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  Eur.J.Hortic.Sci. 81 (2) 78-90 | DOI: 10.17660/eJHS.2016/81.2.2
ISSN 1611-4426 print and 1611-4434 online | © ISHS 2016 | European Journal of Horticultural Science | Original article

Applications of precision agriculture in horticultural crops

M. Zude-Sasse1,2, S. Fountas3, T.A. Gemtos4 and N. Abu-Khalaf5,6
1Leibniz Institute for Agricultural Engineering Potsdam-Bornim, Potsdam, Germany
2Beuth University of Applied Sciences Berlin, Berlin, Germany
3Department of Natural Resource Management and Agricultural Engineering, Agricultural University of Athens, Athens, Greece
4School of Agricultural Sciences, University of Thessaly, Volos, Greece
5Technical and Applied Research Center (TARC), Palestine Technical University – Kadoorie, Tulkarm, Palestine
6Faculty of Agricultural Sciences and Technology, Palestine Technical University – Kadoorie, Tulkarm, Palestine

SUMMARY
Farmer and consumer are driving the request for sustainable production of fruit and vegetables. Precision agriculture, the consideration of spatial and temporal variability for increasing the efficiency of resources, has been developed over the last twenty-five years and was initially applied to field crops. Its application to tree crops and vegetables started later and has been developing with an increasing number of publications as well as research calls in the beginning of the 21st century. First applications were described for mechanical harvesting of horticultural crops with commercial solutions for harvesting fruit that is subjected to processing. A review of methodical approaches and upcoming challenges for precise management of tree crops and vegetables are covered in this paper, addressing horticulturists as well as researchers working in precision agriculture. The precision agriculture domains with specific implications in horticultural crops captured are: data collection, yield mapping, remote sensing, quality mapping, and variable rate application. The spatial and temporal variability in orchards as well as effects of site-specific application of inputs are documented in this paper.

Keywords arable farming, horticulture, orchard, precision agriculture, precision farming, precision fruticulture, precision horticulture, quality, site-specific management, tree fruits, variable rate applications, vegetables, yield

Significance of this study

What is already known on this subject?

  • Obtaining spatial and temporal data have been targeted in the production of fruit and vegetables aimed at characterizing its variability and enable adaptive measures.
What are the new findings?
  • Approaches of data collection, yield monitor, remote sensing, quality mapping, and variable rate application are reviewed in the present paper.
What is the expected impact on horticulture?
  • The concept of precision agriculture has been applied in horticultural research for 12 years. At the same time information and communication technology has been developed rapidly with the new challenges of big data and agriculture 4.0. The adaptive, precise management in horticultural production is part of it.

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E-mail: mzude@atb-potsdam.de  

References

  • Abu-Khalaf, N., and Bennedsen, B.S. (2004). Near infrared (NIR) technology and multivariate data analysis for sensing taste attributes of apples. International Agrophysics 18, 203–212.

  • Ač, A., Malenovský, Z., Olejníčková, J., Gallé, A., Rascher, U., and Mohammed, G. (2015). Meta-analysis assessing potential of steady-state chlorophyll fluorescence for remote sensing detection of plant water, temperature and nitrogen stress plant. Remote Sensing of Environment 168, 420–436. https://doi.org/10.1016/j.rse.2015.07.022.

  • Agam, N., Segal, E., Peeters, A., Levi, A., Dag, A., Yermiyahu, U., and Ben-Gal, A. (2014). Spatial distribution of water status in irrigated olive orchards by thermal imaging. Precision Agriculture 15, 346–359. https://doi.org/10.1007/s11119-013-9331-8.

  • Aggelopoulou, K.D., Wulfsohn, D., Fountas, S., Gemtos, T.A., Nanos, G.D., and Blackmore, S. (2010). Spatial variation in yield and quality in a small apple orchard. Precision Agriculture 11, 538–556. https://doi.org/10.1007/s11119-009-9146-9.

  • Aggelopoulou, K., Bochtis, D., Fountas, F., Swain, K.C., Gemtos, T., and Nanos, G. (2011). Yield prediction in apples based on image processing. Precision Agriculture 12, 448–456. https://doi.org/10.1007/s11119-010-9187-0.

  • Aggelopoulou, K., Castrignanň, A., Gemtos, T.A., and De Benedetto, D. (2013). Delineation of management zones in an apple orchard in Greece using a multivariate approach. Computers and Electronics in Agriculture 90, 119–130. https://doi.org/10.1016/j.compag.2012.09.009.

  • Akdemir, B., Belliturk, K., Sisman, C.B., and Blackmore, S. (2005). Spatial distribution in a dry onion field (a precision farming application in Turkey). Journal of Central European Agriculture 6, 211–222.

  • Ampatzidis, Y.G., Vougioukas, S.G., Bochtis, D.D., and Tsatsarelis, C.A. (2009). A yield mapping system for hand-harvested fruits based on RFID and GPS location technologies: field testing. Precision Agriculture 10, 63–72. https://doi.org/10.1007/s11119-008-9095-8.

  • Ampatzidis, Y.G., Whiting, M.D., Liu, B., et al. (2013). Portable weighing system for monitoring picker efficiency during manual harvest of sweet cherry. Precision Agriculture 14, 162–171. https://doi.org/10.1007/s11119-012-9284-3.

