Original Articles
21 July 2025
Early Access
Recognition of pepper plant and ridge characteristics using an ultrasonic sensor for smart upland crop production
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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Ultrasonic sensing technology can contribute significantly to improving smart agricultural practices by recognizing plants and land features. Accurate detection of these field features is essential for the development of unmanned vehicles, which require precision navigation, obstacle avoidance, and successful field operation. Therefore, the objectives of the study were to employ ultrasonic sensors to detect key parameters of pepper plants and land features, specifically plant height, canopy volume, row spacing, and ridge spacing. Row spacing is the space between rows of plants, and ridge features are the raised soil beds that are often made for planting in upland farming systems. A data collection device was developed and tested in both laboratory and open-field environments. Initially, laboratory tests were conducted to evaluate the sensor accuracy of pepper plant height and canopy volume detection. Following successful validation, field trials were carried out in a pepper cultivation area using a remote-controlled vehicle platform to measure plant height, canopy volume, and row and ridge spacing. An open-source application was used to collect data and visualize the outcomes in real-time. The algorithm presented in the study effectively estimated the height, canopy volume, row spacing, and ridge spacing for pepper plants and associated land features. The results showed plant height of 61.34 and 61.49 cm, canopy volume of 0.29 and 0.31 m³, ridge spacing of 28.88 and 28.94 cm, and row spacing of 44.42 and 43.88 cm, respectively. No significant differences (p>0.05) were found between the measured and estimated plant and land features. Estimation values were strongly correlated with the measured values, with simple linear coefficients of determination (r2) of 0.95, 0.93, 0.88, and 0.81 for height, canopy volume, row spacing, and ridge spacing, respectively. The RMSE of these measurements ranged from 0.93 to 2.08 cm, highlighting relatively high accuracy of the proposed methods. The developed system shows the potential of ultrasonic sensors to develop automatic crop monitoring systems and support smart crop production and be adaptable to greenhouses, open fields or on-farm vehicles to identify different types of plants and land features.
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Abbas, I., Liu, J., Faheem, M., Noor, R.S., Shaikh, S.A., Solangi, K.A., Raza, S.M., 2020. Different sensor-based intelligent spraying systems in agriculture. Sens. Actuators Phys. 316:112265. DOI: https://doi.org/10.1016/j.sna.2020.112265
AL-agele, H.A., Mahapatra, D.M., Nackley, L., Higgins, C., 2022. Economic viability of ultrasonic sensor actuated nozzle height control in center pivot irrigation systems. Agronomy 12:1077. DOI: https://doi.org/10.3390/agronomy12051077
Ali, M., Islam, M.N., Reza, M.N., Hong, J.G., Gulandaz, M.A., Chung, S.O., 2021. Analysis of power requirement of a small-sized tracked-tractor during agricultural field operations. IOP Conf. Ser. Earth Environ. Sci. 924:012017. DOI: https://doi.org/10.1088/1755-1315/924/1/012017
Ali, M., Karim, M.R., Eliezel, H., Gulandaz, M.A., Ali, M.R., Lee, H.S., et al., 2024. Evaluation of gear reduction ratio for a 1.6 kW multi-purpose agricultural electric vehicle platform based on the workload data. Korean J. Agr. Sci. 51:133-146. DOI: https://doi.org/10.7744/kjoas.510204
Alighaleh, P., Gundoshmian, T.M., Alighaleh, S., Rohani, A., 2024. Feasibility and reliability of agricultural crop height measurement using the laser sensor array. Inf. Process. Agric. 11:228-236. DOI: https://doi.org/10.1016/j.inpa.2023.02.005
Antonio, A.S., Wiedemann, L.S.M., Veiga Junior, V.F. 2018. The genus Capsicum: a phytochemical review of bioactive secondary metabolites. RSC Adv. 8:25767-25784. DOI: https://doi.org/10.1039/C8RA02067A
Barik, S., Ponnam, N., Reddy, A.C., Reddy D.C., L., Saha, K., Acharya, G.C., Reddy K., M., 2022. Breeding peppers for industrial uses: progress and prospects. Ind. Crops Prod. 178:114626. DOI: https://doi.org/10.1016/j.indcrop.2022.114626
Boomsma, C.R., Santini, J.B., West, T.D., Brewer, J.C., McIntyre, L.M., Vyn, T.J. 2010. Maize grain yield responses to plant height variability resulting from crop rotation and tillage system in a long-term experiment. Soil Tillage Res. 106:227-240. DOI: https://doi.org/10.1016/j.still.2009.12.006
Bosland, P.W., Votava, E.J. 2000. Peppers: vegetable and spice capsicums, 2nd ed. New York, CABI. pp. 6–9.
