Derleme
BibTex RIS Kaynak Göster

Yıl 2025, Cilt: 6 Sayı: 1, 89 - 117, 30.06.2025

Mohammad Muzamil , Rizwan Ul Zama Banday , Danish Gul , Seemi Lohani , Sehreen Rasool , Kezia Rajan , Muzamil Hamid Wani

https://doi.org/10.46592/turkager.1592116

Öz

Kaynakça

  • Afzaal H, Farooque AA, Schumann AW, Hussain N, McKenzie-Gopsill A, Esau T and Acharya B (2021). Detection of a potato disease (early blight) using artificial intelligence. Remote Sensing, 13(3): 411.
  • Ahmed S, Rahman M and Hossain M (2021). Evaluation of millet varieties under drought conditions using traditional phenotyping methods. Journal of Agricultural Science, 13(2): 85-94.
  • Alonso J, Perez L and Gomez M (2020). Conservation and characterization of heirloom vegetable varieties using traditional phenotyping approaches. Genetic Resources and Crop Evolution, 67(5): 1023-1035.
  • Araus JL, Serret MD and Edmeades GO (2012). Phenotyping maize for adaptation to drought. Frontiers in physiology, 3, 305.
  • Araus JL and Cairns JE (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends in plant science, 19(1): 52-61.
  • Araus JL, Buchaillot ML and Kefauver SC (2022). High Throughput Field Phenotyping. In Wheat Improvement: Food Security in a Changing Climate (pp. 495-512). Cham: Springer International Publishing.
  • Arya S, Sandhu KS, Singh J and Kumar S (2022). Deep learning: As the new frontier in high-throughput plant phenotyping. Euphytica, 218: (4), 47.
  • Asaari MSM, Mertens S, Dhondt S, Inzé D, Wuyts N and Scheunders P (2019). Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform. Computers and Electronics in Agriculture, 162: 749-758.
  • Atefi A, Ge Y, Pitla S and Schnable J (2021). Robotic technologies for high-throughput plant phenotyping: Contemporary reviews and future perspectives. Frontiers in plant science, 12, 611940.
  • Bailey-Serres J, Parker JE, Ainsworth EA, Oldroyd GE and Schroeder JI (2019). Genetic strategies for improving crop yields. Nature, 575 (7781): 109-118.
  • Balota M and Oakes J (2017). UAV remote sensing for phenotyping drought tolerance in peanuts. In Autonomous air and ground sensing systems for agricultural optimization and phenotyping II (Vol. 10218, pp. 81-87). SPIE.
  • Banerjee K, Krishnan P and Das B (2020). Thermal imaging and multivariate techniques for characterizing and screening wheat genotypes under water stress condition. Ecological Indicators, 119, 106829.
  • Barbosa BDS, Costa L, Ampatzidis Y, Vijayakumar V and dos Santos LM (2021). UAV-based coffee yield prediction utilizing feature selection and deep learning. Smart Agricultural Technology, 1, 100010.
  • Bhandari M, Baker S, Rudd JC, Ibrahim AM, Chang A, Xue Q and Auvermann B (2021). Assessing the effect of drought on winter wheat growth using unmanned aerial system (UAS)-based phenotyping. Remote Sensing, 13(6): 1144.
  • Blanchy G, Deroo W, De Swaef T, Lootens P, Quataert P, Roldán-Ruíz I and Garré S (2024). Closing the phenotyping gap with non-invasive below ground field phenotyping. EGUsphere, 1-30.
  • Bohra A, Naik SJ, Kumari A, Tiwari A and Joshi R (2021). Integrating phenomics with breeding for climate-smart agriculture. Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), 1-24.
  • Brown D and Miller S (2019). The enduring relevance of traditional phenotyping in the era of precision agriculture. Plant Science Today, 6(3): 200-207.
  • Cabrera-Bosquet L, Fournier C, et al (2020). Advances in HTP and its application in breeding. Journal of Experimental Botany, 71(16), 4687-4705.
  • Cao HT (2018). A Low-Cost Depth Imaging Mobile Platform for Canola Phenotyping (Doctoral dissertation, University of Saskatchewan).
  • Centorame L, Gasperini T, Ilari A, Del Gatto A and Foppa Pedretti E (2024). An overview of machine learning applications on plant phenotyping, with a focus on sunflower. Agronomy, 14(4): 719.
  • Chaturvedi SK, Sekhar R, Banerjee S and Kamal H (2019). Comparative review study of military and civilian Unmanned Aerial Vehicle. INCAS Bulletin, 11(3): 183-198.
  • Chawade A, van Ham J, Blomquist H, Bagge O, Alexandersson E and Ortiz R (2019). High-throughput field-phenotyping tools for plant breeding and precision agriculture. Agronomy, 9(5): 258.
  • Chenu K, Van Oosterom EJ, McLean G, Deifel KS, Fletcher A, Geetika G and Hammer GL (2018). Integrating modelling and phenotyping approaches to identify and screen complex traits: transpiration efficiency in cereals. Journal of experimental botany, 69(13): 3181-3194.
  • Costa JM, Marques da Silva J, Pinheiro C, Barón M, Mylona P, Centritto, M and Oliveira MM (2019). Opportunities and limitations of crop phenotyping in southern European countries. Frontiers in plant science, 10: 1125.
  • Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, De Los Campos G, and Varshney RK (2017). Genomic selection in plant breeding: methods, models, and perspectives. Trends in plant science, 22(11): 961-975.
  • Cvejić S, Jocić S, Mitrović B, Bekavac G, Mirosavljević M, Jeromela A M and Miladinović D (2022). Innovative Approaches in the Breeding of Climate‐Resilient Crops. Climate Change and Agriculture: Perspectives, Sustainability and Resilience, 111-156.
  • Daviet B, Fernandez R, Cabrera-Bosquet L, Pradal C and Fournier C (2022). PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time. Plant Methods, 18(1): 130.
  • Deery D, Jimenez-Berni J, Jones H, Sirault X and Furbank R (2014). Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping. Vol. 4.
  • Deulkar SS and Barve SS (2018). An automated tomato quality grading using clustering based support vector machine. In 2018 3rd International Conference on Communication and Electronics Systems (ICCES) (pp. 1128-1133). IEEE.
  • Diaz-Garcia G, Lozoya-Saldaña H, Bamberg J and Diaz-Garcia L (2024). Morphometric analysis of wild potato leaves. Genetic Resources and Crop Evolution, 1-16.
  • Dogan A, Uyak C, Keskin N, Akcay A, Sensoy R I G and Ercisli S (2018). Grapevine leaf area measurements by using pixel values. Comptes rendus de l’Acade'mie bulgare des Sciences, 71(6):772-779
  • Dwivedi SL, Goldman I, Ceccarelli S and Ortiz R (2020). Advanced analytics, phenomics and biotechnology approaches to enhance genetic gains in plant breeding. Advances in agronomy, 162: 89-142.
  • Feng L, Chen S, Zhang C, Zhang Y and He Y (2021). A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping. Computers and electronics in agriculture, 182, 106033.
  • Fernandez A, Lopez R and Martinez J (2020). Phenotypic plasticity in response to light variability in understory plants assessed through traditional measurements. Ecology and Evolution, 10(15): 8421-8432.
  • Fiorani F and Schurr U (2019). Future scenarios for plant phenotyping. Annual review of plant biology, 64(1): 267-291.
  • Fisher M, Abate T, Lunduka RW, Asnake W, Alemayehu Y and Madulu RB (2015). Drought tolerant maize for farmer adaptation to drought in sub-Saharan Africa: Determinants of adoption in eastern and southern Africa. Climatic Change, 133: 283-299.
  • Furbank RT and Tester M (2011). Phenomics-technologies to relieve the phenotyping bottleneck. Trends in plant science, 16(12): 635-644.
  • Ge Y, Bai G, Stoerger V and Schnable JC (2016). Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging. Computers and Electronics in Agriculture, 127: 625-632.
  • Gill T, Gill SK, Saini DK, Chopra Y, de Koff J P and Sandhu KS (2022). A comprehensive review of high throughput phenotyping and machine learning for plant stress phenotyping. Phenomics, 2(3): 156-183.
  • Gupta N, Sharma R and Verma S (2020). Monitoring soybean development stages through manual observation for improved crop management. Agricultural Research, 9(4): 541-550.
  • Hatem Y, Hammad G and Safwat G (2022). Artificial intelligence for plant genomics and crop improvement. Egyptian Journal of Botany, 62(2): 291-303.
  • He S, Li X, Chen M, Xu X, Tang F, Gong T and Liu W (2024). Crop HTP Technologies: Applications and Prospects. Agriculture, 14(5): 723.
  • Hu Y and Schmidhalter U (2023). Opportunity and challenges of phenotyping plant salt tolerance. Trends in plant science, 28(5): 552-566.
  • Janni M and Pieruschka R (2022). Plant phenotyping for a sustainable future. Journal of Experimental Botany, 73(15): 5085-5088.
  • Jimenez-Berni JA, David MD, Rozas-Larraondo, Anthony (Tony) G, Condon GJR, Richard AJ, William DB, Robert TF and Xavier RRS (2018). High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR. Frontiers in Plant Science 9:1-18.
  • Kamarianakis Z, Perdikakis S, Daliakopoulos IN, Papadimitriou DM and Panagiotakis S (2024). Design and Implementation of a Low-Cost, Linear Robotic Camera System, Targeting Greenhouse Plant Growth Monitoring. Future Internet, 16(5): 145.
