Sensors Applied to Digital Agriculture: A Review

Daniel Marçal de Queiroz, Andre Luiz de Freitas Coelho, Domingos Sárvio Magalhães Valente, John Kenneth Schueller


Sensors are the basis of digital agriculture; they provide data that allows the development of agricultural control and supervisory systems, and it helps analyze the performance of management practices. Further, sensors can be used to provide data for algorithms developed to automate the prescription of inputs. Among the sensors used in agriculture, those used to monitor soil, plants, and crop yield are reviewed in this work. In soil monitoring, the aim is to measure variables associated with the physical and chemical characteristics of soil to evaluate soil fertility and compaction. In plant monitoring, sensors are used to detect diseases and pests, weed infestation, and nutritional stress. Sensors present in the yield monitors of the harvesters allow the generation of yield maps. Finally, remote sensing techniques for predicting crop yields are analyzed owing to their potential applications in crop management.


Yield monitor. Soil sensors; Remote sensing; Proximal sensors; Sensors for crop monitoring

Texto completo:



ABULAIT, Y. et al. A possible fractional order derivative and optimized spectral indices for assessing total nitrogen content in cotton. Computers and Electronics in Agriculture, v. 171, n. 1, p. 1-11, 2020.

ADAMCHUK, V. I. et al. On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, v. 44, n. 1, p. 71-91, 2004.

AHMAD, L.; MAHDI, S.S. Yield Monitoring and Mapping. In: Satellite Farming. Springer, Cham, 2018.

ANASTASIOU, E. et al. Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes. Agriculture, v. 8, n. 7, p. 94-111, 2018.

ANDRADE, R. et al. Proximal sensing applied to soil texture prediction and mapping in Brazil. Geoderma Regional, v. 23, n. 1, p. 321-335, 2020.

ANDÚJAR, D. et al. Weed discrimination using ultrasonic sensors. Weed Research, v. 51, n. 1, p. 543-547, 2011.

BARBEDO, J.G.A. A review on the main challenges in automatic plant disease identification based on visible range images. Biosystem Engineering, v. 144, n. 1, p. 52-60, 2016.

BECK, A. D.; PICKETT, T. D. Automatic mass-flow sensor calibration for a yield monitor.US n. 7650734 B2, 30 mar. 2006, 26 jan 2010.

BENEDET, L. et al. Soil texture prediction using portable X-ray fluorescence spectrometry and visible near-infrared diffuse reflectance spectroscopy. Geoderma, v. 376, n. 1, p. 114553-114565, 2020.

BURKE, M.; LOBELL, D. B. Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proceedings of the National Academy of Sciences of the United States of America, v. 114, n. 9, p. 2189-2194, 2017.

CABRERA-BOSQUET, L. et al. High throughput phenotyping and genomic selection: the frontiers of crop breeding converge. Journal of integrative plant biology, v. 54, n. 5, p. 312-320, 2012.

CASTALDI, F. et al. Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize, Precision Agriculture, v. 17, n. 1, p. 76-94, 2017.

CASTILLO-RUIZ, F. J. et al. Development of a telemetry and Yield-Mapping system of olive harvester. Sensors, v. 15, n. 2, p. 4001-4018, 2015.

CHANGHUA, L. et al. Development of combine grain yield monitor system with self-feedback function. IFAC-PapersOnLine, v. 51, n. 17, p. 408-411, 2018.

CHERGUI, N.; KECHADI, M.-T.; MCDONNELL, M. The Impact of Data Analytics in Digital Agriculture: A Review. In: 2020 International Multi-Conference on: “Organization of Knowledge and Advanced Technologies” (OCTA). p. 1-13, 2020.

CHO, Y.; SUDDUTH, K. A.; CHUNG, S. Soil physical property estimation from soil strength and apparent electrical conductivity sensor data. Biosystems Engineering, v. 152, n. 1, p. 68-78, 2016.

CHUNG, S.-O. et al. Sensing Technologies for Grain Crop Yield Monitoring Systems: A Review. Journal of Biosystems Engineering, v. 41, n. 4, p. 408-417, 2016.

COCKX, L.; VAN MEIRVENNE, M.; DE VOS, B. Using the EM38DD Soil Sensor to Delineate Clay Lenses in a Sandy Forest Soil. Soil Science Society of America Journal, v. 71, n. 4, p. 1314-1322, 2007.