Bronson, K.F., French, A.N., Conley, M.M., Barnes, E.M. 2021. Use of an ultrasonic sensor for plant height estimation in irrigated cotton. Agronomy J. 113:2175-2183. DOI: https://doi.org/10.1002/agj2.20552
Chang, A., Eo, Y., Kim, S., Kim, Y., Kim, Y. 2011. Canopy-cover thematic-map generation for Military Map products using remote sensing data in inaccessible areas. Landsc. Ecol. Eng. 7:263–274. DOI: https://doi.org/10.1007/s11355-010-0132-1
Chang, A., Jung, J., Maeda, M.M., Landivar, J. 2017. Crop height monitoring with digital imagery from Unmanned Aerial System (UAS). Comput. Electron. Agric. 141:232–237. DOI: https://doi.org/10.1016/j.compag.2017.07.008
Colaço, A.F., Molin, J.P., Rosell-Polo, J.R., Escolà, A., 2018. Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges. Hortic. Res. 5:1-11. DOI: https://doi.org/10.1038/s41438-018-0043-0
Das, C., Samal, A., Awada, T. 2019. Leveraging image analysis for high-throughput plant phenotyping. Front. Plant Sci. 10:508. DOI: https://doi.org/10.3389/fpls.2019.00508
Escolà, A., Planas, S., Rosell, J.R., Pomar, J., Camp, F., Solanelles ,F., et al., 2011. Performance of an ultrasonic ranging sensor in apple tree canopies. Sensors (Basel) 11:2459-2477. DOI: https://doi.org/10.3390/s110302459
Finkelshtain, R., Yovel, Y., Kosa ,G., Bechar, A. 2015. Detection of plant and greenhouse features using sonar sensors. In: J.V. Stafford (ed.), Precision agriculture '15. Wageningen Academic. pp. 299-306. DOI: https://doi.org/10.3920/978-90-8686-814-8_36
Fisher, D.K., Huang, Y. 2017. Mobile open-source plant-canopy monitoring system. Mod. Instrum. 6:1-13. DOI: https://doi.org/10.4236/mi.2017.61001
Forrest, M.M., Chen, Z., Hassan, S., Raymond, I.O., Alinani, K. 2018. Cost effective surface disruption detection system for paved and unpaved roads. IEEE Access 6:48634-48644. DOI: https://doi.org/10.1109/ACCESS.2018.2867207
Ganeva, D., Roumenina, E., Dimitrov, P., Gikov, A., Jelev, G., Dragov, R., et al., 2022. Phenotypic traits estimation and preliminary yield assessment in different phenophases of wheat breeding experiment based on UAV multispectral images. Remote Sens. 14:1019. DOI: https://doi.org/10.3390/rs14041019
Gupta, C., Tewari, V.K., Machavaram, R., Shrivastava, P., 2022. An image processing approach for measurement of chili plant height and width under field conditions. J. Saudi Soc. Agric. Sci. 21: 171–179. DOI: https://doi.org/10.1016/j.jssas.2021.07.007
Guo, L., Zhang, Q., Han, S., 2002. Agricultural machinery safety alert system using ultrasonic sensors. J. Agric. Saf. Health 8:385-396. DOI: https://doi.org/10.13031/2013.10219
Hosoi, F., Omasa, K., 2009. Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable LiDAR imaging. ISPRS J. Photogramm. Remote Sens. 64:151-158. DOI: https://doi.org/10.1016/j.isprsjprs.2008.09.003
Hunt, E.R., Hively, W.D., Fujikawa, S.J., Linden, D.S., Daughtry, C.S., McCarty, G.W., 2010. Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens. 2:290-305. DOI: https://doi.org/10.3390/rs2010290
Iqbal, F., Lucieer, A., Barry, K., Wells, R., 2017. Poppy crop height and capsule volume estimation from a single UAS flight. Remote Sens. 9:647. DOI: https://doi.org/10.3390/rs9070647
Islam, M.N., Iqbal, M.Z., Ali, M., Chowdhury M., Kabir M.S.N., Park, T., et al., 2020. Kinematic analysis of a clamp-type picking device for an automatic pepper transplanter. Agriculture 10:627. DOI: https://doi.org/10.3390/agriculture10120627
Islam, S., Reza, M.N., Chowdhury, M., Islam, M.N., Ali, M., Kiraga, S., Chung, S.O., 2021. Image processing algorithm to estimate ice-plant leaf area from rgb images under different light conditions. IOP Conf. Ser. Earth Environ. Sci. 924:012013. DOI: https://doi.org/10.1088/1755-1315/924/1/012013