  • Kamilaris A and Prenafeta-Boldú FX (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147: 70-90.
  • Karaca M and Ince AG (2019). Conservation of biodiversity and genetic resources for sustainable agriculture. Innovations In Sustainable Agriculture, 363-410.
  • Karunathilake EMBM, Le AT, Heo S, Chung YS and Mansoor S (2023). The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture, 13(8): 1593.
  • Kaur S, Kakani VG, Carver B, Jarquin D and Singh A (2024). Hyperspectral imaging combined with machine learning for high‐throughput phenotyping in winter wheat. The Plant Phenome Journal, 7(1), e20111.
  • Kim Y, Still CJ, Roberts DA and Goulden ML (2018). Thermal infrared imaging of conifer leaf temperatures: comparison to thermocouple measurements and assessment of environmental influences. Agricultural and Forest Meteorology, 248: 361-371.
  • Kim J and Lee S (2019). Sensory evaluation methods in assessing tea quality: A traditional approach. Food Quality and Preference, 71: 87-95.
  • Kim KD, Kang Y and Kim C (2020). Application of genomic big data in plant breeding: Past, present, and future. Plants, 9(11): 1454.
  • Kotal A, Elluri L, Gupta D, Mandalapu V and Joshi A (2023). Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis. In 2023 IEEE International Conference on Big Data (BigData) (pp. 5519-5528). IEEE.
  • Krause MR, González-Pérez L, Crossa J, Pérez-Rodríguez P, Montesinos-López O, Singh RP and Mondal S (2019). Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat. G3: Genes, Genomes, Genetics, 9(4): 1231-1247.
  • Kuriakose SV, Pushker R and Hyde EM (2020). Data-driven decisions for accelerated plant breeding. Accelerated Plant Breeding, Volume 1: Cereal Crops, 89-119.
  • Kurihara J, Nagata T and Tomiyama H (2023). Rice Yield Prediction in Different Growth Environments Using Unmanned Aerial Vehicle-Based Hyperspectral Imaging. Remote Sensing, 15(8): 2004.
  • Lacerda LN, Snider JL, Cohen Y, Liakos V, Gobbo S and Vellidis G (2022). Using UAV-based thermal imagery to detect crop water status variability in cotton. Smart Agricultural Technology, 2, 100029.
  • Lajoie-O'Malley A, Bronson K, van der Burg S and Klerkx L (2020). The future (s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents. Ecosystem Services, 45, 101183.
  • Lassoued R, Macall DM, Smyth SJ, Phillips PW and Hesseln H (2021). Data challenges for future plant gene editing: expert opinion. Transgenic Research, 30(6): 765-780.
  • Le Ru A, Ibarcq G, Boniface MC, Baussart A, Muños S and Chabaud M (2021). Image analysis for the automatic phenotyping of Orobanche cumana tubercles on sunflower roots. Plant methods, 17: 1-14.
  • Lee SH, Goëau H, Bonnet P and Joly A (2020). New perspectives on plant disease characterization based on deep learning. Computers and Electronics in Agriculture, 170, 105220.
  • Li D, Quan C, Song Z, Li X, Yu G, Li C and Muhammad A (2021). High-throughput plant phenotyping platform (HT3P) as a novel tool for estimating agronomic traits from the lab to the field. Frontiers in Bioengineering and Biotechnology, 8, 623705.
  • Li J, Shi Y, Veeranampalayam-Sivakumar A N and Schachtman D P (2018). Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system. Frontiers in plant science, 9, 1406.
  • Li L, Zhang Q and Huang D (2014). A review of imaging techniques for plant phenotyping. Sensors, 14(11): 20078-20111.
  • Li X, Chen M, He S, Xu X, He L, Wang L and Yang W (2024). Estimation of soybean yield based on high-throughput phenotyping and machine learning. Frontiers in Plant Science, 15, 1395760.
  • Lipper L, Thornton P, Campbell BM, Baedeker T, Braimoh A, Bwalya M and Torquebiau EF (2017). Climate-smart agriculture for food security. Nature climate change, 4(12): 1068-1072.
  • Luo S, Liu W, Zhang Y, Wang C, Xi X, Nie S and Zhou G (2021). Maize and soybean heights estimation from unmanned aerial vehicle (UAV) LiDAR data. Computers and Electronics in Agriculture, 182, 106005.
  • Ma Z, Rayhana R, Feng K, Liu Z, Xiao G, Ruan Y and Sangha JS (2022). A review on sensing technologies for high-throughput plant phenotyping. IEEE Open Journal of Instrumentation and Measurement, 1: 1-21.
  • Mahlein AK, Kuska MT, Behmann J, Polder G and Walter A (2018). Hyperspectral sensors and imaging technologies in phytopathology: state of the art. Annual review of phytopathology, 56(1): 535-558.
  • Mao Y, You C, Zhang J, Huang K and Letaief KB (2017). A survey on mobile edge computing: The communication perspective. IEEE communications surveys & tutorials, 19(4): 2322-2358.
  • Maqbool S, Hassan MA, Xia X, York LM, Rasheed A and He Z (2022). Root system architecture in cereals: progress, challenges and perspective. The Plant Journal, 110(1): 23-42.
  • Marwaha S, Deb CK, Haque MA, Naha S and Maji AK (2023). Application of Artificial Intelligence and Machine Learning in Agriculture. In Translating Physiological Tools to Augment Crop Breeding (pp. 441-457). Singapore: Springer Nature Singapore.
  • Mir RR, Reynolds M, Pinto F, Khan MA and Bhat MA (2019). High-throughput phenotyping for crop improvement in the genomics era. Plant Science, 282: 60-72.
  • Montesinos-López OA, Montesinos-López A, Pérez-Rodríguez P, Barrón-López JA, Martini JW, Fajardo-Flores SB and Crossa J (2021). A review of deep learning applications for genomic selection. BMC genomics, 22: 1-23.
  • Müller J and Becker H (2020). Manual phenotyping of sorghum under abiotic stress conditions for breeding resilience. Journal of Agronomy and Crop Science, 206(5): 609-620.
  • Muzamil M, Rasool S and Ummyiah HM (2022). Insitu and Exsitu agricultural waste management system, 45-63. Agricultural waste-New Insights. IntechOpen. ISBN: 978-1- 80356-966-6.
  • Nabwire S, Suh HK, Kim MS, Baek I and Cho BK (2021). Application of artificial intelligence in phenomics. Sensors, 21(13): 4363.
  • Nakhforoosh A, Hallin E, Karunakaran C, Korbas M, Stobbs J and Kochian L (2024). Visualization and Quantitative Evaluation of Functional Structures of Soybean Root Nodules via Synchrotron X-ray Imaging. Plant Phenomics, 6, 0203.
  • Nguyen T, Tran H and Le P (2019). Traditional assessment of salinity stress responses in barley seedlings under controlled conditions. Plant Physiology and Biochemistry, 141: 259-266.
  • Nguyen LQ, Shin J, Ryu S, Dang LM, Park HY, Lee ON and Moon H (2023). Innovative Cucumber Phenotyping: A Smartphone-Based and Data-Labeling-Free Model. Electronics, 12(23): 4775.
  • Oliveira R, Santos L and Pereira J (2021). Rapid assessment of flood damage in rice fields using traditional field surveys. Natural Hazards, 107(1): 495-508.
  • Panday US, Pratihast AK, Aryal J and Kayastha RB (2020). A review on drone-based data solutions for cereal crops. Drones, 4(3): 41.
  • Papoutsoglou EA, Faria D, Arend D, Arnaud E, Athanasiadis IN, Chaves I and Pommier C (2020). Enabling reusability of plant phenomic datasets with MIAPPE 1.1. New Phytologist, 227(1): 260-273.
  • Park S, Lee J and Choi H (2020). Validation of high-throughput phenotyping data through traditional measurement techniques in wheat. Plant Methods, 16(1): 83.
  • Pieruschka R and Schurr U (2019). Plant phenotyping: past, present, and future. Plant Phenomics.
  • Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS and French AP (2017). Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. Gigascience, 6(10), gix083.
  • Reynolds D, Baret F, Welcker C, Bostrom A, Ball J, Cellini F and Tardieu F (2019). What is cost-efficient phenotyping? Optimizing costs for different scenarios. Plant Science, 282: 14-22.
  • Reynolds M, Chapman S, Crespo-Herrera L, Molero G, Mondal S, Pequeno D N and Sukumaran S (2020). Breeder friendly phenotyping. Plant Science, 295, 110396.
  • Rico-Chávez AK, Franco JA, Fernandez-Jaramillo AA, Contreras-Medina LM, Guevara-González RG and Hernandez-Escobedo Q (2022). Machine learning for plant stress modeling: A perspective towards hormesis management. Plants, 11(7): 970.
  • Roberts DA, Roth KL, Wetherley EB, Meerdink SK and Perroy RL (2018). Hyperspectral vegetation indices. In Hyperspectral indices and image classifications for agriculture and vegetation (pp. 3-26). CRC press.
  • Rodriguez M, Hernandez L and Gomez R (2019). Participatory breeding in maize: The continued relevance of traditional phenotyping methods. Agriculture and Human Values, 36(4): 799-810.
  • Roitsch T, Cabrera-Bosquet L, Fournier A, Ghamkhar K, Jiménez-Berni J, Pinto F and Ober E S (2019). New sensors and data-driven approaches-A path to next generation phenomics. Plant Science, 282: 2-10.
