{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T23:23:14Z","timestamp":1773271394057,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T00:00:00Z","timestamp":1630713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007659","name":"Bayer Corporation","doi-asserted-by":"publisher","award":["106027"],"award-info":[{"award-number":["106027"]}],"id":[{"id":"10.13039\/100007659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000980","name":"Grains Research and Development Corporation","doi-asserted-by":"publisher","award":["2062311"],"award-info":[{"award-number":["2062311"]}],"id":[{"id":"10.13039\/501100000980","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Advances in early insect detection have been reported using digital technologies through camera systems, sensor networks, and remote sensing coupled with machine learning (ML) modeling. However, up to date, there is no cost-effective system to monitor insect presence accurately and insect-plant interactions. This paper presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning. Several artificial neural network (ANN) models were developed based on classification to detect the level of infestation and regression to predict insect numbers for both e-nose and NIR inputs, and plant physiological response based on e-nose to predict photosynthesis rate (A), transpiration (E) and stomatal conductance (gs). Results showed high accuracy for classification models ranging within 96.5\u201399.3% for NIR and between 94.2\u201399.2% using e-nose data as inputs. For regression models, high correlation coefficients were obtained for physiological parameters (gs, E and A) using e-nose data from all samples as inputs (R = 0.86) and R = 0.94 considering only control plants (no insect presence). Finally, R = 0.97 for NIR and R = 0.99 for e-nose data as inputs were obtained to predict number of insects. Performances for all models developed showed no signs of overfitting. In this paper, a field-based system using unmanned aerial vehicles with the e-nose as payload was proposed and described for deployment of ML models to aid growers in pest management practices.<\/jats:p>","DOI":"10.3390\/s21175948","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T13:18:26Z","timestamp":1630934306000},"page":"5948","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modeling"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0377-5085","authenticated-orcid":false,"given":"Sigfredo","family":"Fuentes","sequence":"first","affiliation":[{"name":"Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia"}]},{"given":"Eden","family":"Tongson","sequence":"additional","affiliation":[{"name":"Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia"}]},{"given":"Ranjith R.","family":"Unnithan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, School of Engineering, University of Melbourne, Melbourne, VIC 3010, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9207-9307","authenticated-orcid":false,"given":"Claudia","family":"Gonzalez Viejo","sequence":"additional","affiliation":[{"name":"Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s13744-019-00686-5","article-title":"Population Dynamics of Drosophila suzukii (Diptera: Drosophilidae) in Berry Crops in Southern Brazil","volume":"48","author":"Wollmann","year":"2019","journal-title":"Neotrop. \u00c8ntomol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1002\/fee.2217","article-title":"Seasonal insect migrations: Massive, influential, and overlooked","volume":"18","author":"Satterfield","year":"2020","journal-title":"Front. Ecol. Environ."},{"key":"ref_3","first-page":"618","article-title":"Chemical ecology and pest management: A review","volume":"5","author":"Saha","year":"2017","journal-title":"Int. J. Cardiovasc. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1002\/ps.5536","article-title":"Advances in insect phototaxis and application to pest management: A review","volume":"75","author":"Kim","year":"2019","journal-title":"Pest Manag. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s10340-020-01309-4","article-title":"Insect pest monitoring with camera-equipped traps: Strengths and limitations","volume":"94","author":"Preti","year":"2020","journal-title":"J. Pest Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3028","DOI":"10.1093\/jee\/toaa223","article-title":"Use of Digital Video Cameras to Determine the Efficacy of Two Trap Types for Capturing Rhynchophorus palmarum (Coleoptera: Curculionidae)","volume":"113","author":"Hoddle","year":"2020","journal-title":"J. Econ. \u00c8ntomol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Remboski, T.B., de Souza, W.D., de Aguiar, M.S., and Ferreira, P.R. (2018, January 9\u201313). Identification of Fruit Fly in Intelligent Traps Using Techniques of Digital Image Processing and Machine Learning. Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Pau, France.","DOI":"10.1145\/3167132.3167155"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"13","DOI":"10.33260\/zictjournal.v3i1.71","article-title":"Developing an automatic identification and early warning and monitoring web based system of fall army worm based on machine learning in developing countries","volume":"3","author":"Chulu","year":"2019","journal-title":"Zamb. ICT J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"312","DOI":"10.3390\/ai1020021","article-title":"Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review","volume":"1","author":"Barbedo","year":"2020","journal-title":"AI"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Markovi\u0107, D., Vuji\u010di\u0107, D., Tanaskovi\u0107, S., \u0110or\u0111evi\u0107, B., Ran\u0111i\u0107, S., and Stamenkovi\u0107, Z. (2021). Prediction of Pest Insect Appearance Using Sensors and Machine Learning. Sensors, 21.","DOI":"10.3390\/s21144846"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kasinathan, T., and Uyyala, S.R. (2021). Machine learning ensemble with image processing for pest identification and classification in field crops. Neural Comput. Appl., 1\u201314.","DOI":"10.1007\/s00521-020-05497-z"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lima, M.C.F., Leandro, M.E.D.D.A., Valero, C., Coronel, L.C.P., and Bazzo, C.O.G. (2020). Automatic Detection and Monitoring of Insect Pests\u2014A Review. Agriculture, 10.","DOI":"10.3390\/agriculture10050161"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104943","DOI":"10.1016\/j.compag.2019.104943","article-title":"Monitoring plant diseases and pests through remote sensing technology: A review","volume":"165","author":"Zhang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Vel\u00e1squez, D., S\u00e1nchez, A., Sarmiento, S., Toro, M., Maiza, M., and Sierra, B. (2020). A Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks, Remote Sensing, and Deep Learning: Case Study of the Caturra Variety in Colombia. Appl. Sci., 10.","DOI":"10.3390\/app10020697"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/IOTM.0001.1900037","article-title":"Energy Neutral Machine Learning Based IoT Device for Pest Detection in Precision Agriculture","volume":"2","author":"Brunelli","year":"2019","journal-title":"IEEE Internet Things Mag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2020.02.010","article-title":"Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis","volume":"162","author":"Poblete","year":"2020","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111480","DOI":"10.1016\/j.rse.2019.111480","article-title":"Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling","volume":"236","author":"Hornero","year":"2019","journal-title":"Remote. Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P.J. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote. Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cellini, A., Blasioli, S., Biondi, E., Bertaccini, A., Braschi, I., and Spinelli, F. (2017). Potential Applications and Limitations of Electronic Nose Devices for Plant Disease Diagnosis. Sensors, 17.","DOI":"10.3390\/s17112596"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cui, S., Ling, P., Zhu, H., and Keener, H.M. (2018). Plant Pest Detection Using an Artificial Nose System: A Review. Sensors, 18.","DOI":"10.3390\/s18020378"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2014\/297219","article-title":"Development of a Portable Electronic Nose for Detection of Cotton Damaged by Nezara viridula (Hemiptera: Pentatomidae)","volume":"2014","author":"Lampson","year":"2014","journal-title":"J. Insects"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cui, S., Inocente, E.A.A., Acosta, N., Keener, H.M., Zhu, H., and Ling, P.P. (2019). Development of Fast E-nose System for Early-Stage Diagnosis of Aphid-Stressed Tomato Plants. Sensors, 19.","DOI":"10.3390\/s19163480"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1002\/(SICI)1097-0010(199912)79:15<2067::AID-JSFA490>3.0.CO;2-3","article-title":"Detection of mite infestation in wheat by electronic nose with transient flow sampling","volume":"79","author":"Ridgway","year":"1999","journal-title":"J. Sci. Food Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.jspr.2007.01.004","article-title":"Detection of age and insect damage incurred by wheat, with an electronic nose","volume":"43","author":"Zhang","year":"2007","journal-title":"J. Stored Prod. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3.1","DOI":"10.7451\/CBE.2013.55.3.1","article-title":"Feasibility of the application of electronic nose technology to detect insect infestation in wheat","volume":"55","author":"Wu","year":"2013","journal-title":"Can. Biosyst. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ahouandjinou, S.A.R.M., Kiki, M.P.A.F., Badoussi, P.E.N.A., and Assogba, K.M. (2020, January 8\u20139). A Multi-level Smart Monitoring System by Combining an E-Nose and Image Processing for Early Detection of FAW Pest in Agriculture. Proceedings of the Innovations and Interdisciplinary Solutions for Underserved Areas: 4th EAI International Conference, InterSol 2020, Nairobi, Kenya.","DOI":"10.1007\/978-3-030-51051-0_2"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"062002","DOI":"10.1088\/1361-6501\/abef3b","article-title":"Development of compact electronic noses: A review","volume":"32","author":"Cheng","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s10340-018-1004-y","article-title":"Improved biosecurity surveillance of non-native forest insects: A review of current methods","volume":"92","author":"Poland","year":"2018","journal-title":"J. Pest Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/S1672-6529(08)60090-6","article-title":"Identification of Stink Bugs Using an Electronic Nose","volume":"5","author":"Lan","year":"2008","journal-title":"J. Bionic Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.snb.2011.07.002","article-title":"Use of electronic nose technology for identifying rice infestation by Nilaparvata lugens","volume":"160","author":"Zhou","year":"2011","journal-title":"Sens. Actuators B Chem."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"127688","DOI":"10.1016\/j.snb.2020.127688","article-title":"Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality","volume":"308","author":"Viejo","year":"2020","journal-title":"Sens. Actuators B Chem."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"83","DOI":"10.17660\/ActaHortic.2004.648.10","article-title":"A suspended pot, non-circulating hydroponic method","volume":"648","author":"Kratky","year":"2004","journal-title":"Acta Hortic."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Shavrukov, Y., Genc, Y., and Hayes, J. (2012). The Use of Hydroponics in Abiotic Stress Tolerance Research, InTech Rijeka.","DOI":"10.5772\/35206"},{"key":"ref_34","unstructured":"McDonald, G., Umina, P., and Hangartner, S. (2020, November 25). Oat Aphid Rhophalosiphum padi. Available online: https:\/\/cesaraustralia.com\/pestnotes\/aphids\/oat-aphid\/."},{"key":"ref_35","first-page":"61","article-title":"Comparison of Field Population Growths of Three Cereal Aphid Species on Winter Wheat","volume":"39","author":"Tichopad","year":"2011","journal-title":"Plant Prot. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1111\/j.1461-9563.2007.00348.x","article-title":"An exponential growth model with decreasing r captures bottom-up effects on the population growth of Aphis glycines Matsumura (Hemiptera: Aphididae)","volume":"9","author":"Costamagna","year":"2007","journal-title":"Agric. For. \u00c8ntomol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Viejo, C.G., Tongson, E., and Fuentes, S. (2021). Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity. Sensors, 21.","DOI":"10.3390\/s21062016"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Viejo, C.G., Torrico, D.D., Dunshea, F.R., and Fuentes, S. (2019). Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages. Beverages, 5.","DOI":"10.3390\/beverages5040062"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Viejo, C.G., Torrico, D.D., Dunshea, F.R., and Fuentes, S. (2019). Development of Artificial Neural Network Models to Assess Beer Acceptability Based on Sensory Properties Using a Robotic Pourer: A Comparative Model Approach to Achieve an Artificial Intelligence System. Beverages, 5.","DOI":"10.3390\/beverages5020033"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1111\/ppl.13225","article-title":"Hydrogen peroxide potentiates defense system in presence of sulfur to protect chloroplast damage and photosynthesis of wheat under drought stress","volume":"172","author":"Sehar","year":"2020","journal-title":"Physiol. Plant."