COELHO, A. L. F. et al. An open-source spatial analysis system for embedded systems. Computers and Electronics in Agriculture, v. 154, n. 1, p. 289-295, 2018.

COLAÇO, A.F.; BRAMLEY, R.G.V. Do crop sensors promote improved nitrogen management in grain crops? Field Crops Research, v. 218, n. 1, p. 126-140, 2018.

COLAÇO, A. F. et al. Yield mapping methods for manually harvested crops. Computers and Electronics in Agriculture, v. 177, n. 1, p. 1-14, 2020.

CHRISTY, C.D.; DRUMMOND, P.; LAIRD, D.A. An On-The-Go Spectral Reflectance Sensor for Soil. In: 2003 ASAE Annual Meeting, ASABE Paper No. 031044, 2003.

CORRÊDO, L.D. et al. Sugarcane Harvester for In-field Data Collection: State of the Art, Its Applicability and Future Perspectives. Sugar Tech, v. 1, n. 1, p. 1-14, 2020.

CORWIN, D. L.; LESCH, S. M. Characterizing soil spatial variability with apparent soil electrical conductivity: I. Survey protocols. Computers and Electronics in Agriculture, v. 46, n. 1-3, p. 103-133, 2005.

CORWIN, D. L.; SCUDIERO, E. Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors. Advances in Agronomy, v. 158, n. 1, p. 1-130, 2019.

CUNHA, M. L. P. et al. Diagnosis of the nutritional status of garlic crops. Revista Brasileira de Ciência do Solo, v. 40, n. 1, p. 1-14, 2016.

DARR, M. J et al. Yield measurement and base cutter height control systems for a harvester.US n. 20150124054A1, 29 oct. 2014, 7 may 2014.

DENG, X. et al. A method of electrical conductivity compensation in a low-cost soil moisture sensing measurement based on capacitance. Measurement, v. 150, n. 1, p. 107052-107062, 2020.

DRIEMEIER, C. et al. A computational environment to support research in sugarcane agriculture. Computers and Electronics in Agriculture, v. 130, n. 1, p. 13-19, 2016.

FOUNTAS, S. et al. Site-specific management in an olive tree plantation. Precision Agriculture, v. 12, n. 2, p. 179-195, 2011.

FRASCONIA, C. et al. An automatic machine able to perform variable rate application of flame weeding: design and assembly. Chemical Engineering Transactions, v. 308, n. 1, p. 301-306, 2017.

FU, Y. et al. Predicting soil organic matter from cellular phone images under varying soil moisture. Geoderma, v. 361, n. 1, p. 114020-114030, 2020.

GAN, H. et al. Immature green citrus fruit detection using color and thermal images. Computers and Electronics in Agriculture, v. 152, n. 1, p. 117-125, 2018.

GAO, J. et al. Recognizing weeds in a maize crop using a random forest machine-learning algorithm and near-infrared snapshot mosaic hyperspectral imagery. Biosystem Engineering, v. 170, n. 1, p. 39-50, 2018.

GOLHANI, K. et al. A review of neural networks in plant disease detection using hyperspectral data. Information Processing in Agriculture, v. 5, n. 3, p. 354-371, 2018.

GRAEFF, S. et al. Evaluation of Image Analysis to Determine the N-Fertilizer Demand of Broccoli Plants (Brassica oleracea convar. botrytis var. italica). Advances in Optical Technologies, v. 2008, n. 1, p. 1-8, 2008.

HEEGE, H.J.; REUSCH, S.; THIESSEN, E. Prospects and results for optical systems for site-specific on-the-go control of nitrogen-top-dressing in Germany. Precision Agriculture, v. 9, n. 1, p. 115-131, 2008.

JACQUES, A. A. B. et al. Machine Vision Yield Monitor for Vegetable Crops. St. Joseph, MI: ASABE, 2017.

JENSEN, et al. An assessment of sugarcane yield monitoring concepts and techniques from commercial yield monitoring systems. Proceedings of the Australian Society of Sugar Cane Technology, v. 34, n. 1, p. 1-7, 2012.

JIN, S. et al. Hyperspectral imaging using the single-pixel Fourier transform technique. Scientific Reports, v. 7, n. 1, p. 1-7, 2017.