  • Rose DC, Wheeler R, Winter M, Lobley M and Chivers CA (2021). Agriculture 4.0: Making it work for people, production, and the planet. Land use policy, 100, 104933.
  • Ruzzante S, Labarta R and Bilton A (2021). Adoption of agricultural technology in the developing world: A meta-analysis of the empirical literature. World Development, 146, 105599.
  • Ryan M (2023). The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature. AI & SOCIETY, 38(6): 2473-2485.
  • Sahoo L, Mohapatra D, Raghuvanshi HR, Kumar S, Kaur R, Chawla R and Afreen N (2024). Transforming Agriculture through Artificial Intelligence: Advancements in Plant Disease Detection, Applications, and Challenges. Journal of Advances in Biology & Biotechnology, 27(5): 381-388.
  • Sarić R, Nguyen V D, Burge T, Berkowitz O, Trtílek M, Whelan J and Čustović E (2022). Applications of hyperspectral imaging in plant phenotyping. Trends in plant science, 27(3): 301-315.
  • Satbhai SB, Göschl C and Busch W (2017). Automated high-throughput root phenotyping of Arabidopsis thaliana under nutrient deficiency conditions. Plant Genomics: Methods and Protocols, 135-153.
  • Shakoor N, Lee, S and Mockler TC (2017). High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Current opinion in plant biology, 38: 184-192.
  • Shakshi RP, Yadav S and Singh MK (2024). Integrating Genomics and Phenomics in Agricultural Breeding: A Comprehensive Review. Asian Research Journal of Agriculture,17(4):116-125.
  • Shi Y, Zhu Y, Wang X, Sun X, Ding Y, Cao W and Hu Z (2020). Progress and development on biological information of crop phenotype research applied to real-time variable-rate fertilization. Plant methods, 16: 1-15.
  • Shi Y, Thomasson JA, Murray SC, Pugh NA, Rooney WL, Shafian S and Yang C (2021). Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PloS one, 11(7), e0159781.
  • Shi T, Liu Y, Zheng X, Hu K, Huang H, Liu H and Huang H (2023). Recent advances in plant disease severity assessment using convolutional neural networks. Scientific Reports, 13(1): 2336.
  • Sinesio F, Cammareri M, Cottet V, Fontanet L, Jost M, Moneta E and Grandillo S (2021). Sensory traits and consumer’s perceived quality of traditional and modern fresh market tomato varieties: a study in three European countries. Foods, 10(11): 2521.
  • Singh A, Ganapathysubramanian B, Singh A K and Sarkar S (2016). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 21(2): 110-124.
  • Singh D, Bhatia S and Rehman A (2021). Big data analytics for phenotyping: Tackling the challenge of high-throughput data. Computers and Electronics in Agriculture, 182: 106-111.
  • Singh AK (2022). Precision agriculture in india-opportunities and challenges. Indian Journal of Fertilisers, 18(4): 308-331.
  • Stanghellini G and Leoni F (2020). Digital phenotyping: Ethical issues, opportunities, and threats. Frontiers in Psychiatry, 11: 473.
  • Sweet DD, Tirado SB, Springer NM, Hirsch CN and Hirsch CD (2022). Opportunities and challenges in phenotyping row crops using drone‐based RGB imaging. The Plant Phenome Journal, 5(1), e20044.
  • Tahir MN, Lan Y, Zhang Y, Wang Y, Nawaz F, Shah MAA and Naqvi SZA (2020). Real time estimation of leaf area index and groundnut yield using multispectral UAV. International Journal of Precision Agricultural Aviation, 3(1).
  • Tanaka TST, Wang S, Jørgensen JR, Gentili M, Vidal AZ, Mortensen AK and Gislum R (2024). Review of Crop Phenotyping in Field Plot Experiments Using UAV-Mounted Sensors and Algorithms. Drones, 8(6): 212.
  • Tanger P, Klassen S, Mojica JP, Lovell JT, Moyers BT, Baraoidan M and McKay JK (2017). Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. Scientific Reports, 7(1): 42839.
  • Tardieu F, Cabrera-Bosquet L, Pridmore T and Bennett M (2017). Plant phenomics, from sensors to Aknowledge. Current Biology, 27(15): R770-R783.
  • Thompson G and Carter J (2019). Traditional bioassays for detecting herbicide resistance in weed populations. Weed Science, 67(4): 498-507.
  • Thorp KR, Thompson AL, Harders SJ, French AN and Ward RW (2018). High-throughput phenotyping of crop water use efficiency via multispectral drone imagery and a daily soil water balance model. Remote Sensing, 10(11): 1682.
  • Thrash T, Lee H and Baker RL (2022). A low‐cost high‐throughput phenotyping system for automatically quantifying foliar area and greenness. Applications in Plant Sciences, 10(6), e11502.
  • Tomičić A, Malešević A and Čartolovni A (2022). Ethical, legal and social issues of digital phenotyping as a future solution for present-day challenges: A scoping review. Science and Engineering Ethics, 28: 1-25.
  • Tong H and Nikoloski Z (2021). Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. Journal of plant physiology, 257, 153354.
  • Tsaftaris S, Minervini M and Scharr H (2016). Machine learning for plant phenotyping needs image processing. Trends in plant science, 21(12): 989-991.
  • Walter A, Scharr H, Gilmer F, Zierer R, Nagel K A, Ernst M and Schurr U (2019). Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species. New Phytologist, 174(2): 447-455.
  • Wang Y, Wen W, Wu S, Wang C, Yu Z, Guo X and Zhao C (2018). Maize plant phenotyping: comparing 3D laser scanning, multi-view stereo reconstruction, and 3D digitizing estimates. Remote Sensing, 11(1): 63.
  • Wang X, Zhou X, Ji L and Shen K (2024). Artificial intelligence/machine learning-assisted near-infrared/optical biosensing for plant phenotyping. In Machine Learning and Artificial Intelligence in Chemical and Biological Sensing (pp. 203-225).
  • Wasaya A, Zhang X, Fang Q and Yan Z (2018). Root phenotyping for drought tolerance: a review. Agronomy, 8(11): 241.
  • Washburn JD, Adak A, DeSalvio AJ, Arik MA and Murray SC (2024). High temporal resolution unoccupied aerial systems phenotyping provides unique information between flight dates. The Plant Phenome Journal, 7(1): e20113.
  • Williams E, Brown S and Miller D (2019). Incorporating traditional phenotyping techniques in plant science education. Journal of Natural Resources and Life Sciences Education, 48(1): 45-52.
  • Wu D, Guo Z, Ye J, Feng H, Liu J, Chen G and Yang W (2019). Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice. Journal of Experimental Botany, 70(2): 545-561.
  • Xiao Q, Bai X, Zhang C and He Y (2022). Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review. Journal of advanced research, 35: 215-230.
  • Xie T, Li J, Yang C, Jiang Z, Chen Y, Guo L and Zhang J (2021). Crop height estimation based on UAV images: Methods, errors, and strategies. Computers and Electronics in Agriculture, 185, 106155.
  • Xue J and Su B (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of sensors, 2017(1), 1353691.
  • Yang W, Feng H, Zhang X, Zhang J, Doonan J H, Batchelor W D and Yan J (2020). Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives. Molecular plant, 13(2): 187-214.
  • Yang B, Zhu W, Rezaei EE, Li J, Sun Z and Zhang J (2022). The optimal phenological phase of maize for yield prediction with high-frequency UAV remote sensing. Remote Sensing, 14(7): 1559.
  • Yao L, Van De Zedde R and Kowalchuk G (2021). Recent developments and potential of robotics in plant eco-phenotyping. Emerging topics in life sciences, 5(2): 289-300.
  • Young SN, Kayacan E and Peschel JM (2019). Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum. Precision Agriculture, 20(4): 697-722.
  • Yuan H, Song M, Liu Y, Xie Q, Cao W, Zhu Y and Ni J (2023). Field Phenotyping Monitoring Systems for High-Throughput: A Survey of Enabling Technologies, Equipment, and Research Challenges. Agronomy, 13(11): 2832.
  • Zaman-Allah M, Vergara O, Araus JL, Tarekegne A, Magorokosho C, Zarco-Tejada PJ and Cairns J (2015). Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant methods, 11: 1-10.
  • Zhang L, Zhao H and Li D (2021). Integrating traditional and modern phenotyping methods for comprehensive plant trait analysis. Frontiers in Plant Science, 12: 640.
  • Zhang H, Wang L, Jin X, Bian L and Ge Y (2023). High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing. The Crop Journal, 11(5): 1303-1318.
  • Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S and Fan J (2019). Crop phenomics: current status and perspectives. Frontiers in Plant Science, 10: 714.
  • Zhou RQ, Jin JJ, Li QM, Su ZZ, Yu XJ, Tang Y and Li XL (2019). Early detection of magnaporthe oryzae- infected barley leaves and lesion visualization based on hyperspectral imaging. Frontiers in Plant Science, 9: 1962.
  • Zhu W, Chen H, Ciechanowska I and Spaner D (2018). Application of infrared thermal imaging for the rapid diagnosis of crop disease. IFAC-PapersOnLine, 51(17): 424-430.

