{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:47:55Z","timestamp":1775666875069,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect\u2019s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.<\/jats:p>","DOI":"10.3390\/s21144846","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T10:52:58Z","timestamp":1626432778000},"page":"4846","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Prediction of Pest Insect Appearance Using Sensors and Machine Learning"],"prefix":"10.3390","volume":"21","author":[{"given":"Du\u0161an","family":"Markovi\u0107","sequence":"first","affiliation":[{"name":"Faculty of Agronomy in \u010ca\u010dak, University of Kragujevac, Cara Du\u0161ana 34, 32102 \u010ca\u010dak, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7532-6185","authenticated-orcid":false,"given":"Dejan","family":"Vuji\u010di\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Technical Sciences \u010ca\u010dak, University of Kragujevac, Svetog Save 65, 32102 \u010ca\u010dak, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7853-2328","authenticated-orcid":false,"given":"Sne\u017eana","family":"Tanaskovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Agronomy in \u010ca\u010dak, University of Kragujevac, Cara Du\u0161ana 34, 32102 \u010ca\u010dak, Serbia"}]},{"given":"Borislav","family":"\u0110or\u0111evi\u0107","sequence":"additional","affiliation":[{"name":"Institute Mihailo Pupin d.o.o., Volgina 15, 11060 Belgrade, Serbia"}]},{"given":"Sini\u0161a","family":"Ran\u0111i\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Technical Sciences \u010ca\u010dak, University of Kragujevac, Svetog Save 65, 32102 \u010ca\u010dak, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6078-413X","authenticated-orcid":false,"given":"Zoran","family":"Stamenkovi\u0107","sequence":"additional","affiliation":[{"name":"IHP-Leibniz-Institut f\u00fcr Innovative Mikroelektronik, Im Technologiepark 25, 15236 Frankfurt Oder, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1023\/A:1021171514148","article-title":"Precision agriculture: A challenge for crop nutrition management","volume":"247","author":"Robert","year":"2002","journal-title":"Plant Soil"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shafi, U., Mumtaz, R., Garc\u00eda-Nieto, J., Hassan, S.A., Zaidi, S.A.R., and Iqbal, N. (2019). Precision Agriculture Techniques and Practices: From Considerations to Applications. Sensors, 19.","DOI":"10.3390\/s19173796"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105895","DOI":"10.1016\/j.compag.2020.105895","article-title":"A survey on the 5G network and its impact on agriculture: Challenges and opportunities","volume":"180","author":"Tang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Vitali, G., Francia, M., Golfarelli, M., and Canavari, M. (2021). Crop Management with the IoT: An Interdisciplinary Survey. Agronomy, 11.","DOI":"10.3390\/agronomy11010181"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lima, M.C.F., de Almeida Leandro, D.M.E., 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_6","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.compag.2018.05.012","article-title":"Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review","volume":"151","author":"Chlingaryan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.compag.2018.10.024","article-title":"Forecasting yield by integrating agrarian factors and machine learning models: A survey","volume":"155","author":"Elavarasan","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Abbas, F., Afzaal, H., Farooque, A.A., and Tang, S. (2020). Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms. Agronomy, 10.","DOI":"10.3390\/agronomy10071046"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"71","DOI":"10.3390\/ai2010006","article-title":"Using Machine Learning and Feature Selection for Alfalfa Yield Prediction","volume":"2","author":"Whitmire","year":"2021","journal-title":"AI"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"124026","DOI":"10.1088\/1748-9326\/ab5268","article-title":"Maize yield and nitrate loss prediction with machine learning algorithms","volume":"14","author":"Shahhosseini","year":"2019","journal-title":"Environ. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"126193","DOI":"10.1016\/j.eja.2020.126193","article-title":"Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn","volume":"123","author":"Wang","year":"2021","journal-title":"Eur. J. Agron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2596","DOI":"10.2134\/agronj2018.03.0222","article-title":"Application of Machine Learning Methodologies for Predicting Corn Economic Optimal Nitrogen Rate","volume":"110","author":"Qin","year":"2018","journal-title":"Agron. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3390\/ai1020015","article-title":"Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning","volume":"1","author":"Wei","year":"2020","journal-title":"AI"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Taghizadeh-Mehrjardi, R., Nabiollahi, K., Rasoli, L., Kerry, R., and Scholten, T. (2020). Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models. Agronomy, 10.","DOI":"10.3390\/agronomy10040573"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Betemps, D.L., de Paula, B.V., Parent, S.-\u00c9., Galar\u00e7a, S.P., Mayer, N.A., Marodin, G.A.B., Rozane, D.E., Natale, W., Melo, G.W.B., and Parent, L.E. (2020). Humboldtian Diagnosis of Peach Tree (Prunus persica) Nutrition Using Machine-Learning and Compositional Methods. Agronomy, 10.","DOI":"10.3390\/agronomy10060900"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, J., He, Y., Yuan, L., Liu, P., Zhou, X., and Huang, Y. (2019). Machine Learning-Based Spectral Library for Crop Classification and Status Monitoring. Agronomy, 9.","DOI":"10.3390\/agronomy9090496"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Naeem, S., Ali, A., Chesneau, C., Tahir, M.H., Jamal, F., Sherwani, R.A.K., and Ul Hassan, M. (2021). The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach. Agronomy, 11.","DOI":"10.3390\/agronomy11020263"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e20001","DOI":"10.1002\/ppj2.20001","article-title":"Plant segmentation by supervised machine learning methods","volume":"3","author":"Adams","year":"2020","journal-title":"Plant Phenome. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hashim, I.C., Shariff, A.R.M., Bejo, S.K., Muharam, F.M., and Ahmad, K. (2021). Machine-Learning Approach Using SAR Data for the Classification of Oil Palm Trees That Are Non-Infected and Infected with the Basal Stem Rot Disease. Agronomy, 11.","DOI":"10.3390\/agronomy11030532"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.geoderma.2018.11.044","article-title":"Estimation of soil temperature from meteorological data using different machine learning models","volume":"338","author":"Feng","year":"2019","journal-title":"Geoderma"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2019.11.008","article-title":"Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods","volume":"160","author":"Kamir","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Balducci, F., Impedovo, D., and Pirlo, G. (2018). Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement. Machines, 6.","DOI":"10.3390\/machines6030038"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, C.-H., Kung, H.-Y., and Hwang, F.-J. (2019). Deep Learning Techniques for Agronomy Applications. Agronomy, 9.","DOI":"10.3390\/agronomy9030142"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11119-014-9372-7","article-title":"A review of advanced machine learning methods for the detection of biotic stress in precision crop protection","volume":"16","author":"Behmann","year":"2015","journal-title":"Precis. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"34","DOI":"10.3390\/ai2010004","article-title":"Testing the Suitability of Automated Machine Learning for Weeds Identification","volume":"2","author":"Malounas","year":"2021","journal-title":"AI"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"198","DOI":"10.3390\/ai1020013","article-title":"A Study on CNN-Based Detection of Psyllids in Sticky Traps Using Multiple Image Data Sources","volume":"1","author":"Barbedo","year":"2020","journal-title":"AI"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"328","DOI":"10.54386\/jam.v19i4.600","article-title":"Development and validation of weather based prediction model for Helicoverpa armigera in chickpea","volume":"19","author":"Sagar","year":"2017","journal-title":"J. Agrometeorol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecolmodel.2017.12.019","article-title":"Predicting Heliothis (Helicoverpa armigera) pest population dynamics with an age-structured insect population model driven by satellite data","volume":"369","author":"Blum","year":"2018","journal-title":"Ecol. Model."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.agrformet.2016.07.009","article-title":"Prediction of Helicoverpa armigera Hubner on pigeonpea during future climate change periods using MarkSim multimodel data","volume":"228","author":"Mathukumalli","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_31","unstructured":"(2021, March 05). Portal Prognozno-Izve\u0161tajne Slu\u017ebe Za\u0161tite Bilja. Available online: http:\/\/www.pissrbija.com\/default.aspx."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"348","DOI":"10.3102\/1076998619832248","article-title":"Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language","volume":"44","author":"Hao","year":"2019","journal-title":"J. Educ. Behav. Stat."},{"key":"ref_33","unstructured":"(2021, March 02). Scikit-Learn, Machine Learning in Python. Available online: https:\/\/scikit-learn.org."},{"key":"ref_34","unstructured":"Visa, S., Inoue, A., and Ralescu, A. (2011, January 16\u201317). Confusion Matrix-based Feature Selection. Proceedings of the Twenty Second Midwest Artificial Intelligence and Cognitive Science Conference, Cincinnati, OH, USA."},{"key":"ref_35","unstructured":"(2021, March 07). Pessl Instruments Hygroclip (Air Temperature and Relative Humidity), Part No. A660611. Available online: https:\/\/metos.at\/portfolio\/hygroclip-relative-humidity-and-air-temperature-sensor."},{"key":"ref_36","unstructured":"(2021, March 04). iMETOS 3.3. Available online: https:\/\/metos.at\/imetos33."},{"key":"ref_37","unstructured":"(2021, March 04). iMETOS\u00ae 3.3 Manual: Content. Available online: https:\/\/metos.at\/imetos-3-3-manual."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1111\/j.1461-9563.2009.00466.x","article-title":"Host-parasitoid population density prediction using artificial neural networks: Diamondback moth and its natural enemies","volume":"12","author":"Tonnang","year":"2010","journal-title":"Agric. For. Entomol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1017\/S1742758411000336","article-title":"Predicting the oriental fruit fly Bactrocera dorsalis (Diptera: Tephritidae) trap catch using artificial neural networks: A case study","volume":"31","author":"Jayanthi","year":"2011","journal-title":"Int. J. Trop. Insect Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Skawsang, S., Nagai, M., Tripathi, N.K., and Soni, P. (2019). Predicting Rice Pest Population Occurrence with Satellite-Derived Crop Phenology, Ground Meteorological Observation, and Machine Learning: A Case Study for the Central Plain of Thailand. Appl. Sci., 9.","DOI":"10.3390\/app9224846"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4846\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:31:24Z","timestamp":1760164284000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/14\/4846"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,16]]},"references-count":40,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21144846"],"URL":"https:\/\/doi.org\/10.3390\/s21144846","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,16]]}}}