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, W., Liu, L., Shen, Q., Yang, J., Han, X., Tian, F., and Wu, J. (2020). Effects of Water Stress on Photosynthesis, Yield, and Water Use Efficiency in Winter Wheat. Water, 12.","DOI":"10.22541\/au.159246549.98572928"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Shahzad, M.W., Ghani, H., Ayyub, M., Ali, Q., Ahmad, H.M., Faisal, M., Ali, A., and Qasim, M.U. (2019). Performance of some Wheat Cultivars against APHID and Its Damage on Yield and Photosynthesis. J. Glob. Innov. Agric. Soc. Sci., 105\u2013109.","DOI":"10.22194\/JGIASS\/7.869"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.cj.2017.01.001","article-title":"Improving water-use efficiency by decreasing stomatal conductance and transpiration rate to maintain higher ear photosynthetic rate in drought-resistant wheat","volume":"5","author":"Li","year":"2017","journal-title":"Crop. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"106829","DOI":"10.1016\/j.ecolind.2020.106829","article-title":"Thermal imaging and multivariate techniques for characterizing and screening wheat genotypes under water stress condition","volume":"119","author":"Banerjee","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Francesconi, S., and Balestra, G.M. (2020). The modulation of stomatal conductance and photosynthetic parameters is involved in Fusarium head blight resistance in wheat. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0235482"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ahmed, S.S., Liu, D., and Simon, J.-C. (2017). Impact of water-deficit stress on tritrophic interactions in a wheat-aphid-parasitoid system. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0186599"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1255\/jnirs.353","article-title":"Determination of hydrogen peroxide by near infrared spectroscopy","volume":"11","author":"Pimenta","year":"2003","journal-title":"J. Infrared Spectrosc."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Fuentes, S., Tongson, E., Chen, J., and Viejo, C.G. (2020). A Digital Approach to Evaluate the Effect of Berry Cell Death on Pinot Noir Wines\u2019 Quality Traits and Sensory Profiles Using Non-Destructive Near-Infrared Spectroscopy. Beverages, 6.","DOI":"10.3390\/beverages6020039"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"De Bei, R., Fuentes, S., Wirthensohn, M., Cozzolino, D., and Tyerman, S. (2018). Feasibility study on the use of Near Infrared spectroscopy to measure water status of almond trees. Acta Hortic., 79\u201384.","DOI":"10.17660\/ActaHortic.2018.1219.14"},{"key":"ref_50","first-page":"28","article-title":"Quality assessment of growing media with near-infrared spectroscopy: Chemical characteristics and plant assays","volume":"73","author":"Bruns","year":"2008","journal-title":"Eur. J. Hortic. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Burns, D.A., and Ciurczak, E.W. (2007). Handbook of Near-Infrared Analysis, CRC Press.","DOI":"10.1201\/9781420007374"},{"key":"ref_52","unstructured":"Osborne, B.G., Fearn, T., and Hindle, P.H. (1993). Practical NIR Spectroscopy with Applications in Food and Beverage Analysis, Longman scientific and technical."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1603\/EN09079","article-title":"Salivary Proteins of Russian Wheat Aphid (Hemiptera: Aphididae)","volume":"39","author":"Cooper","year":"2010","journal-title":"Environ. \u00c8ntomol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1094\/MPMI-01-14-0018-R","article-title":"Suppression of Plant Defenses by a Myzus persicae (Green Peach Aphid) Salivary Effector Protein","volume":"27","author":"Elzinga","year":"2014","journal-title":"Mol. Plant-Microbe Interact."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"118777","DOI":"10.1016\/j.jclepro.2019.118777","article-title":"Biogas generation from insects breeding post production wastes","volume":"244","author":"Bulak","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2849","DOI":"10.1073\/pnas.1013465108","article-title":"Aphid genome expression reveals host-symbiont cooperation in the production of amino acids","volume":"108","author":"Hansen","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chou, S., Chen, J.M., Yu, H., Chen, B., Zhang, X., Croft, H., Khalid, S., Li, M., and Shi, Q. (2017). Canopy-Level Photochemical Reflectance Index from Hyperspectral Remote Sensing and Leaf-Level Non-Photochemical Quenching as Early Indicators of Water Stress in Maize. Remote. Sens., 9.","DOI":"10.3390\/rs9080794"},{"key":"ref_58","unstructured":"Paz, V.S., Mikkelsen, T.N., Johnson, M., Mo, X., Morillas, L., Liu, S., Shen, L., and Garcia, M. (2019, January 7\u201312). Hyperspectral and thermal sensing of stomatal conductance and photosynthesis under water stress for a C3 (soybean) and a C4 (maize) crop. Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.envpol.2018.12.029","article-title":"Using artificial neural network to investigate physiological changes and cerium oxide nanoparticles and cadmium uptake by Brassica napus plants","volume":"246","author":"Rossi","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Park, S., Ryu, D., Fuentes, S., Chung, H., Hern\u00e1ndez-Montes, E., and O\u2019Connell, M. (2017). Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV). Remote. Sens., 9.","DOI":"10.3390\/rs9080828"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s00271-012-0375-8","article-title":"Computational water stress indices obtained from thermal image analysis of grapevine canopies","volume":"30","author":"Fuentes","year":"2012","journal-title":"Irrig. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Fuentes, S., Tongson, E.J., De Bei, R., Viejo, C.G., Ristic, R., Tyerman, S., and Wilkinson, K. (2019). Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach. Sensors, 19.","DOI":"10.3390\/s19153335"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3045","DOI":"10.1016\/S2095-3119(20)63240-3","article-title":"Application of imidacloprid controlled-release granules to enhance the utilization rate and control wheat aphid on winter wheat","volume":"19","author":"Yuan","year":"2020","journal-title":"J. Integr. Agric."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.ecoser.2018.02.019","article-title":"Economic valuation of natural pest control of the summer grain aphid in wheat in South East England","volume":"30","author":"Zhang","year":"2018","journal-title":"Ecosyst. Serv."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, J., Zhou, H., and Chen, J. (2020). Enhancement of Natural Control Function for Aphids by Intercropping and Infochemical Releasers in Wheat Ecosystem. Integr. Biol. Control, 85\u2013116.","DOI":"10.1007\/978-3-030-44838-7_6"},{"key":"ref_66","unstructured":"Peairs, F. (2017). Development of Integrated Pest Management Approaches for Russian Wheat Aphid in Colorado USA, GRDC Update Paper."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Singh, B., and Jasrotia, P. (2020). Impact of integrated pest management (IPM) module on major insect-pests of wheat and their natural enemies in North-western plains of India. J. Cereal Res., 12.","DOI":"10.25174\/2582-2675\/2020\/100185"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Dixon, A., and Kindlmann, P. (1998). Population dynamics of aphids. Insect Populations in Theory and in Practice, Springer.","DOI":"10.1007\/978-94-011-4914-3_9"},{"key":"ref_69","first-page":"1039","article-title":"Population Dynamics of Aphids (Hemiptera: Aphididae) on Wheat Varieties (Triticum aestivum L.) as Affected by Abiotic Conditions in Bahawalpur, Pakistan","volume":"48","author":"Ahmad","year":"2016","journal-title":"Pak. J. Zool."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Brabec, M., Hon\u011bk, A., Pek\u00e1r, S., and Martinkova, Z. (2014). Population Dynamics of Aphids on Cereals: Digging in the Time-Series Data to Reveal Population Regulation Caused by Temperature. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0106228"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/3964376","article-title":"Aphid Identification and Counting Based on Smartphone and Machine Vision","volume":"2017","author":"Xuesong","year":"2017","journal-title":"J. Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"105200","DOI":"10.1016\/j.compag.2019.105200","article-title":"A method for counting and classifying aphids using computer vision","volume":"169","author":"Lins","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.neucom.2020.07.140","article-title":"Recognition and counting of wheat mites in wheat fields by a three-step deep learning method","volume":"437","author":"Chen","year":"2021","journal-title":"Neurocomputing"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/17\/5948\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:56:30Z","timestamp":1760165790000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/17\/5948"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,4]]},"references-count":73,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["s21175948"],"URL":"https:\/\/doi.org\/10.3390\/s21175948","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,4]]}}}