A Critical Review on ‘Current Status and Future Implications of Advanced Phenotyping Systems for Monitoring of Agricultural Crops’

Yıl 2025, Cilt: 6 Sayı: 1, 89 - 117, 30.06.2025

Mohammad Muzamil , Rizwan Ul Zama Banday , Danish Gul , Seemi Lohani , Sehreen Rasool , Kezia Rajan , Muzamil Hamid Wani

https://doi.org/10.46592/turkager.1592116

Öz

Phenotyping systems propels the growth of modern agriculture, driving innovations in plant breeding, crop management, precise application of resources and smart agriculture. This review provides a comprehensive analysis of phenotyping systems, exploring their status, technological advancements, challenges and future directions. The evolution from traditional phenotyping to high-throughput phenotyping (HTP) systems with involvement of advanced imaging (visible, infrared, hyperspectral, and thermal), sensors (LIDAR and NIR), data analytics, drones and automated platforms have enabled rapid non-invasive collection of phenotypic information, significantly hastening breeding programs and improving stress tolerance studies. The integration of big data, artificial intelligence (AI) and machine learning (ML) has enhanced data management and interpretation, enabling the development of predictive models and real-time decision-making tools. Despite these advancements, several challenges persist. The technical issues such as data accuracy, resolution and consistency alongside economic concerns related to high cost of implementation, limits the widespread adoption of advanced phenotyping technologies, especially among smallholder farmers. Furthermore, the integration of these technologies with traditional farming practices and the handling of large datasets raises concerns about data privacy, ownership and interpretation. The impending growth of phenotyping lies in advancements such as the integration of AI and genomics, enabling more precise breeding through the linking of genetic information with phenotypic traits. Additionally, the development of low-cost systems is essential to democratize access to precision agriculture, particularly in developing regions. As phenotyping systems continue to advance, they will play a critical role in promoting sustainable agriculture, enhancing resource efficiency, ensuring food security and addressing global climate change.