Jeon, H.Y., Zhu, H., Derksen, R., Ozkan, E., Krause, C., 2011. Evaluation of ultrasonic sensor for variable-rate spray applications. Comput. Electron. Agric. 75:213-221. DOI: https://doi.org/10.1016/j.compag.2010.11.007
Kasirajan S., Ngouajio M. 2012. Polyethylene and biodegradable mulches for agricultural applications: a review. Agron. Sustain. Dev. 32:501-529. DOI: https://doi.org/10.1007/s13593-011-0068-3
Leidenfrost, H.T., Tate, T.T., Canning J.R., Anderson M.J., Soule T., Edwards D.B., Frenzel, J.F., 2013. Autonomous navigation of forest trails by an industrial-size robot. T. ASABE 56:1273-1290. DOI: https://doi.org/10.13031/trans.56.9684
Li, W., Niu, Z., Huang, N., Wang, C., Gao, S., Wu, C. 2015. Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China. Ecol. Indic. 57:486-496. DOI: https://doi.org/10.1016/j.ecolind.2015.04.016
Llorens, J., Gil, E., Llop, J., Escolà, A., 2011. Ultrasonic and LIDAR sensors for electronic canopy characterization in vineyards: Advances to improve pesticide application methods. Sensors (Basel) 11: 2177-2194. DOI: https://doi.org/10.3390/s110202177
Maia, A.A.D., de Morais, L.C., 2016. Kinetic parameters of red pepper waste as biomass to solid biofuel. Bioresour. Technol. 204:157–163. DOI: https://doi.org/10.1016/j.biortech.2015.12.055
Mancinelli, R., Muleo, R., Marinari, S., Radicetti, E., 2019. How soil ecological intensification by means of cover crops affects nitrogen use efficiency in pepper cultivation. Agriculture 9:145. DOI: https://doi.org/10.3390/agriculture9070145
Mielcarek, M., Stereńczak, K., Khosravipour, A., 2018. Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation. Int. J. Appl. Earth Obs. Geoinf. 71:132–143. DOI: https://doi.org/10.1016/j.jag.2018.05.002
Miqueloto, T., Winter, F.L., Bernardon, A., Cavalcanti, H.S., Neto, C.D.M., Martins, C.D., Sbrissia, A.F., 2020. Canopy structure of mixed kikuyugrass–tall fescue pastures in response to grazing management. Crop Sci. 60:499-506. DOI: https://doi.org/10.1002/csc2.20005
Moeckel, T., Dayananda, S., Nidamanuri, R.R., Nautiyal, S., Hanumaiah, N., Buerkert, A., Wachendorf, M., 2018. Estimation of vegetable crop parameters by multi-temporal UAV-borne images. Remote Sens. 10:805. DOI: https://doi.org/10.3390/rs10050805
Montazeaud, G., Langrume, C., Moinard, S., Goby, C., Ducanchez, A., Tisseyre, B., Brunel, G., 2021. Development of a low cost open-source ultrasonic device for plant height measurements. Smart Agr. Technol. 1:100022. DOI: https://doi.org/10.1016/j.atech.2021.100022
Navabi, A., Iqbal, M., Strenzke, K., Spaner, D., 2006. The relationship between lodging and plant height in a diverse wheat population. Can. J. Plant Sci. 86:723–726. DOI: https://doi.org/10.4141/P05-144
Nguyen, T., Le, T., Vu, H., Hoang, V., Tran, T. 2018. Crowdsourcing for botanical data collection towards automatic plant identification: A review. Comput. Electron. Agric. 155: 412-425. DOI: https://doi.org/10.1016/j.compag.2018.10.042
Poenaru, V., Badea, A., Cimpeanu, S.M., Irimescu, A. 2015. Multi-temporal multi-spectral and radar remote sensing for agricultural monitoring in the Braila Plain. Agric. Agric. Sci. Procedia 6:506-516. DOI: https://doi.org/10.1016/j.aaspro.2015.08.134
Palleja, T., Landers, A.J. 2015. Real time canopy density estimation using ultrasonic envelope signals in the orchard and vineyard. Comput. Electron. Agric. 115:108-117. DOI: https://doi.org/10.1016/j.compag.2015.05.014