JIN, Z. et al. Smallholder maize area and yield mapping at national scales with Google Earth Engine. Remote Sensing of Environment, v. 228, n. 1, p. 115-128, 2019.

KALAJI, H.M. et al. A comparison between different chlorophyll content meters under nutrient deficiency conditions, Journal of Plant Nutrition, v. 40, n. 7, p. 1024-1034, 2017.

KAYAD, A. G. et al. Performance Evaluation of Hay Yield Monitoring System in Large Rectangular Baler. American-Eurasian Journal of Agricultural & Environmental Sciences, v. 15, n. 6, p. 1025-1032, 2015.

KHAREL, T. P. et al. Yield monitor data cleaning is essential for accurate corn grain and silage yield determination. Agronomy Journal, v. 111, n. 2, p. 509-516, 2019.

KIRK, K. et al. Development of a Yield Monitor for Peanut Research Plots. In: ASABE Annual International Meeting. ASABE Paper No. 12-1337625. 2012.

KODALI, R.; RAWAT, N.; BOPPANA, L. WSN Sensors for Precision Agriculture. In: IEEE TENSYMP 2014 - 2014 IEEE Region 10 Symposium.p. 651-656, 2014.

KWEON, G.; MAXTON, C. Soil organic matter sensing with an on-the-go optical sensor. Biosystems Engineering, v. 115, n. 1, p. 66-81, 2013.

LENK, S. et al. Multispectral fluorescence and reflectance imaging at the leaf level and its possible applications. Journal of Experimental Botany, v. 58, n. 4, p. 807-814, 2007.

LEROUX, C. et al. general method to filter out defective spatial observations from yield mapping datasets. Precision Agriculture, v. 19, n. 5, p. 789-808, 2018.

LIU, B.; BRUCH, R. Weed Detection for Selective Spraying: A Review. Current Robotics Reports, v. 1, n. 1, p. 19-26, 2020.

LOBELL, D. B. et al. A scalable satellite-based crop yield mapper. Remote Sensing of Environment, v. 164, n. 1, p. 324-333, 2015.

LONG, E. A. et al. Assessment of yield monitoring equipment for dry matter and yield of corn silage and alfalfa/grass. Precision Agriculture, v. 17, n. 5, p. 546-563, 2016.

LOPEZ-GRANADOS, F. Weed detection for site-specific weed management: mapping and real-time approaches. Weed Research, v. 51, n. 1, p. 1-11, 2011.

LOPEZ-GRANADOS, F. et al. Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds. Precision Agriculture, v. 17, n. 1, p. 183-199, 2016.

LOWE, A.; HARRISON, N.; FRENCH, A.P. Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods, v. 13, n. 1, p. 80-92, 2017.

MAHLEIN, A. Plant disease detection by imaging sensors - parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease, v. 100, n. 2, p. 241-251, 2016.

MAILANDER, M. et al. Sugar Cane Yield Monitoring System. Applied Engineering in Agriculture, v. 26, n. 6, p. 965-969, 2010.

MAIMAITIJIANG, M. et al. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, v. 237, n. 1, p. 111599-111619, 2020.

MAJA, J. M.; EHSANI, R. Development of a yield monitoring system for citrus mechanical harvesting machines. Precision Agriculture, v. 11, n. 5, p. 475-487, 2010.

MALDANER, L. F.; MOLIN, J. P. Data processing within rows for sugarcane yield mapping. Scientia Agricola, v. 77, n. 5, p. 1-8, 2020.

MANHÃES, C. M. et al. Visible losses in mechanized harvesting of sugarcane using the Case IH A4000 harvester. American Journal of Plant Sciences, v. 5, n. 1, p. 2734-2740, 2014.

MARTINELLI, F. et al. Advanced methods of plant disease detection. A review. Agronomy for Sustainable Development, v. 35, n. 1, p. 1-25, 2015.

MAUGHAN, J. D. et al. Yield monitoring and mapping systems for hay and forage harvesting: A review. In: ASABE Annual International Meeting 2012, ASABE: Dallas, Texas, ASABE Paper No. 121338184, 2012.

MAVRIDOU, E. et al. Machine Vision Systems in Precision Agriculture for Crop Farming. Journal of Imaging. v. 5, n. 12, p. 89-121, 2019.