Anahtar Kelimeler

Phenotyping data Agriculture Technologies Artificial intelligence Challenges

Kaynakça

  • Afzaal H, Farooque AA, Schumann AW, Hussain N, McKenzie-Gopsill A, Esau T and Acharya B (2021). Detection of a potato disease (early blight) using artificial intelligence. Remote Sensing, 13(3): 411.
  • Ahmed S, Rahman M and Hossain M (2021). Evaluation of millet varieties under drought conditions using traditional phenotyping methods. Journal of Agricultural Science, 13(2): 85-94.
  • Alonso J, Perez L and Gomez M (2020). Conservation and characterization of heirloom vegetable varieties using traditional phenotyping approaches. Genetic Resources and Crop Evolution, 67(5): 1023-1035.
  • Araus JL, Serret MD and Edmeades GO (2012). Phenotyping maize for adaptation to drought. Frontiers in physiology, 3, 305.
  • Araus JL and Cairns JE (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends in plant science, 19(1): 52-61.
  • Araus JL, Buchaillot ML and Kefauver SC (2022). High Throughput Field Phenotyping. In Wheat Improvement: Food Security in a Changing Climate (pp. 495-512). Cham: Springer International Publishing.
  • Arya S, Sandhu KS, Singh J and Kumar S (2022). Deep learning: As the new frontier in high-throughput plant phenotyping. Euphytica, 218: (4), 47.
  • Asaari MSM, Mertens S, Dhondt S, Inzé D, Wuyts N and Scheunders P (2019). Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform. Computers and Electronics in Agriculture, 162: 749-758.
  • Atefi A, Ge Y, Pitla S and Schnable J (2021). Robotic technologies for high-throughput plant phenotyping: Contemporary reviews and future perspectives. Frontiers in plant science, 12, 611940.
  • Bailey-Serres J, Parker JE, Ainsworth EA, Oldroyd GE and Schroeder JI (2019). Genetic strategies for improving crop yields. Nature, 575 (7781): 109-118.
  • Balota M and Oakes J (2017). UAV remote sensing for phenotyping drought tolerance in peanuts. In Autonomous air and ground sensing systems for agricultural optimization and phenotyping II (Vol. 10218, pp. 81-87). SPIE.
  • Banerjee K, Krishnan P and Das B (2020). Thermal imaging and multivariate techniques for characterizing and screening wheat genotypes under water stress condition. Ecological Indicators, 119, 106829.
  • Barbosa BDS, Costa L, Ampatzidis Y, Vijayakumar V and dos Santos LM (2021). UAV-based coffee yield prediction utilizing feature selection and deep learning. Smart Agricultural Technology, 1, 100010.
  • Bhandari M, Baker S, Rudd JC, Ibrahim AM, Chang A, Xue Q and Auvermann B (2021). Assessing the effect of drought on winter wheat growth using unmanned aerial system (UAS)-based phenotyping. Remote Sensing, 13(6): 1144.
  • Blanchy G, Deroo W, De Swaef T, Lootens P, Quataert P, Roldán-Ruíz I and Garré S (2024). Closing the phenotyping gap with non-invasive below ground field phenotyping. EGUsphere, 1-30.
  • Bohra A, Naik SJ, Kumari A, Tiwari A and Joshi R (2021). Integrating phenomics with breeding for climate-smart agriculture. Omics Technologies for Sustainable Agriculture and Global Food Security (Vol II), 1-24.
  • Brown D and Miller S (2019). The enduring relevance of traditional phenotyping in the era of precision agriculture. Plant Science Today, 6(3): 200-207.
  • Cabrera-Bosquet L, Fournier C, et al (2020). Advances in HTP and its application in breeding. Journal of Experimental Botany, 71(16), 4687-4705.
  • Cao HT (2018). A Low-Cost Depth Imaging Mobile Platform for Canola Phenotyping (Doctoral dissertation, University of Saskatchewan).
  • Centorame L, Gasperini T, Ilari A, Del Gatto A and Foppa Pedretti E (2024). An overview of machine learning applications on plant phenotyping, with a focus on sunflower. Agronomy, 14(4): 719.
  • Chaturvedi SK, Sekhar R, Banerjee S and Kamal H (2019). Comparative review study of military and civilian Unmanned Aerial Vehicle. INCAS Bulletin, 11(3): 183-198.
  • Chawade A, van Ham J, Blomquist H, Bagge O, Alexandersson E and Ortiz R (2019). High-throughput field-phenotyping tools for plant breeding and precision agriculture. Agronomy, 9(5): 258.
  • Chenu K, Van Oosterom EJ, McLean G, Deifel KS, Fletcher A, Geetika G and Hammer GL (2018). Integrating modelling and phenotyping approaches to identify and screen complex traits: transpiration efficiency in cereals. Journal of experimental botany, 69(13): 3181-3194.
  • Costa JM, Marques da Silva J, Pinheiro C, Barón M, Mylona P, Centritto, M and Oliveira MM (2019). Opportunities and limitations of crop phenotyping in southern European countries. Frontiers in plant science, 10: 1125.
  • Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, De Los Campos G, and Varshney RK (2017). Genomic selection in plant breeding: methods, models, and perspectives. Trends in plant science, 22(11): 961-975.
  • Cvejić S, Jocić S, Mitrović B, Bekavac G, Mirosavljević M, Jeromela A M and Miladinović D (2022). Innovative Approaches in the Breeding of Climate‐Resilient Crops. Climate Change and Agriculture: Perspectives, Sustainability and Resilience, 111-156.
  • Daviet B, Fernandez R, Cabrera-Bosquet L, Pradal C and Fournier C (2022). PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time. Plant Methods, 18(1): 130.
  • Deery D, Jimenez-Berni J, Jones H, Sirault X and Furbank R (2014). Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping. Vol. 4.
  • Deulkar SS and Barve SS (2018). An automated tomato quality grading using clustering based support vector machine. In 2018 3rd International Conference on Communication and Electronics Systems (ICCES) (pp. 1128-1133). IEEE.
  • Diaz-Garcia G, Lozoya-Saldaña H, Bamberg J and Diaz-Garcia L (2024). Morphometric analysis of wild potato leaves. Genetic Resources and Crop Evolution, 1-16.
  • Dogan A, Uyak C, Keskin N, Akcay A, Sensoy R I G and Ercisli S (2018). Grapevine leaf area measurements by using pixel values. Comptes rendus de l’Acade'mie bulgare des Sciences, 71(6):772-779
  • Dwivedi SL, Goldman I, Ceccarelli S and Ortiz R (2020). Advanced analytics, phenomics and biotechnology approaches to enhance genetic gains in plant breeding. Advances in agronomy, 162: 89-142.
  • Feng L, Chen S, Zhang C, Zhang Y and He Y (2021). A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping. Computers and electronics in agriculture, 182, 106033.
  • Fernandez A, Lopez R and Martinez J (2020). Phenotypic plasticity in response to light variability in understory plants assessed through traditional measurements. Ecology and Evolution, 10(15): 8421-8432.
  • Fiorani F and Schurr U (2019). Future scenarios for plant phenotyping. Annual review of plant biology, 64(1): 267-291.
  • Fisher M, Abate T, Lunduka RW, Asnake W, Alemayehu Y and Madulu RB (2015). Drought tolerant maize for farmer adaptation to drought in sub-Saharan Africa: Determinants of adoption in eastern and southern Africa. Climatic Change, 133: 283-299.
  • Furbank RT and Tester M (2011). Phenomics-technologies to relieve the phenotyping bottleneck. Trends in plant science, 16(12): 635-644.
  • Ge Y, Bai G, Stoerger V and Schnable JC (2016). Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging. Computers and Electronics in Agriculture, 127: 625-632.
  • Gill T, Gill SK, Saini DK, Chopra Y, de Koff J P and Sandhu KS (2022). A comprehensive review of high throughput phenotyping and machine learning for plant stress phenotyping. Phenomics, 2(3): 156-183.
  • Gupta N, Sharma R and Verma S (2020). Monitoring soybean development stages through manual observation for improved crop management. Agricultural Research, 9(4): 541-550.
  • Hatem Y, Hammad G and Safwat G (2022). Artificial intelligence for plant genomics and crop improvement. Egyptian Journal of Botany, 62(2): 291-303.
  • He S, Li X, Chen M, Xu X, Tang F, Gong T and Liu W (2024). Crop HTP Technologies: Applications and Prospects. Agriculture, 14(5): 723.
  • Hu Y and Schmidhalter U (2023). Opportunity and challenges of phenotyping plant salt tolerance. Trends in plant science, 28(5): 552-566.
  • Janni M and Pieruschka R (2022). Plant phenotyping for a sustainable future. Journal of Experimental Botany, 73(15): 5085-5088.
  • Jimenez-Berni JA, David MD, Rozas-Larraondo, Anthony (Tony) G, Condon GJR, Richard AJ, William DB, Robert TF and Xavier RRS (2018). High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR. Frontiers in Plant Science 9:1-18.
  • Kamarianakis Z, Perdikakis S, Daliakopoulos IN, Papadimitriou DM and Panagiotakis S (2024). Design and Implementation of a Low-Cost, Linear Robotic Camera System, Targeting Greenhouse Plant Growth Monitoring. Future Internet, 16(5): 145.
  • Kamilaris A and Prenafeta-Boldú FX (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147: 70-90.