Palleja, T., Landers, A.J., 2017. Real time canopy density validation using ultrasonic envelope signals and point quadrat analysis. Comput. Electron. Agric. 134:43-50. DOI: https://doi.org/10.1016/j.compag.2017.01.012
Saeys, W., Lenaerts, B., Craessaerts, G., Baerdemaeker, J.D., 2009. Estimation of the crop density of small grains using LiDAR sensors. Biosyst. Eng. 102:22-30. DOI: https://doi.org/10.1016/j.biosystemseng.2008.10.003
Scharr, H., Minervini, M., French, A.P., Klukas, C., Kramer, D.M., Liu, X., et al., 2016. Leaf segmentation in plant phenotyping: A collation study. Mach. Vis. Appl. 27:585-606. DOI: https://doi.org/10.1007/s00138-015-0737-3
Schirrmann, M., Giebel, A., Gleiniger, F., Pflanz, M., Lentschke, J., Dammer, K.H., 2016. Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery. Remote Sens. 8:706. DOI: https://doi.org/10.3390/rs8090706
Schor, N., Berman, S., Dombrovsky, A., Elad, Y., Ignat, T., Bechar, A. 2015. A robotic monitoring system for diseases of pepper in greenhouse. In: J.V. Stafford (ed.), Precision agriculture '15. Wageningen Academic. pp. 627-634. DOI: https://doi.org/10.3920/978-90-8686-814-8_78
Scotford, I.M., Miller, P.C.H., 2004. Combination of spectral reflectance and ultrasonic sensing to monitor the growth of winter wheat. Biosyst. Eng. 87:27-38. DOI: https://doi.org/10.1016/j.biosystemseng.2003.09.009
Schumann, A.W., Zaman, Q.U., 2005. Software development for real-time ultrasonic mapping of tree canopy size. Comput. Electron. Agric. 47:25-40. DOI: https://doi.org/10.1016/j.compag.2004.10.002
Sui, R., Baggard, J., 2018. Center-pivot-mounted sensing system for monitoring plant height and canopy temperature. T. ASABE 61:831-837. DOI: https://doi.org/10.13031/trans.12506
Tsetkova, M., Anastasova, E., Polimenov, V., Djamiykov, T., Dimitrova K., 2024. Remote sensing for smart agriculture monitoring pepper crops. Proceedings XXXIII Int. Scientific Conf. Electronics (ET), Sozopol. pp. 1-4. DOI: https://doi.org/10.1109/ET63133.2024.10721488
United Nations, Department of Economic and Social Affairs, Population Division. World population prospects highlights, 2019 revision highlights, 2019 revision. New York, United Nations.
Wei, Z., Xue, X., Salcedo, R., Zhang, Z., Gil, E., Sun, Y., et al., 2023. Key technologies for an orchard variable-rate sprayer: current status and future prospects. Agronomy 13:59. DOI: https://doi.org/10.3390/agronomy13010059
White, J.W., Andrade-Sanchez, P., Gore, M.A., Bronson, K.F., Coffelt, T.A., Conley, M.M., et al., 2012. Field-based phenomics for plant genetics research. Field Crops Res. 133:101-112. DOI: https://doi.org/10.1016/j.fcr.2012.04.003
Wu, J., 2022. Crop growth monitoring system based on agricultural internet of things technology. J. Electr. Comput. Eng. 8466037:1-10. DOI: https://doi.org/10.1155/2022/8466037
Zhao, X., Zhai, C., Wang, S., Dou, H., Yang, S., Wang, X., Chen, L., 2022. Sprayer boom height measurement in wheat field using ultrasonic sensor: An exploratory study. Front. Plant Sci. 13;1008122. DOI: https://doi.org/10.3389/fpls.2022.1008122
Zolkos, S., Goetz, S., Dubayah, R.A. 2013. Meta-analysis of terrestrial aboveground biomass estimation using LiDAR remote sensing. Remote Sens. Environ. 128:289-298. DOI: https://doi.org/10.1016/j.rse.2012.10.017
Supporting Agencies
Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry , Ministry of Agriculture, Food and Rural Affairs of he Republic of KoreaHow to Cite
“Recognition of pepper plant and ridge characteristics using an ultrasonic sensor for smart upland crop production” (2025) Journal of Agricultural Engineering [Preprint]. doi:10.4081/jae.2025.1881.
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