  • Anastassiu, H.T., Vougioukas, S., Fronimos, T., Regen, C., Petrou, L., Zude, M., and Käthner, J. (2014). A computational model for path loss in wireless sensor networks in orchard environments. Sensors 14, 5118–5135. https://doi.org/10.3390/s140305118.

  • Baranyai, L., and Zude, M. (2009). Analysis of laser light propagation in kiwifruit using backscattering imaging and Monte Carlo simulation. Computers and Electronics in Agriculture 69, 33–39. https://doi.org/10.1016/j.compag.2009.06.011.

  • Bellon-Maurel, V., Fernandez-Ahumada, E., Palagos, B., et al. (2010). Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy. TRAC – Trends in Analytical Chemistry 29, 1073–1081. https://doi.org/10.1016/j.trac.2010.05.006.

  • Bellon-Maurel, V., Peters, G.M., Clermidy, S., Frizarin, G., Sinfort, C., Ojeda, H., Roux, P., and Short, M.D. (2014). Streamlining life cycle inventory data generation in agriculture using traceability data and information and communication technologies – Part II: Application to viticulture. Journal of Cleaner Production 87, 119–129. https://doi.org/10.1016/j.jclepro.2014.09.095.

  • Bendig, J.V. (2015). Unmanned aerial vehicles (UAVs) for multi-temporal crop surface modelling. A new method for plant height and biomass estimation based on RGB-imaging. Doctoral dissertation, University of Köln, Germany.

  • Berni, J.A.J., Zarco-Tejada, P.J., Sepulcre-Cantó, G., Fereres, E., and Villalobos, F. (2009). Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sensing of Environment 113, 2380–2388. https://doi.org/10.1016/j.rse.2009.06.018.

  • Betemps, D.L., Fachinello, J.C., Galarca, S.P., Portela, N.M., Remorini, D., Massai, R., and Agati, G. (2012). Non-destructive evaluation of ripening and quality traits in apples using a multiparametric fluorescence sensor. Journal of the Science of Food and Agriculture 92, 1855–1864. https://doi.org/10.1002/jsfa.5552.

  • Blackmore, S., Godwin, R., and Fountas, S. (2003). The analysis of spatial and temporal trends in yield map data over six years. Biosystems Engineering 84, 455–466. https://doi.org/10.1016/S1537-5110(03)00038-2.

  • Blasco, J., Aleixos, N., Cubero, S., et al. (2012). Fruit, vegetable and nut quality evaluation and control using computer vision. In Computer vision technology in the food and beverage industries, D.W. Sun, ed. pp. 379–399. https://doi.org/10.1533/9780857095770.3.379.

  • Calfapietra, C., Peńuelas, J., and Niinemets, Ü. (2015). Urban plant physiology: adaptation-mitigation strategies under permanent stress. Trends in Plant Science 20, 72–75. https://doi.org/10.1016/j.tplants.2014.11.001.

  • Castillo-Ruiz, F.J., Pérez-Ruiz, M., Blanco-Roldán, G.L., Gil-Ribes, J.A., and Agüera, J. (2015). Development of a telemetry and yield-mapping system of olive harvester. Sensors 15, 4001–4018. https://doi.org/10.3390/s150204001.

  • Cen, H., and He, Y. (2007). Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science & Technology 18, 72–83. https://doi.org/10.1016/j.tifs.2006.09.003.

  • Chang, Y., Chung, S.O., et al. (2011). Measurement of agricultural atmospheric factors using ubiquitous sensor network – temperature, humidity and light intensity. Journal of Biosystems Engineering 36, 122–129. https://doi.org/10.5307/JBE.2011.36.2.122.

  • Clarke, T.R. (1997). An empirical approach for detecting crop water stress using multispectral airborne sensors. HortTechnology 7, 9–16.

  • Cohen, Y., Alchanatis, V., Prigojin, A., Levi, A., and Soroker, V. (2012). Use of aerial thermal imaging to estimate water status of palm trees. Precision Agriculture 13, 123–140. https://doi.org/10.1007/s11119-011-9232-7.

  • Colaço, A.F., Trevisan, R.G., Karp, F.H.S., and Molin, J.P. (2015). Yield mapping methods for manually harvested crops. Precision Agriculture 15, 225–232. https://doi.org/10.3920/978-90-8686-814-8_27.

  • Cubeddu, R., D’Andrea, C., Pifferi, A., Taroni, P., Torricelli, A., Valentini, G., Dover, C., Johnson, D., Ruiz-Altisent, M., and Valero, C. (2001). Non-destructive quantification of chemical and physical properties of fruits by time-resolved reflectance spectroscopy in the wavelength range 650–1000 nm. Applied Optics 40, 538–543. https://doi.org/10.1364/AO.40.000538.

  • Dale, M.R.T. (1999). Spatial pattern analysis in plant ecology (UK: Cambridge University Press). https://doi.org/10.1017/cbo9780511612589.

  • Das, J., Cross, G., Qu, C., Makineni, A., Tokekar, P., Mulgaonkar, Y., and Kumar, V. (2015). Devices, systems, and methods for automated monitoring enabling precision agriculture. In Automation Science and Engineering (CASE), 2015 IEEE International Conference. pp. 462–469. https://doi.org/10.1109/coase.2015.7294123.

  • De Ell, J.R., Prange, R.K., and Murr, D.P. (1998). Chlorophyll fluorescence techniques to detect atmospheric stress in stored apples. Acta Hortic. 464, 127–134. https://doi.org/10.17660/ActaHortic.1998.464.16.