  • Karaca M and Ince AG (2019). Conservation of biodiversity and genetic resources for sustainable agriculture. Innovations In Sustainable Agriculture, 363-410.
  • Karunathilake EMBM, Le AT, Heo S, Chung YS and Mansoor S (2023). The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture, 13(8): 1593.
  • Kaur S, Kakani VG, Carver B, Jarquin D and Singh A (2024). Hyperspectral imaging combined with machine learning for high‐throughput phenotyping in winter wheat. The Plant Phenome Journal, 7(1), e20111.
  • Kim Y, Still CJ, Roberts DA and Goulden ML (2018). Thermal infrared imaging of conifer leaf temperatures: comparison to thermocouple measurements and assessment of environmental influences. Agricultural and Forest Meteorology, 248: 361-371.
  • Kim J and Lee S (2019). Sensory evaluation methods in assessing tea quality: A traditional approach. Food Quality and Preference, 71: 87-95.
  • Kim KD, Kang Y and Kim C (2020). Application of genomic big data in plant breeding: Past, present, and future. Plants, 9(11): 1454.
  • Kotal A, Elluri L, Gupta D, Mandalapu V and Joshi A (2023). Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis. In 2023 IEEE International Conference on Big Data (BigData) (pp. 5519-5528). IEEE.
  • Krause MR, González-Pérez L, Crossa J, Pérez-Rodríguez P, Montesinos-López O, Singh RP and Mondal S (2019). Hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat. G3: Genes, Genomes, Genetics, 9(4): 1231-1247.
  • Kuriakose SV, Pushker R and Hyde EM (2020). Data-driven decisions for accelerated plant breeding. Accelerated Plant Breeding, Volume 1: Cereal Crops, 89-119.
  • Kurihara J, Nagata T and Tomiyama H (2023). Rice Yield Prediction in Different Growth Environments Using Unmanned Aerial Vehicle-Based Hyperspectral Imaging. Remote Sensing, 15(8): 2004.
  • Lacerda LN, Snider JL, Cohen Y, Liakos V, Gobbo S and Vellidis G (2022). Using UAV-based thermal imagery to detect crop water status variability in cotton. Smart Agricultural Technology, 2, 100029.
  • Lajoie-O'Malley A, Bronson K, van der Burg S and Klerkx L (2020). The future (s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents. Ecosystem Services, 45, 101183.
  • Lassoued R, Macall DM, Smyth SJ, Phillips PW and Hesseln H (2021). Data challenges for future plant gene editing: expert opinion. Transgenic Research, 30(6): 765-780.
  • Le Ru A, Ibarcq G, Boniface MC, Baussart A, Muños S and Chabaud M (2021). Image analysis for the automatic phenotyping of Orobanche cumana tubercles on sunflower roots. Plant methods, 17: 1-14.
  • Lee SH, Goëau H, Bonnet P and Joly A (2020). New perspectives on plant disease characterization based on deep learning. Computers and Electronics in Agriculture, 170, 105220.
  • Li D, Quan C, Song Z, Li X, Yu G, Li C and Muhammad A (2021). High-throughput plant phenotyping platform (HT3P) as a novel tool for estimating agronomic traits from the lab to the field. Frontiers in Bioengineering and Biotechnology, 8, 623705.
  • Li J, Shi Y, Veeranampalayam-Sivakumar A N and Schachtman D P (2018). Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system. Frontiers in plant science, 9, 1406.
  • Li L, Zhang Q and Huang D (2014). A review of imaging techniques for plant phenotyping. Sensors, 14(11): 20078-20111.
  • Li X, Chen M, He S, Xu X, He L, Wang L and Yang W (2024). Estimation of soybean yield based on high-throughput phenotyping and machine learning. Frontiers in Plant Science, 15, 1395760.
  • Lipper L, Thornton P, Campbell BM, Baedeker T, Braimoh A, Bwalya M and Torquebiau EF (2017). Climate-smart agriculture for food security. Nature climate change, 4(12): 1068-1072.
  • Luo S, Liu W, Zhang Y, Wang C, Xi X, Nie S and Zhou G (2021). Maize and soybean heights estimation from unmanned aerial vehicle (UAV) LiDAR data. Computers and Electronics in Agriculture, 182, 106005.
  • Ma Z, Rayhana R, Feng K, Liu Z, Xiao G, Ruan Y and Sangha JS (2022). A review on sensing technologies for high-throughput plant phenotyping. IEEE Open Journal of Instrumentation and Measurement, 1: 1-21.
  • Mahlein AK, Kuska MT, Behmann J, Polder G and Walter A (2018). Hyperspectral sensors and imaging technologies in phytopathology: state of the art. Annual review of phytopathology, 56(1): 535-558.
  • Mao Y, You C, Zhang J, Huang K and Letaief KB (2017). A survey on mobile edge computing: The communication perspective. IEEE communications surveys & tutorials, 19(4): 2322-2358.
  • Maqbool S, Hassan MA, Xia X, York LM, Rasheed A and He Z (2022). Root system architecture in cereals: progress, challenges and perspective. The Plant Journal, 110(1): 23-42.
  • Marwaha S, Deb CK, Haque MA, Naha S and Maji AK (2023). Application of Artificial Intelligence and Machine Learning in Agriculture. In Translating Physiological Tools to Augment Crop Breeding (pp. 441-457). Singapore: Springer Nature Singapore.
  • Mir RR, Reynolds M, Pinto F, Khan MA and Bhat MA (2019). High-throughput phenotyping for crop improvement in the genomics era. Plant Science, 282: 60-72.
  • Montesinos-López OA, Montesinos-López A, Pérez-Rodríguez P, Barrón-López JA, Martini JW, Fajardo-Flores SB and Crossa J (2021). A review of deep learning applications for genomic selection. BMC genomics, 22: 1-23.
  • Müller J and Becker H (2020). Manual phenotyping of sorghum under abiotic stress conditions for breeding resilience. Journal of Agronomy and Crop Science, 206(5): 609-620.
  • Muzamil M, Rasool S and Ummyiah HM (2022). Insitu and Exsitu agricultural waste management system, 45-63. Agricultural waste-New Insights. IntechOpen. ISBN: 978-1- 80356-966-6.
  • Nabwire S, Suh HK, Kim MS, Baek I and Cho BK (2021). Application of artificial intelligence in phenomics. Sensors, 21(13): 4363.
  • Nakhforoosh A, Hallin E, Karunakaran C, Korbas M, Stobbs J and Kochian L (2024). Visualization and Quantitative Evaluation of Functional Structures of Soybean Root Nodules via Synchrotron X-ray Imaging. Plant Phenomics, 6, 0203.
  • Nguyen T, Tran H and Le P (2019). Traditional assessment of salinity stress responses in barley seedlings under controlled conditions. Plant Physiology and Biochemistry, 141: 259-266.
  • Nguyen LQ, Shin J, Ryu S, Dang LM, Park HY, Lee ON and Moon H (2023). Innovative Cucumber Phenotyping: A Smartphone-Based and Data-Labeling-Free Model. Electronics, 12(23): 4775.
  • Oliveira R, Santos L and Pereira J (2021). Rapid assessment of flood damage in rice fields using traditional field surveys. Natural Hazards, 107(1): 495-508.
  • Panday US, Pratihast AK, Aryal J and Kayastha RB (2020). A review on drone-based data solutions for cereal crops. Drones, 4(3): 41.
  • Papoutsoglou EA, Faria D, Arend D, Arnaud E, Athanasiadis IN, Chaves I and Pommier C (2020). Enabling reusability of plant phenomic datasets with MIAPPE 1.1. New Phytologist, 227(1): 260-273.
  • Park S, Lee J and Choi H (2020). Validation of high-throughput phenotyping data through traditional measurement techniques in wheat. Plant Methods, 16(1): 83.
  • Pieruschka R and Schurr U (2019). Plant phenotyping: past, present, and future. Plant Phenomics.
  • Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS and French AP (2017). Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. Gigascience, 6(10), gix083.
  • Reynolds D, Baret F, Welcker C, Bostrom A, Ball J, Cellini F and Tardieu F (2019). What is cost-efficient phenotyping? Optimizing costs for different scenarios. Plant Science, 282: 14-22.
  • Reynolds M, Chapman S, Crespo-Herrera L, Molero G, Mondal S, Pequeno D N and Sukumaran S (2020). Breeder friendly phenotyping. Plant Science, 295, 110396.
  • Rico-Chávez AK, Franco JA, Fernandez-Jaramillo AA, Contreras-Medina LM, Guevara-González RG and Hernandez-Escobedo Q (2022). Machine learning for plant stress modeling: A perspective towards hormesis management. Plants, 11(7): 970.
  • Roberts DA, Roth KL, Wetherley EB, Meerdink SK and Perroy RL (2018). Hyperspectral vegetation indices. In Hyperspectral indices and image classifications for agriculture and vegetation (pp. 3-26). CRC press.
  • Rodriguez M, Hernandez L and Gomez R (2019). Participatory breeding in maize: The continued relevance of traditional phenotyping methods. Agriculture and Human Values, 36(4): 799-810.
  • Roitsch T, Cabrera-Bosquet L, Fournier A, Ghamkhar K, Jiménez-Berni J, Pinto F and Ober E S (2019). New sensors and data-driven approaches-A path to next generation phenomics. Plant Science, 282: 2-10.
  • Rose DC, Wheeler R, Winter M, Lobley M and Chivers CA (2021). Agriculture 4.0: Making it work for people, production, and the planet. Land use policy, 100, 104933.
