihi 8 Temmuz 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 13 Sayı: 3

Kaynak Göster

APA Erdoğan, H. (2024). Detection of Bean Rust (Uromyces appendiculatus) Disease Under Field Conditions Using Thermal Imaging. Türk Doğa Ve Fen Dergisi, 13(3), 8-13. https://doi.org/10.46810/tdfd.1415444
AMA Erdoğan H. Detection of Bean Rust (Uromyces appendiculatus) Disease Under Field Conditions Using Thermal Imaging. TDFD. Eylül 2024;13(3):8-13. doi:10.46810/tdfd.1415444
Chicago Erdoğan, Hilal. “Detection of Bean Rust (Uromyces Appendiculatus) Disease Under Field Conditions Using Thermal Imaging”. Türk Doğa Ve Fen Dergisi 13, sy. 3 (Eylül 2024): 8-13. https://doi.org/10.46810/tdfd.1415444.
EndNote Erdoğan H (01 Eylül 2024) Detection of Bean Rust (Uromyces appendiculatus) Disease Under Field Conditions Using Thermal Imaging. Türk Doğa ve Fen Dergisi 13 3 8–13.
IEEE H. Erdoğan, “Detection of Bean Rust (Uromyces appendiculatus) Disease Under Field Conditions Using Thermal Imaging”, TDFD, c. 13, sy. 3, ss. 8–13, 2024, doi: 10.46810/tdfd.1415444.
ISNAD Erdoğan, Hilal. “Detection of Bean Rust (Uromyces Appendiculatus) Disease Under Field Conditions Using Thermal Imaging”. Türk Doğa ve Fen Dergisi 13/3 (Eylül 2024), 8-13. https://doi.org/10.46810/tdfd.1415444.
JAMA Erdoğan H. Detection of Bean Rust (Uromyces appendiculatus) Disease Under Field Conditions Using Thermal Imaging. TDFD. 2024;13:8–13.
MLA Erdoğan, Hilal. “Detection of Bean Rust (Uromyces Appendiculatus) Disease Under Field Conditions Using Thermal Imaging”. Türk Doğa Ve Fen Dergisi, c. 13, sy. 3, 2024, ss. 8-13, doi:10.46810/tdfd.1415444.
Vancouver Erdoğan H. Detection of Bean Rust (Uromyces appendiculatus) Disease Under Field Conditions Using Thermal Imaging. TDFD. 2024;13(3):8-13.