  • Ehsani, R., and Karim, D. (2010). Yield monitors for specialty crops. In Advanced engineering systems for specialty crops: A review of precision agriculture for water, chemical, and nutrient, VTI Agriculture and Forestry Research 59, No. 309.2009, S. Upadhyaya, K. Giles, S. Haneklaus, and E. Schnug, eds. (Braunschweig, Germany: Johann Heinrich von Thünen-Institut). pp. 31–43.

  • Farooque, A.A., Zaman, Q.U., and Schumann, A.W. (2012). Delineating management zones for site-specific fertilization in wild blueberry fields. Applied Engineering in Agriculture 28, 57–70. https://doi.org/10.13031/2013.41286.

  • Felderhof, L., and Gillieson, D. (2011). Near-infrared imagery from unmanned aerial systems and satellites can be used to specify fertilizer application rates in tree crops. Canadian Journal of Remote Sensing 37, 376–386. https://doi.org/10.5589/m11-046.

  • Fernandez, J.E., and Cuevas, M.V. (2010). Irrigation scheduling from stem diameter variations: A review. Agricultural and Forest Meteorology 150, 135–151. https://doi.org/10.1016/j.agrformet.2009.11.006.

  • Fernandez-Pacheco, D.G., Molina-Martinez, J.M., Jimenez, M., et al. (2014). SCADA Platform for regulated deficit irrigation management of almond trees. Journal of Irrigation and Drainage Engineering 140, 04014008. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000718.

  • Fischer, F., Hoppe, D., Schleicher, E., Mattausch, G., Flaske, H., Bartel, R., and Hampel, U. (2008). An ultra-fast electron beam X-ray tomography scanner. Meas. Sci. Technol. 19, 094002. https://doi.org/10.1088/0957-0233/19/9/094002.

  • Fountas, S., Blackmore, S., Gemtos, T.A., and Markinos, T. (2004). Trend yield maps in Greece and the UK. In Proceedings of the 2nd HAICTA Conference, M. Vlachopoulou, V. Manthou, L. Illiadis, S. Gertsis, and M. Salampasis, eds. (Thessaloniki: Yiahoudis-Yiapoulis). pp. 309–319.

  • Fountas, S., Wulfsohn, D., Blackmore, S., Jacobsen, H.L., and Pedersen, S.M. (2006). A model of decision making and information flows for information-intensive agriculture. Agricultural Systems 87, 192–210. https://doi.org/10.1016/j.agsy.2004.12.003.

  • Fountas, S., Aggelopoulou, K., Bouloulis, C., Nanos, G.D., Wulfsohn, D., Gemtos, T.A., Paraskevopoulos, A., and Galanis, M. (2011). Site-specific management in an olive tree plantation. Precision Agriculture 12, 179–195. https://doi.org/10.1007/s11119-010-9167-4.

  • Fountas, S., Anastasiou, E., Xanthopoulos, G., Lambrinos, G., Manolopoulou, E., Apostolidou, S., Lentzou, D., and Tsiropoulos, Z. (2015). Precision agriculture in watermelons. 10th European Conference on Precision Agriculture, Tel Aviv, Israel, July, 2015. https://doi.org/10.3920/978-90-8686-814-8_25.

  • Fukatsu, T., Endo, G., Ito, Y., Kobayashi, K., and Saito, Y. (2014). Mobile robotic field server for field-scale and fruit-scale crop monitoring. Agricultural Information Research 23, 140–153. https://doi.org/10.3173/air.23.140.

  • Garcia-Ruiz, F., Sankaran, S., Maja, J.M., et al. (2013). Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Computers and Electronics in Agriculture 91, 106–115. https://doi.org/10.1016/j.compag.2012.12.002.

  • Gebbers, R., and Adamchuk, V. (2010). Precision agriculture and food security. Science 12, 828–831. https://doi.org/10.1126/science.1183899.

  • Giles, D.K., Delwiche, M.J., and Dodd, R.B. (1988). Electronic measurement of tree canopy volume. Transactions of the ASAE 31(1), 264–272. https://doi.org/10.13031/2013.30698.

  • Gobrecht, A., Bendoula, R., Roger, J.M., and Bellon-Maurel, V. (2015). Combining linear polarization spectroscopy and the representative layer theory to measure the Beer-Lambert law absorbance of highly scattering materials. Analytica Chimica Acta 853, 486–494. https://doi.org/10.1016/j.aca.2014.10.014.

  • Gonzalez-Dugo, V., Zarco-Tejada, P., Nicolas, E., et al. (2013). Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precision Agriculture 14, 660–678. https://doi.org/10.1007/s11119-013-9322-9.

  • Goodwin, I., O’Connell, M., and Whitfield, D. (2008). Optimising irrigation management units in a nectarine orchard. Australian Fruitgrower 2, 28–30.

  • Guillen-Climent, M.L., Zarco-Tejada, P.J., Berni, J.A.J., et al. (2012). Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV. Precision Agriculture 13, 473–500. https://doi.org/10.1007/s11119-012-9263-8.

  • Guo, X.M., Yang, X.T., Chen, M.X., et al. (2015). A model with leaf area index and apple size parameters for 2.4 GHz radio propagation in apple orchards. Precision Agriculture 16, 180–200. https://doi.org/10.1007/s11119-014-9369-2.