  • Ruzzante S, Labarta R and Bilton A (2021). Adoption of agricultural technology in the developing world: A meta-analysis of the empirical literature. World Development, 146, 105599.
  • Ryan M (2023). The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature. AI & SOCIETY, 38(6): 2473-2485.
  • Sahoo L, Mohapatra D, Raghuvanshi HR, Kumar S, Kaur R, Chawla R and Afreen N (2024). Transforming Agriculture through Artificial Intelligence: Advancements in Plant Disease Detection, Applications, and Challenges. Journal of Advances in Biology & Biotechnology, 27(5): 381-388.
  • Sarić R, Nguyen V D, Burge T, Berkowitz O, Trtílek M, Whelan J and Čustović E (2022). Applications of hyperspectral imaging in plant phenotyping. Trends in plant science, 27(3): 301-315.
  • Satbhai SB, Göschl C and Busch W (2017). Automated high-throughput root phenotyping of Arabidopsis thaliana under nutrient deficiency conditions. Plant Genomics: Methods and Protocols, 135-153.
  • Shakoor N, Lee, S and Mockler TC (2017). High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Current opinion in plant biology, 38: 184-192.
  • Shakshi RP, Yadav S and Singh MK (2024). Integrating Genomics and Phenomics in Agricultural Breeding: A Comprehensive Review. Asian Research Journal of Agriculture,17(4):116-125.
  • Shi Y, Zhu Y, Wang X, Sun X, Ding Y, Cao W and Hu Z (2020). Progress and development on biological information of crop phenotype research applied to real-time variable-rate fertilization. Plant methods, 16: 1-15.
  • Shi Y, Thomasson JA, Murray SC, Pugh NA, Rooney WL, Shafian S and Yang C (2021). Unmanned aerial vehicles for high-throughput phenotyping and agronomic research. PloS one, 11(7), e0159781.
  • Shi T, Liu Y, Zheng X, Hu K, Huang H, Liu H and Huang H (2023). Recent advances in plant disease severity assessment using convolutional neural networks. Scientific Reports, 13(1): 2336.
  • Sinesio F, Cammareri M, Cottet V, Fontanet L, Jost M, Moneta E and Grandillo S (2021). Sensory traits and consumer’s perceived quality of traditional and modern fresh market tomato varieties: a study in three European countries. Foods, 10(11): 2521.
  • Singh A, Ganapathysubramanian B, Singh A K and Sarkar S (2016). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 21(2): 110-124.
  • Singh D, Bhatia S and Rehman A (2021). Big data analytics for phenotyping: Tackling the challenge of high-throughput data. Computers and Electronics in Agriculture, 182: 106-111.
  • Singh AK (2022). Precision agriculture in india-opportunities and challenges. Indian Journal of Fertilisers, 18(4): 308-331.
  • Stanghellini G and Leoni F (2020). Digital phenotyping: Ethical issues, opportunities, and threats. Frontiers in Psychiatry, 11: 473.
  • Sweet DD, Tirado SB, Springer NM, Hirsch CN and Hirsch CD (2022). Opportunities and challenges in phenotyping row crops using drone‐based RGB imaging. The Plant Phenome Journal, 5(1), e20044.
  • Tahir MN, Lan Y, Zhang Y, Wang Y, Nawaz F, Shah MAA and Naqvi SZA (2020). Real time estimation of leaf area index and groundnut yield using multispectral UAV. International Journal of Precision Agricultural Aviation, 3(1).
  • Tanaka TST, Wang S, Jørgensen JR, Gentili M, Vidal AZ, Mortensen AK and Gislum R (2024). Review of Crop Phenotyping in Field Plot Experiments Using UAV-Mounted Sensors and Algorithms. Drones, 8(6): 212.
  • Tanger P, Klassen S, Mojica JP, Lovell JT, Moyers BT, Baraoidan M and McKay JK (2017). Field-based high throughput phenotyping rapidly identifies genomic regions controlling yield components in rice. Scientific Reports, 7(1): 42839.
  • Tardieu F, Cabrera-Bosquet L, Pridmore T and Bennett M (2017). Plant phenomics, from sensors to Aknowledge. Current Biology, 27(15): R770-R783.
  • Thompson G and Carter J (2019). Traditional bioassays for detecting herbicide resistance in weed populations. Weed Science, 67(4): 498-507.
  • Thorp KR, Thompson AL, Harders SJ, French AN and Ward RW (2018). High-throughput phenotyping of crop water use efficiency via multispectral drone imagery and a daily soil water balance model. Remote Sensing, 10(11): 1682.
  • Thrash T, Lee H and Baker RL (2022). A low‐cost high‐throughput phenotyping system for automatically quantifying foliar area and greenness. Applications in Plant Sciences, 10(6), e11502.
  • Tomičić A, Malešević A and Čartolovni A (2022). Ethical, legal and social issues of digital phenotyping as a future solution for present-day challenges: A scoping review. Science and Engineering Ethics, 28: 1-25.
  • Tong H and Nikoloski Z (2021). Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. Journal of plant physiology, 257, 153354.
  • Tsaftaris S, Minervini M and Scharr H (2016). Machine learning for plant phenotyping needs image processing. Trends in plant science, 21(12): 989-991.
  • Walter A, Scharr H, Gilmer F, Zierer R, Nagel K A, Ernst M and Schurr U (2019). Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species. New Phytologist, 174(2): 447-455.
  • Wang Y, Wen W, Wu S, Wang C, Yu Z, Guo X and Zhao C (2018). Maize plant phenotyping: comparing 3D laser scanning, multi-view stereo reconstruction, and 3D digitizing estimates. Remote Sensing, 11(1): 63.
  • Wang X, Zhou X, Ji L and Shen K (2024). Artificial intelligence/machine learning-assisted near-infrared/optical biosensing for plant phenotyping. In Machine Learning and Artificial Intelligence in Chemical and Biological Sensing (pp. 203-225).
  • Wasaya A, Zhang X, Fang Q and Yan Z (2018). Root phenotyping for drought tolerance: a review. Agronomy, 8(11): 241.
  • Washburn JD, Adak A, DeSalvio AJ, Arik MA and Murray SC (2024). High temporal resolution unoccupied aerial systems phenotyping provides unique information between flight dates. The Plant Phenome Journal, 7(1): e20113.
  • Williams E, Brown S and Miller D (2019). Incorporating traditional phenotyping techniques in plant science education. Journal of Natural Resources and Life Sciences Education, 48(1): 45-52.
  • Wu D, Guo Z, Ye J, Feng H, Liu J, Chen G and Yang W (2019). Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice. Journal of Experimental Botany, 70(2): 545-561.
  • Xiao Q, Bai X, Zhang C and He Y (2022). Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review. Journal of advanced research, 35: 215-230.
  • Xie T, Li J, Yang C, Jiang Z, Chen Y, Guo L and Zhang J (2021). Crop height estimation based on UAV images: Methods, errors, and strategies. Computers and Electronics in Agriculture, 185, 106155.
  • Xue J and Su B (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of sensors, 2017(1), 1353691.
  • Yang W, Feng H, Zhang X, Zhang J, Doonan J H, Batchelor W D and Yan J (2020). Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives. Molecular plant, 13(2): 187-214.
  • Yang B, Zhu W, Rezaei EE, Li J, Sun Z and Zhang J (2022). The optimal phenological phase of maize for yield prediction with high-frequency UAV remote sensing. Remote Sensing, 14(7): 1559.
  • Yao L, Van De Zedde R and Kowalchuk G (2021). Recent developments and potential of robotics in plant eco-phenotyping. Emerging topics in life sciences, 5(2): 289-300.
  • Young SN, Kayacan E and Peschel JM (2019). Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum. Precision Agriculture, 20(4): 697-722.
  • Yuan H, Song M, Liu Y, Xie Q, Cao W, Zhu Y and Ni J (2023). Field Phenotyping Monitoring Systems for High-Throughput: A Survey of Enabling Technologies, Equipment, and Research Challenges. Agronomy, 13(11): 2832.
  • Zaman-Allah M, Vergara O, Araus JL, Tarekegne A, Magorokosho C, Zarco-Tejada PJ and Cairns J (2015). Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant methods, 11: 1-10.
  • Zhang L, Zhao H and Li D (2021). Integrating traditional and modern phenotyping methods for comprehensive plant trait analysis. Frontiers in Plant Science, 12: 640.
  • Zhang H, Wang L, Jin X, Bian L and Ge Y (2023). High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing. The Crop Journal, 11(5): 1303-1318.
  • Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S and Fan J (2019). Crop phenomics: current status and perspectives. Frontiers in Plant Science, 10: 714.
  • Zhou RQ, Jin JJ, Li QM, Su ZZ, Yu XJ, Tang Y and Li XL (2019). Early detection of magnaporthe oryzae- infected barley leaves and lesion visualization based on hyperspectral imaging. Frontiers in Plant Science, 9: 1962.
  • Zhu W, Chen H, Ciechanowska I and Spaner D (2018). Application of infrared thermal imaging for the rapid diagnosis of crop disease. IFAC-PapersOnLine, 51(17): 424-430.
Toplam 141 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Tarım Makine Sistemleri, Tarımsal Elektrifikasyon, Tarımsal Enerji Sistemleri, Tarımsal Otomasyon
Bölüm Derleme
Yazarlar