MAYRINK, G. O. et al. Determination of chemical soil properties using diffuse reflectance and ion ‑ exchange resins. Precision Agriculture, v. 20, n. 3, p. 541-561, 2019.

MOHAMED, A.; ABOU-AMER, I.; IBRAHIM, S.M. Using GreenSeeker active optical sensor for optimizing maize nitrogen fertilization in calcareous soils of Egypt. Archives of Agronomy and Soil Science, v. 64, n. 8, p. 1083-1093, 2017.

MOMIN, M. A. et al. Sugarcane yield mapping based on vehicle tracking. Precision Agriculture, v. 20, n. 5, p. 896-910, 2019.

NADERI-BOLDAJI, M. et al. A mechanical-dielectric-high frequency acoustic sensor fusion for soil physical characterization. Computers and Electronics in Agriculture, v. 156, n. 1, p. 10-23, 2019.

NAUŠ, J. et al. SPAD chlorophyll meter Reading can be pronouncedly affected by chloroplast movement. Photosynthesis Research, v. 105, n. 3, p. 265-271, 2010.

NOCCO, M. A.; RUARK, M. D.; KUCHARIK, C. J. Apparent electrical conductivity predicts physical properties of coarse soils. Geoderma, v. 335, n. 1, p. 1-11, 2019.

PADILLA, F.M. et al. Proximal Optical Sensors for Nitrogen Management of Vegetable Crops: A Review. Sensors, v. 18, n. 7, p. 2083-2105, 2018.

PAGANI, A.; MALLARINO, A. On-Farm Evaluation of Corn and Soybean Grain Yield and Soil pH Responses to Liming. Agronomy Journal. v. 107, n. 1, p.71-82, 2015.

PATRÍCIO, D. I.; RIEDER, R. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and electronics in agriculture, v. 153, n. 1, p. 69-81, 2018.

PELLETIER, M. G.; WANJURA, J. D.; HOLT, G. A. Electronic Design of a Cotton Harvester Yield Monitor Calibration System. AgriEngineering, v. 1, n. 4, p. 523-538, 2019a.

PELLETIER, M. G.; WANJURA, J. D.; HOLT, G. A. Embedded Micro-Controller Software Design of a Cotton Harvester Yield Monitor Calibration System. AgriEngineering, v. 1, n. 4, p. 485-495, 2019b.

PELLETIER, M. G.; WANJURA, J. D.; HOLT, G. A. Man-Machine-Interface Software Design of a Cotton Harvester Yield Monitor Calibration System. AgriEngineering, v. 1, n. 4, p. 511-522, 2019c.

PETEINATOS, G.G. et al. Potential use of ground-based sensor technologies for weed detection. Pest Management Science, v. 70, n. 1, p. 190-199, 2014.

PISON, G. How many humans tomorrow? The United Nations revises its projections. The Conversation. Waltham, 20 jun. 2019. Retrieved from: <>. Access in 17 nov. 2020.

PRICE, R. R.; JOHNSON, R. M.; VIATOR, R. P. An overhead optical yield monitor for a sugarcane harvester based on two optical distance sensors mounted above the loading elevator. Applied Engineering in Agriculture, v. 33, n. 5, p. 687-693, 2017.

PRICE, R. R. et al. Fiber Optic Yield Monitor for a Sugarcane Harvester. Transactions of the ASABE, v. 54, n. 2007, p. 31-39, 2011.

QUADERER, J. G.; CASH, M.F. Sugar cane yield mapping.US n. 8955402B2, 25 jan. 2013, 17 fev. 2015.

QUEIROZ, D. M. et al. Development and testing of a low-cost portable apparent soil electrical conductivity sensor using a beaglebone black. Applied Engineering in Agriculture, v. 35, n. 3, p. 341-355, 2020.

QUEMADA, M.; GABRIEL, J.L.; ZARCO-TEJADA, P. Airborne hyperspectral images and ground-level optical sensors as assessment tools for maize Nitrogen fertilization. Remote Sensing, v. 6, n. 4, p. 2940-2962, 2014.

RAMSEY, H. G. et al. Development and Testing of a Forage and Hay Yield Monitor for Use on Mowers Written. In: 2015 ASABE Annual International Meeting. ASABE: Louisiana, ASABE Paper No. 152190035, 2015.

RAWAL, A. et al. Determination of base saturation percentage in agricultural soils via portable X-ray fluorescence spectrometer. Geoderma, v. 338, n. 1, p. 375-382, 2019.

REINKE, R. et al. A dynamic grain flow model for a mass flow yield sensor on a combine. Precision Agriculture, v. 12, n. 1, p. 732-749, 2011.

SAGAN, V. et al. UAV/satellite multiscale data fusion for crop monitoring and early stress detection. ISPRS - International Archives of the Photogrammetry, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. XLII-2, n. W13, p. 715-722, 2019.

SANCHES, G. M.; MAGALHÃES, P. S. G.; FRANCO, H. C. J. Site-specific assessment of spatial and temporal variability of sugarcane yield related to soil attributes. Geoderma, v. 334, n. 2, p. 90-98, 2019.

SANKARAN, S. et al. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, v. 72, n. 1, p .1-13, 2010.

SARRON, J. et al. Mango yield mapping at the orchard scale based on tree structure and land cover assessed by UAV. Remote Sensing, v. 10, n. 12, p. 1-21, 2018.

SARTORI, S. et al. Mapping the Spatial Variability of Coffee Yield with Mechanical Harvester. In: Proceedings of the World Congress of Computers in Agriculture and Natural Resources, ASABE: Iguacu Falls, Brazil, p.196-205, 2012.

SCHUELLER, J.K. et al. Combine feed rate sensors. Transactions of the ASAE, v. 28, n. 1, p. 2-5, 1985.

SCHUELLER, J.K. et al. Low-cost automatic yield mapping in hand-harvested citrus. Computers and Electronics in Agriculture. v. 23, n. 2, p. 145-153, 1999.

SEARCY, S.W. et al. Mapping of spatially-variable yield during grain combining. Transactions of the ASAE. v. 32, n. 3, p. 826-829, 1989.

SCHUSTER, J. N. et al. Design and development of a particle flow yield monitor for combine harvesters. In: Annual International Meeting. ASABE, ASABE Paper No. 1800992, 2018.

SEVERTSON, D. et al. Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola. Precision Agriculture, v. 17, n. 6, p. 659-677, 2016.

SILVA, C. B.; DE MORAES, M. A. F. D.; MOLIN, J. P. Adoption and use of precision agriculture technologies in the sugarcane industry of São Paulo state, Brazil. Precision Agriculture, v. 12, n. 1, p. 67-81, 2011.

SU, W.; FENNIMORE, S.A.; SLAUGHTER, D. C. Fluorescence imaging for rapid monitoring of translocation behavior of systemic markers in snap beans for automated crop/weed discrimination. Biosystems Engineering, v. 186, n. 1, p. 156-167, 2019.

SUDDUTH, K. A.; DRUMMOND, S.T. Yield Editor: Software for Removing Errors from Crop Yield Maps. Agronomy Journal, v. 99, n. 1, p. 1471-1482, 2007.

SUDDUTH, K.; DRUMMOND, S. T.; MYERS, D. Yield Editor 2.0: Software for Automated Removal of Yield Map Errors. In: Annual International Meeting, ASABE : Dallas, Texas. ASABE Paper No 121338243, 2012.

SUN, W. et al. An integrated framework for software to provide yield data cleaning and estimation of an opportunity index for site-specific crop management. Precision Agriculture, v. 14, n. 4, p. 376-391, 2013.

TAGARAKIS, A. C. et al. Proximal sensing to estimate yield of brown midrib forage sorghum. Agronomy Journal, v. 109, n. 1, p. 107-114, 2017.

TANG, Y.; JONES, E.; MINASNY, B. Evaluating low-cost portable near infrared sensors for rapid analysis of soils from South Eastern Australia. Geoderma Regional, v. 20, n. 1, p. 1-11, 2020.

TAYLOR, J. A.; MCBRATNEY, A. B.; WHELAN, B. M. Establishing management classes for broadacre agricultural production. Agronomy Journal, v. 99, n. 5, p. 1366-1376, 2007.

TAYLOR, J. A. et al. Evaluation of a commercial grape yield monitor for use mid-season and at-harvest. Journal International des Sciences de la Vigne et du Vin. v. 50, n. 2, p. 57-63, 2016.

THOMAS, S. et al. Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective, Journal of Plant Diseases and Protection, v. 125, n. 1, p. 5-20, 2018.

THOMASSON, J. et al. Optical Peanut Yield Monitor: Development and Testing. Applied Engineering in Agriculture, v. 22, n. 6, p. 809-818, 2006.

THOMASSON, A. et al. Autonomous Technologies in Agricultural Equipment: A Review of the State of the Art. In: Proceedings of the 2019 Agricultural Equipment Technology Conference, ASABE: Louisville, KY. ASABE No Number 913C0119. 2019.

TIAN, Z. et al. Estimating soil bulk density with combined commercial soil water content and thermal property sensors. Soil & Tillage Research, v. 96, n. 1, p. 104445-104453, 2020.

TOSCANO, P. et al. A precision agriculture approach for durum wheat yield assessment using remote sensing data and yield mapping. Agronomy, v. 9, n. 8, p. 1-18, 2019.

VALENTE, D. S. M. et al. Definition of management zones in coffee production fields based on apparent soil electrical conductivity. Scientia Agricola, v. 69, n. 3, p. 173-179, 2012.

VAN ITTERSUM, M.K. et al. Yield gap analysis with local to global relevance - A review. Field Crops Research, v. 143, n .1, p. 4-17, 2013.

VEGA, A. et al. Protocol for automating error removal from yield maps. Precision Agriculture, v. 20, n. 5, p. 1030-1044, 2019.

VISCARRA ROSSEL, R. A.; BOUMA, J. Soil sensing: A new paradigm for agriculture. Agricultural Systems, v. 148, n. 1, p. 71-74, 2016.

VORIES, E. D. et al. Variety effects on cotton yield monitor calibration. Applied Engineering in Agriculture, v. 35, n. 3, p. 345-354, 2019.

WAN, M. et al. Rapid estimation of soil cation exchange capacity through sensor data fusion of portable XRF spectrometry and Vis-NIR spectroscopy. Geoderma, v. 363, n. 1, p. 114163-114171, 2020.

WEI, M. C. F. et al. Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning. AI, v. 1, n. 2, p. 229-241, 2020.

WELTZIEN, C. Digital agriculture or why agriculture 4.0 still offers only modest returns. Landtechnik, v. 71, n. 22, p. 66-68, 2016.

WENDTE, K.W.; SKOTNIKOV, A.; THOMAS, K.K. Sugar cane yield monitor.US n. 6272819B1, 17 nov. 1998, 14 ago. 2001.

WHELAN, B.; TAYLOR, J. Precision agriculture for grain production systems. Csiro publishing. 2013.

WORLDOMETER. Department of economic and social affairs, population division, world population prospects. Retrieved from: . Access in 17 nov. 2020.

WU, W. et al. Global cropping intensity gaps: Increasing food production without cropland expansion. Land Use Policy, v. 76, n. 1, p. 515-525, 2018.

YU, J. et al. Detection of broadleaf weeds growing in turfgrass with convolutional neural networks. Pest Management Science, v. 75, n. 1, p. 2211-2218, 2019.

ZANELLA, M. A. et al. Management class delimitation in a soybean crop using orbital images. Engenharia Agrícola, v. 39, n. 5, p. 676-683, 2019.

ZANDONADI, R. S. et al. Laboratory performance of a low cost mass flow sensor for combines. In: Proceedings of the ASABE Annual International Meeting, ASABE: Rhode Island. ASABE Paper No. 084167, 2008.

ZHANG, J. et al. Monitoring plant diseases and pests through remote sensing technology: A Review. Computers and Electronics in Agriculture, v. 165, n. 1, p. 104943-104957, 2019a.

ZHANG, J. et al. Using a portable active sensor to monitor growth parameters and predict grain yield of winter wheat. Sensors, v. 19, n. 5, p. 1-18, 2019b.

ZHAO, D. et al. Predicting soil physical and chemical properties using vis-NIR in Australian cotton areas. Catena, v. 196, n. 1, p. 104938-104948, 2021.

Revista Ciência Agronômica ISSN 1806-6690 (online) 0045-6888 (impresso), Site:, e-mail: - Fone: (85) 3366.9702 - Expediente: 2ª a 6ª feira - de 7 às 17h.