  • Hedley, C. (2015). The role of precision agriculture for improved nutrient management on farms. Journal of the Science of Food and Agriculture 95, 12–19. https://doi.org/10.1002/jsfa.6734.

  • Herold, B., Geyer, M., and Studman, C.J. (2001). Fruit contact pressure distributions - equipment. Comp. Electron. Agric. 32, 167–179. https://doi.org/10.1016/S0168-1699(01)00160-0.

  • Hodrius, M., Migdall, S., Bach, H., and Hank, T. (2015). The impact of multi-sensor data assimilation on plant parameter retrieval and yield estimation for sugar beet. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, Volume XL-7/W3, 36th International Symposium on Remote Sensing of Environment, 11–15 May 2015, Berlin, Germany. https://doi.org/10.5194/isprsarchives-xl-7-w3-19-2015.

  • Hofstee, J.W., and Molema, G.J. (2002). Machine vision based yield mapping of potatoes. Paper No. 02-1200 (St. Joseph, MI, USA: ASAE).

  • Hsiao, S-C., Chen, S.M., Yang, I.C., Chena, C-T., Tsai, C-Y., Chuang, Y-K., Wang, F-J., Chen, Y-L., Lin, T-S., and Lo, Y.M. (2010). Evaluation of plant seedling water stress using dynamic fluorescence index with blue LED-based fluorescence imaging. Computers and Electronics in Agriculture 72, 127–133. https://doi.org/10.1016/j.compag.2010.03.005.

  • Johnson, L., Pierce, L., Michaelis, A., Scholasch, T., and Nemani, R. (2006). Remote sensing and water balance modeling in California drip-irrigated vineyards. In Proceedings of ASCE World Environmental & Water Resources Congress, R. Graham, ed., pp. 1–9. https://doi.org/10.1061/40856(200)293.

  • Jones, H. (1992). Plants and microclimate, a quantitative approach to environmental plant physiology (UK: Cambridge University Press).

  • Kameoka, T., and Hashimoto, A. (2009). In Optical Monitoring of Fresh and Processed Agricultural Crops, M. Zude, ed. (CRC Press). pp. 576.

  • Käthner, J., and Zude-Sasse, M. (2015). Interaction of 3D soil electrical conductivity and generative growth in Prunus domestica. European Journal of Horticultural Science 80(5), 231–239. https://doi.org/10.17660/eJHS.2015/80.5.5.

  • Konopatzki, M.R., Souza, E.G., Nóbrega, L.H., Bazzi, C.L., and Rocha, D.M. (2015). Spatial variability of chemical attributes of the soil, plant and yield in a pear orchard. Journal of Plant Nutrition 39(3), 323–336. https://doi.org/10.1080/01904167.2015.1014562.

  • Kozukue, N., and Friedman, M. (2003). Tomatine, chlorophyll, β-carotene and lycopene content in tomatoes during growth and maturation. Journal of the Science of Food and Agriculture 83, 195–200. https://doi.org/10.1002/jsfa.1292.

  • Kuckenberg, J., Tartachnyk, I., and Noga, G. (2008). Evaluation of fluorescence and remission techniques for monitoring changes in peel chlorophyll and internal fruit characteristics in sunlit and shaded sides of apple fruit during shelf-life. Postharvest Biology and Technology 48, 231–241. https://doi.org/10.1016/j.postharvbio.2007.10.013.

  • Kurata, Y., Tsuchida, T., and Tsuchikawa, S. (2013). Time-of-flight near-infrared spectroscopy for nondestructive measurement of internal quality in grapefruit. J. Am. Soc. Hortic. Sci. 138, 225–228.

  • Lee, K.H., and Ehsani, R. (2009). Quantification of citrus tree geometric characteristics. Appl. Engin. Agric. 25, 777–788. https://doi.org/10.13031/2013.28846.

  • Liakos, V., Tagarakis, A., Aggelopoulou, K., Kleftaki, X., Mparas, G., Fountas, S., and Gemtos, T. (2011). Yield prediction in a commercial apple orchard by analyzing RGB and multi-spectral images of trees during flowering period. In Precision Agriculture, Proceedings of the 8th European Conference on Precision Agriculture, J. Stafford, ed. (Prague: Czech Centre for Science and Society). pp. 617–627.

  • Lichtenthaler, H.K., Langsdorf, G., and Buschmann, C. (2012). Multicolor fluorescence images and fluorescence ratio images of green apples at harvest and during storage. Israel Journal of Plant Sciences 60, 97–106. https://doi.org/10.1560/IJPS.60.1-2.97.

  • Lopez-Granados, F., Jurado-Exposito, M., Alammo, S., and Garcia-Torres, L. (2004). Leaf nutrient spatial variability and site-specific fertilization maps within olive (Olea europaea L.) orchards. European Journal of Agronomy 21, 209–222. https://doi.org/10.1016/j.eja.2003.08.005.

  • Lorente, D., Aleixos, N., Gomez-Sanchis, J., et al. (2012). Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food and Bioprocess Technology 5, 1121–1142. https://doi.org/10.1007/s11947-011-0725-1.

  • Lu, R. (2004). Multispectral imaging for predicting firmness and soluble solids content of apple fruit. Postharvest Biol. Technol. 31, 147–157. https://doi.org/10.1016/j.postharvbio.2003.08.006.

  • Maes, W.H., and Steppe, K. (2012). Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review. Journal of Experimental Botany 63, 4671–4712. https://doi.org/10.1093/jxb/ers165.

  • Maiwald, M., Eppich, B., Ginolas, A., Sumpf, B., Erbert, G., and Tränkle, G. (2015). Compact handheld probe for shifted excitation Raman difference spectroscopy with implemented dual-wavelength diode laser at 785 nanometers. Applied Spectroscopy 69, 1144–1151. https://doi.org/10.1366/15-07858.

  • Mann, K.K., Schumann, A.W., and Obreza, T.A. (2010). Delineating productivity zones in a citrus grove using citrus production, tree growth and temporally stable soil data. Precision Agriculture 12, 457–472. https://doi.org/10.1007/s11119-010-9189-y.

  • Martinez Rach, M., Migallon Gomis, H., Lopez Granado, O., et al. (2013). On the design of a bioacoustic sensor for the early detection of the red palm weevil. Sensors 13, 1706–1729. https://doi.org/10.3390/s130201706.

  • Mathanker, S.K., Weckler, P.R., and Bowser, T.J. (2013). X-Ray applications in food and agriculture: a review. Transactions of the ASABE 56, 1227–1239.

  • Matsushima, U., Graf, W., Zabler, S., Manke, I., Dawson, M., Choinka, G., Hilger, A., and Herppich, W. (2013). 3D-analysis of plant microstructures: as advantages and limitations of synchrotron X-ray microtomography. International Agrophysics 27, 23–30.

  • Mazloumzadeh, S.M., Shamsi, M., and Nezamabadi-pour, H. (2010). Fuzzy logic to classify date palm trees based on some physical properties related to precision agriculture. Precision Agriculture 11, 258–273. https://doi.org/10.1007/s11119-009-9132-2.

  • Meena, M.K., Sharma, D.D., and Meena, O.P. (2015). Effect of different weed management practices on weed population, yield potential and nutrient status of peach cv. July Elberta. Research on Crops 16, 519–525. https://doi.org/10.5958/2348-7542.2015.00073.X.

  • Mendez, V., Rosell-Polo, J.R., Sanz, R., et al. (2014). Deciduous tree reconstruction algorithm based on cylinder fitting from mobile terrestrial laser scanned point clouds. Biosystems Engineering 124, 78–88. https://doi.org/10.1016/j.biosystemseng.2014.06.001.

  • Merzlyak, M.N., Solovchenko, A.E., and Gitelson, A.A. (2003). Reflectance spectral features and non-destructive estimation of chlorophyll, carotenoid and anthocyanin content in apple fruit. Postharvest Biology and Technology 27, 197–211. https://doi.org/10.1016/S0925-5214(02)00066-2.

  • Moeller, M., Alchanatis, V., Cohen, Y., et al. (2007). Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. Journal of Experimental Botany 58, 827–838. https://doi.org/10.1093/jxb/erl115.

  • Moreda, G.P., Munoz, M.A., Ruiz-Altisent, M., et al. (2012). Shape determination of horticultural produce using two-dimensional computer vision – A review. Journal of Food Engineering 108, 245–261. https://doi.org/10.1016/j.jfoodeng.2011.08.011.

  • Nedbal, L., Soukupova, J., Whitmarsh, J., et al. (2000). Postharvest imaging of chlorophyll fluorescence from lemons can be used to predict fruit quality. Photosynthetica 38, 571–579. https://doi.org/10.1023/A:1012413524395.

  • Nguyen, D.T., Erkinbaev, C., Tsuta, M., De Baerdmaeker, J., Nicolaď, B., and Saeys, W. (2014). Spatially resolved diffuse reflectance in the visible and near-infrared wavelength range for non-destructive quality assessment of ‘Braeburn’ apples. Postharvest Biology Technology 91, 39–48. https://doi.org/10.1016/j.postharvbio.2013.12.004.

  • Nicolai, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I., and Lammertyn, J. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biology and Technology 46, 99–118. https://doi.org/10.1016/j.postharvbio.2007.06.024.

  • Nink, S., Hill, J., Buddenbaum, H., Stoffels, J., Sachtleber, T., and Langshausen, J. (2015). Assessing the suitability of future multi- and hyperspectral satellite systems for mapping the spatial distribution of Norway spruce timber volume. Remote Sensing 7, 12009–12040. https://doi.org/10.3390/rs70912009.

  • OECD (1998). Guidance on objective tests for determining the ripeness of fruit.

  • Olsen, K.L., Schomer, H.A., and Bartram, R.D. (1969). Segregation of ‘Golden Delicious’ apples for quality by light transmission. Proc. Am. Soc. Hortic. Sci. 91, 821–828.

  • Panda, S.S., Hoogenboom, G., and Paz, J.O. (2010). Remote sensing and geospatial technological applications for site-specific management of fruit and nut crops: A review. Remote Sensing 2, 1973–1997. https://doi.org/10.3390/rs2081973.

  • Park, M.H. (2011). Extracting image information of the unmanned-crane automation system using an integrated vision system. Journal of the Korea Institute of Information and Communication Engineering 15, 545–550. https://doi.org/10.6109/jkiice.2011.15.3.545.

  • Paulus, S., Behmann, J., Mahlein, A.K., et al. (2014). Low-cost 3D systems: suitable tools for plant phenotyping. Sensors 14, 3001–3018. https://doi.org/10.3390/s140203001.

  • Peeters, A., Ben-Gal, A., Gebbers, R., Hetzroni, A., Zude, M., et al. (2015). Getis-Ord’s hot- and cold-spot statistics as a basis for multivariate spatial clustering of tree-based data. Computers and Electronics in Agriculture 111, 140–150. https://doi.org/10.1016/j.compag.2014.12.011.

  • Pelletier, G., and Upadhyaya, S.K. (1999). Development of a tomato load/yield monitor. Computers and Electronics in Agriculture 23, 103–118. https://doi.org/10.1016/S0168-1699(99)00025-3.

  • Peng, Y.K., and Lu, R.F. (2006). New approaches of analyzing multispectral scattering profiles for predicting apple fruit firmness and soluble solids content. In Meeting Presentation of ASABE, Oregon, U.S.A., 9-12 July 2006, Paper Number 066234.

  • Perry, E.M., Dezzani, R.J., Seavert, C.F., and Pierce, F.J. (2010). Spatial variation in tree characteristics and yield in a pear orchard. Precision Agriculture 11, 42–60. https://doi.org/10.1007/s11119-009-9113-5.

  • Primicerio, J., Di Gennaro, S.F., Fiorillo, E., Genesio, L., Lugato, E., Matese, A., and Vaccari, F.P. (2012). A flexible unmanned aerial vehicle for precision agriculture. Precision Agriculture 13, 517–523. https://doi.org/10.1007/s11119-012-9257-6.

  • Pozdnyakova, L., Giménez, D., and Oudemans, P.V. (2005). Spatial analysis of cranberry yield at three scales. Agronomy Journal 97, 49–57. https://doi.org/10.2134/agronj2005.0049.

  • Praeger, U., Surdilovic, J., Truppel, I., Herold, B., and Geyer, M. (2013). Comparison of electronic fruits for impact detection on a laboratory scale. Sensors 13, 7140–7155. https://doi.org/10.3390/s130607140.

  • Pu, Y.Y., Feng, Y.Z., and Sun, D.W. (2015). Recent progress of hyperspectral imaging on quality and safety inspection of fruits and vegetables: a review. Comprehensive Reviews in Food Science and Food Safety 14, 176–188. https://doi.org/10.1111/1541-4337.12123.

  • Qarallah, B., Shoji, K., and Kawamura, T. (2008). Development of a yield sensor for measuring individual weights of onion bulbs. Biosystems Engineering 100, 511–515. https://doi.org/10.1016/j.biosystemseng.2008.05.009.

  • Qiao, J., Sasao, A., Shibusawa, S., Kondo, N., and Morimoto, E. (2005). Mapping yield and quality using the mobile fruit grading robot. Biosystems Engineering 90, 135–142. https://doi.org/10.1016/j.biosystemseng.2004.10.002.

  • Qing, Z.S., Ji, B.P., and Zude, M. (2008). Non-destructive analyses of apple quality parameters by means of laser-induced light backscattering imaging. Postharvest Biology and Technology 48, 215–222. https://doi.org/10.1016/j.postharvbio.2007.10.004.

  • Rains, G.C., Thomas, D.L., and Perry, C.D. (2002). Pecan mechanical harvesting parameters for yield mapping. Transactions of the ASAE 45, 281–285. https://doi.org/10.13031/2013.8518.

  • Rawlins, S.L., Campbell, G.S., Campbell, R.H., and Hess, J.R. (1995). Yield mapping of potato. In Proceedings of Site-Specific Management for Agricultural Systems, P.C. Robert, R.H. Rust, and W.E. Larson, eds. (Madison, WI, USA: ASA, CSA, SSSA). pp. 59–68.

  • Richardson, A.D., Duigan, S.P., and Berlyn, G.P. (2002). An evaluation of non-invasive methods to estimate foliar chlorophyll content. New Phytologist 153, 185–194. https://doi.org/10.1046/j.0028-646X.2001.00289.x.

  • Rosell Polo, J.R., Sanz, R., Llorens, J., Arnó, J., Escolŕ, A., Ribes-Dasi, M., Masip, J., Camp, F., Grŕcia, F., Solanelles, F., Pallejŕ, T., Val, L., Planas, S., Gil, E., and Palacín, J. (2009). A tractor-mounted scanning LIDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with conventional destructive measurements. Biosystems Engineering 102, 128–134. https://doi.org/10.1016/j.biosystemseng.2008.10.009.

  • Ruiz-Altisent, M., Ruiz-Garcia, L., Moreda, G.P., Renfu, L., Hernandez-Sanchez, N., Correa, E.C., Diezma, B., Nicolaď, B., and García-Ramos, J. (2010). Sensors for product characterization and quality of specialty crops – A review. Computers and Electronics in Agriculture 74, 176–194. https://doi.org/10.1016/j.compag.2010.07.002.

  • Saldana, N., Cabrera, J.M., Serwatowski, R.J., and Gracia, C. (2006). Yield mapping system for vegetables picked up with a tractor-pulled platform. Spanish Journal of Agricultural Research 4(2), 130–139. https://doi.org/10.5424/sjar/2006042-185.

  • Sandri, D., Pereira, J.A., and Vargas, R.B. (2014). Production costs and profitability of watermelon under different water depths and irrigation systems. Irriga 19, 414–429. https://doi.org/10.15809/irriga.2014v19n3p414.

  • Scharf, P.C. (2015). Determining the optimal nitrogen rate: N credits, soil tests, and crop-based diagnosis. Crops and Soils 48, 34–42.

  • Schueller, J.K., Whitney, J.D., Wheaton, T.A., Miller, W.M., and Turner, A.E. (1999). Low-cost automatic yield mapping in hand-harvested citrus. Computers and Electronics in Agriculture 23, 145–153. https://doi.org/10.1016/S0168-1699(99)00028-9.

  • Seifert, B., Zude, M., Spinelli, L., and Torricelli, A. (2015). Optical properties of developing pip and stone fruit reveal underlying structural changes. Physiologia Plantarum 153, 327–336. https://doi.org/10.1111/ppl.12232.

  • Shahbazi, M., Théau, J., and Ménard, P. (2014). Recent applications of unmanned aerial imagery in natural resource management. GIScience & Remote Sensing 51(4), 339–365. https://doi.org/10.1080/15481603.2014.926650.

  • Srisopaporn, S., Jourdain, D., Perret, S.R., and Shivakoti, G. (2015). Adoption and continued participation in a public Good Agricultural Practices program: The case of rice farmers in the Central Plains of Thailand. Technological Forecasting and Social Change 96, 242–253. https://doi.org/10.1016/j.techfore.2015.03.016.

  • Stafford, J.V. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research 76, 267–275. https://doi.org/10.1006/jaer.2000.0577.

  • Stagakis, S., Gonzalez-Dugo, V., Cid, P., et al. (2012). Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices. Journal of Photogrammetry and Remote Sensing 71, 47–61. https://doi.org/10.1016/j.isprsjprs.2012.05.00.

  • Suárez, L., Zarco-Tejada, P.J., Sepulcre-Cantó, G., Pérez-Priego, O., Miller, J.R., Jiménez-Muńoz, J.C., and Sobrino, J. (2008). Assessing canopy PRI for water stress detection with diurnal airborne imagery. Remote Sensing of Environment 112, 560–575. https://doi.org/10.1016/j.rse.2007.05.009.

  • Subedi, P.P., Walsh, K.B., and Owens, G. (2007). Prediction of mango eating quality at harvest using short-wave near infrared spectrometry. Postharvest Biology and Technology 43, 326–334. https://doi.org/10.1016/j.postharvbio.2006.09.012.

  • Taroni, P., Pifferi, A., Torricelli, A., et al. (2003). Review: In vivo absorption and scattering spectroscopy of biological tissues. Photochemical & Photobiological Sciences 2, 124–129. https://doi.org/10.1039/b209651j.

  • Taylor, J.A., Praat, J.P., and Bollen, A.F. (2007). Spatial variability of kiwifruit quality in orchards and its implications for sampling and mapping. HortScience 42, 246–250.

  • Togami, T., Ito, R., Hashimoto, A., et al. (2011). Agro-environmental monitoring using a wireless sensor network to support production of high quality mandarin oranges. Agricultural Information Research 20, 110–121. https://doi.org/10.3173/air.20.110.

  • Türker, U., Talebpour, B., and Yegül, U. (2011). Determination of the relationship between apparent soil electrical conductivity with pomological properties and yield in different apple varieties. Žemdirbystė = Agriculture 98, 307–314.

  • Ünlü, M., Kanber, R., Koc, D., Özekici, B., Kekec, U., Yesiloglu, T., Ortas, I., Ünlü, F., Kapur, B., Tekin, S., Käthner, J., Gebbers, R., Zude, M., Ben-Gal, A., and Peeters, A. (2014). Irrigation scheduling of grapefruit trees in a Mediterranean environment throughout evaluation of plant water status and evapotranspiration. Turkish Journal of Agriculture and Forestry 38, 908–915. https://doi.org/10.3906/tar-1403-58.

  • Usha, K., and Singh, B. (2013). Potential applications of remote sensing in horticulture – A review. Scientia Horticulturae 153, 71–83. https://doi.org/10.1016/j.scienta.2013.01.008.

  • Vatsanidou, A., Fountas, S., Nanos, G., and Gemtos, T. (2014). Variable Rate Application of nitrogen fertilizer in a commercial pear orchard. Fork to Farm: International Journal of Innovative Research and Practice 1(1).

  • Verstraeten, W.W., Veroustraete, F., and Feyen, J. (2008). Assessment of evapotranspiration and soil moisture content across different scales of observation. Sensors 8, 70–117. https://doi.org/10.3390/s8010070.

  • Vijayarekha, K. (2012). Machine vision application for food quality: a review. Research Journal of Applied Sciences, Engineering and Technology 4, 5453–5458.

  • Walklate, P.J., Cross, J.V., Richardson, G.M., et al. (2002). Comparison of different spray volume deposition models using LIDAR measurements of apple orchards. Biosystems Engineering 82, 253–267. https://doi.org/10.1006/bioe.2002.0082.

  • Wei, J., and Salyani, M. (2005). Development of a laser scanner for measuring tree canopy characteristics: Foliage density measurement. Trans. ASAE 48, 1595–1601. https://doi.org/10.13031/2013.19174.

  • Weng, J.H., Liao, T.S., Hwang, M.Y., Chung, C.C., Lin, C.P., and Chu, C.H. (2006). Seasonal variation in photosystem II efficiency and photochemical reflectance index of evergreen trees and perennial grasses growing at low and high elevations in subtropical Taiwan. Tree Physiology 26, 1097–1104. https://doi.org/10.1093/treephys/26.8.1097.

  • Whitney, J.D., Miller, W.M., Wheaton, T.A., Salyoni, M., and Schueller, J.K. (1999). Precision farming applications in Florida citrus. Applied Engineering in Agriculture 15, 399–403. https://doi.org/10.13031/2013.5795.

  • Windt, C., and Blumler, P. (2015). A portable NMR sensor to measure dynamic changes in the amount of water in living stems or fruit and its potential to measure sap flow. Tree Physiology 35, 366–375. https://doi.org/10.1093/treephys/tpu105.

  • Wulf, J.S., Rühmann, S., Regos, I., Puhl, I., Treutter, D., and Zude, M. (2008). Nondestructive application of laser-induced fluorescence spectroscopy for quantitative analyses of phenolic compounds in straw-berry fruits (Fragaria × ananassa). J. Agric. Food Chem. 56, 2875–2882. https://doi.org/10.1021/jf072495i.

  • Wünsche, J.N., Palmer, J.W., and Greer, D.H. (2000). Effects of crop load on fruiting and gas-exchange characteristics of ‘Braeburn’/M.26 apple trees at full canopy. J. Am. Soc. Hortic. Sci. 125, 93–99.

  • Xujun, Y., Sakai, K., Manago, M., Asada, S., and Sasao, A. (2007). Prediction of citrus yield from airborne hyperspectral imagery. Precision Agriculture 8, 111–125. https://doi.org/10.1007/s11119-007-9032-2.

  • Zaman, Q.U., and Salyani, M. (2004). Effects of foliage density and ground speed on ultrasonic measurement of citrus tree volume. Applied Engineering in Agriculture 20, 173–178. https://doi.org/10.13031/2013.15887.

  • Zaman, Q.U., Schumann, A.W., and Miller, W.M. (2005). Variable rate nitrogen application in Florida citrus based on ultrasonically-sensed tree size. Applied Engineering in Agriculture 21, 331–335. https://doi.org/10.13031/2013.18448.

  • Zaman, Q.U., Schumann, A.W., and Hostler, H.K. (2006). Estimation of citrus fruit yield using ultrasonically-sensed tree size. Applied Engineering in Agriculture 22, 39–44. https://doi.org/10.13031/2013.20186.

  • Zaman, Q., and Schuman, A.W. (2006). Nutrient management zones for citrus based on variation in soil properties and tree performance. Precision Agriculture 7, 45–63. https://doi.org/10.1007/s11119-005-6789-z.

  • Zaman, Q.U., Schumann, A.W., Percival, D.C., and Gordon, R.J. (2008). Estimation of wild blueberry fruit yield using digital color photography. Transactions of the ASABE 51, 1539–1544. https://doi.org/10.13031/2013.25302.

  • Zarco-Tejada, P.J., Gonzalez-Dugo, V., and Berni, J.A.J. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment 117, 322–337. https://doi.org/10.1016/j.rse.2011.10.007.

  • Zarco-Tejada, P.J., Diaz-Varela, R., Angileri, V., et al. (2014). Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy 55, 89–99. https://doi.org/10.1016/j.eja.2014.01.004.

  • Zdunek, A., Adamiak, A., Pieczywek, P.M., et al. (2014). The biospeckle method for the investigation of agricultural crops: A review. Optics and Lasers in Engineering 52, 276–285. https://doi.org/10.1016/j.optlaseng.2013.06.017.

  • Zhang, C., and Kovacs, J.M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture 13, 693–712. https://doi.org/10.1007/s11119-012-9274-5.

  • Zhou, R., Damerow, L., Sun, Y., and Blanke, M.M. (2012). Using colour features of cv. ‘Gala’ apple fruits in an orchard in image processing to predict yield. Precision Agriculture 13, 568–580. https://doi.org/10.1007/s11119-012-9269-2.

  • Ziosi, V., Noferini, M., Fiori, G., Tadiello, A., Trainotti, L., Casadoro, G., and Costa, G. (2008). A new index based on vis spectroscopy to characterize the progression of ripening in peach fruit. Postharvest Biology and Technology 49, 319–329. https://doi.org/10.1016/j.postharvbio.2008.01.017.

  • Zude, M. (2003). Comparison of indices and multivariate models to non-destructively predict the fruit chlorophyll by means of visible spectrometry in apples. Analytica Chimica Acta 481, 119–126. https://doi.org/10.1016/S0003-2670(03)00070-9.

  • Zude, M., Pflanz, M., Kaprielian, C., and Aivazian, B.L. (2008). NIRS as a tool for precision horticulture in the citrus industry. Journal Biosystems Engineering 99, 455–459. https://doi.org/10.1016/j.biosystemseng.2007.10.016.

  • Zude, M., Pflanz, M., Dosche, K., Spinelli, L., and Torricelli, A. (2011). Non-destructive analysis of anthocyanins in cherries by means of Lambert-Beer and multivariate regression based on spectroscopy and scatter correction using time-resolved analysis. J. Food Engineering 103, 68–75. https://doi.org/10.1016/j.jfoodeng.2010.09.021.

Received: 23 November 2015 | Revised: 16 February 2016 | Accepted: 6 March 2016 | Published: 25 April 2016 | Available online: 25 April 2016

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