Mohammad Muzamil 0000-0002-1440-2604

Rizwan Ul Zama Banday 0000-0002-2714-3867

Danish Gul 0009-0002-1020-3285

Seemi Lohani 0000-0002-6874-4474

Sehreen Rasool 0009-0007-7288-7041

Kezia Rajan 0000-0002-4515-4551

Muzamil Hamid Wani 0000-0002-9973-9897

Erken Görünüm Tarihi 27 Haziran 2025
Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 27 Kasım 2024
Kabul Tarihi 24 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: 1

Kaynak Göster

APA Muzamil, M., Banday, R. U. Z., Gul, D., Lohani, S., vd. (2025). A Critical Review on ‘Current Status and Future Implications of Advanced Phenotyping Systems for Monitoring of Agricultural Crops’. Turkish Journal of Agricultural Engineering Research, 6(1), 89-117. https://doi.org/10.46592/turkager.1592116
 Kapak Resmi İndir

26831    32449  32450 32451 3245232453

International peer double-blind reviewed journal

The articles in the Turkish Journal of Agricultural Engineering Research are open access articles and the articles are licensed under a Creative Commons Attribution 4.0 International License (CC-BY-NC-4.0)(https://creativecommons.org/licenses/by-nc/4.0/deed.en). This license allows third parties to share and adapt the content for non-commercial purposes with proper attribution to the original work. Please visit for more information this link https://creativecommons.org/licenses/by-nc/4.0/ 

Turkish Journal of Agricultural Engineering Research (TURKAGER) is indexed/abstracted in Information Matrix for the Analysis of Journals (MIAR), EBSCO, CABI, Food Science & Technology Abstracts (FSTA), CAS Source Index (CASSI).

Turkish Journal of Agricultural Engineering Research (TURKAGER) does not charge any application, publication, or subscription fees.

Publisher: Ebubekir ALTUNTAŞ

For articles citations to the articles of the Turkish Journal of Agricultural Engineering Research (TURKAGER), please click: