Precision agriculture '21 / / John V. Stafford.

• VerfasserIn: Stafford, John V.,
• Place / Publishing House: Wageningen, The Netherlands : : Wageningen Academic Publishers,, [2021]; ©2021
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (986 pages)

Precision agriculture is a reality in agriculture and is playing a key role as the industry comes to terms with the environment, market forces, quality requirements, traceability, vehicle guidance and crop management. Research continues to be necessary, and needs to be reported and disseminated to a wide audience.These proceedings contain reviewed papers presented at the 13th European Conference on Precision Agriculture, held in Budapest, Hungary. The papers reflect the wide range of disciplines that impinge on precision agriculture - technology, crop science, soil science, agronomy, information technology, decision support, remote sensing and others.The broad range of research topics reported will be a valuable resource for researchers, advisors, teachers and professionals in agriculture long after the conference has finished.

Table of Contents

Intro -- Preface -- John V Stafford -- Foreword -- Dr Gabor Milics, PhD -- Table of contents -- Section 1. Precision agriculture -- 1. Consideration of resilience for digital farming systems -- S. Bökle*, L. Könn, D. Reiser, D.S. Paraforos and H.W. Griepentrog -- 2. Leaf area index estimation in maize breeding trials from RGB imagery and machine learning algorithms -- P. Castro-Valdecantos, O.E. Apolo-Apolo, M. Pérez-Ruiz and G. Egea* -- 3. A simple web-based tool for optimizing nitrogen variable rate application in durum wheat -- R. Ferrise1*, G. Trombi1, G. Padovan1, S. Costafreda-Aumedes1, E. Di Giuseppe2, M. Pasqui2, J. Moretto3 and F. Morari3 -- 4. Evaluation of different crop model-based approaches for variable rate nitrogen fertilization in winter wheat -- S. Gobbo1*, F. Morari1, R. Ferrise2, M. De Antoni Migliorati3, L. Furlan4 and L. Sartori5 -- 5. A farm scale evaluation of variable rate application (VRA) for improving liquid digestate agronomic performances -- F. Grillo1*, I. Piccoli1, I. Furlanetto2, F. Ragazzi3, S. Obber3 and F. Morari1 -- 6. Real-time fuzzy system for variable rate nitrogen application based on multiple parameters -- A. Heiß*, D.S. Paraforos, G.M. Sharipov, M. Karampoiki and H.W. Griepentrog -- 7. Methodology for comparison between uniform and variable rate application in a drip-irrigated peach orchard -- L. Katz1,2,3,4*, A. Naor3, M.I. Litaor3,5, A. Ben-Gal4, V. Alchanatis1, M. Peres6, A. Peeters7 and Y. Cohen1 -- 8. Multi species weed detection with Retinanet one-step network in a maize field -- J.M. López Correa1,2, M. Todeschini1, D.S. Pérez2, J. Karouta1, F. Bromberg1, A. Ribeiro and D. Andújar1 -- 9. Influence on rooting intensity and nutrient utilization of maize at different plant spacings -- Y. Reckleben1*, B. Brandenburg1 and H.W. Griepentrog2.

10. Modelling of in-field dynamic response of a centrifugal spreader to variable rate application -- G.M. Sharipov*, A. Heiß, M. Karampoiki, H.W. Griepentrog and D.S. Paraforos -- 11. Yield measurement of wilted forage and silage maize with forage harvesters -- F. Worek* and S. Thurner -- Section 2. Precision horticulture -- 12. The uncharted territory of drone-based cross-season monitoring for precision horticulture -- S. Delalieux1*, J. Vandermaesen3, Y. Vanbrabant1,2, M. Wuyts2, W. Dierckx1 and L. Tits1 -- 13. Efficiency evaluation of automated insecticide spot spraying in lettuce and bok choy fields -- P. Haberey1*, D. Hodel1, L. Collet2, C. Bucher3, T. Anken4, R. Total1 and M. Keller1 -- 14. Estimation of geometric and structural parameters in a super-intensive almond (Prunus dulcis) orchard from multispectral vegetation indices derived from UAV-based imagery -- J. Llorens, A. Escolà, E. Casañas, J.R. Rosell-Polo, J. Arnó and J.A. Martínez-Casasnovas* -- 15. Strawberry flower and fruit detection using deep learning for developing yield prediction models -- P. Puranik1*, W.S. Lee2*, N. Peres3, F. Wu3, A. Abd-Elrahman3 and S. Agehara3 -- Section 3. Precision viticulture -- 16. Fine-tuning and testing of a deep learning algorithm for pruning regions detection in spur-pruned grapevines -- P. Guadagna1, T. Frioni1, F. Chen2, A. Incerti Delmonte2, T. Teng1,2, M. Fernandes2, A. Scaldaferri2, C. Semini2, S. Poni1 and M. Gatti1* -- 17. Are all NDVI maps created equal - comparing vineyard NDVI data from proximal and remote sensing -- A. Kasimati1*, A. Kalogrias1, V. Psiroukis1, K. Grivakis1, J.A. Taylor2 and S. Fountas1 -- 18. Is it relevant to account for grapevine phenology in time series of satellite images? -- C. Laurent1,2,3*, F. Rançon3, E. López Fornieles3, T. Scholasch1, A. Metay2, J. Taylor3 and B. Tisseyre3.

19. Assessing actual number of grapevine berries using linear methods and machine learning -- F. Palacios1,2, P. Melo-Pinto3,4, M.P. Diago1,2, R. Iñiguez1,2 and J. Tardaguila1,2* -- 20. Characterising within-field variability of vine water status with simple visual observations of shoot growth -- L. Pichon*, O. Bopp and B. Tisseyre -- 21. Relationship between foliar composition and the vigour of vineyards in D.O. Rias Baixas, Spain -- M. Rodríguez-Fernández1, M. Fandiño1, X.P. González2* and J.J. Cancela1* -- 22. Grape yield spatial variability assessment using YOLOv4 object detection algorithm -- M. Sozzi1, S. Cantalamessa2, A. Cogato1, A. Kayad1 and F. Marinello1 -- 23. Validation of a commercial optoelectronics device for grape quality analysis -- M. Sozzi1, A. Cogato1, D. Boscaro2, A. Kayad1, D. Tomasi2 and F. Marinello1 -- Section 4. Precision crop protection -- 24. Satellite-based spectral indices for monitoring Helicoverpa armigera damage in maize -- F.E. Sári-Barnácz*, M. Szalai, M. Kun, D. Iványi, M. Chaddadi, F.M. Barnácz and J. Kiss -- 25. Early detection of soil-borne diseases in field crops via remote sensing -- A. Chen*, M. Jacob, G. Shoshani, M. Dafny-Yelin, O. Degani and O. Rabinovitz -- 26. High throughput field phenotyping (HTFP) of wheat and weed cover in field experiments using RGB images: assessment of crop-weed competition with a simple ecophysiological model -- C. Gée1*, V. Mignon1, L. Dujourdy2 and E. Denimal2 -- 27. Four-band weed detection using machine learning algorithms based on hyperspectral images -- A. Lazar1,2* and A. Bechar1,2 -- 28. Economic assessment of site-specific pesticide applications in Northern Germany -- S. Rajmis1,2*, I. Karpinski1, J.-P. Pohl3, M. Herrmann4 and H. Kehlenbeck1 -- 29. Determination of wheat rust severity using hyperspectral imagery and 3D plant reconstruction modelling.

J.N. Rodríguez-Vázquez1, O.E. Apolo-Apolo1, P. Castro-Valdecantos1, M. Pérez-Ruiz1, J. Marínez-Guanter1, F. Martínez-Moreno2, I. Solís2 and G. Egea1* -- 30. Comparison of sensor-based harrowing technology (SenHa) with a conventional manual harrowing-system -- M. Spaeth* and R. Gerhards -- Section 5. Proximal and remote sensing of soil and crop -- 31. Evaluation of a portable sensor suite for real time CWSI monitoring in wheat -- O.E. Apolo-Apolo*, M. Pérez-Ruiz, P. Castro-Valdecantos and G. Egea -- 32. Achieving bread-making flour quality in winter wheat using chlorophyll meter readings -- M. Aranguren*, A. Castellón, A. Uribeetxebarria and A. Aizpurua -- 33. Early detection of grapevine downy mildew using thermal imaging -- B. Cohen1,2, Y. Edan2, A. Levi1 and V. Alchanatis1* -- 34. Semantic interpretation of multispectral maps for precision agriculture: a machine learning approach -- L. Comba1,2*, A. Biglia1, D. Ricauda Aimonino1, P. Barge1, C. Tortia1 and P. Gay1 -- 35. In-season prediction of maize lodging characteristics using an active crop sensor -- R. Dong1, Y. Miao2*, X. Wang3, and P. Berry4 -- 36. Camera-based estimation of sugar beet stem points and weed cover using convolutional neural networks -- M. Dyrmann*, S.K. Skovsen and P.H. Christiansen -- 37. Mapping of hailstorm and strong wind damaged crop areas using LAI estimated from multispectral imagery -- J. Furlanetto1*, N. Dal Ferro1, F. Briffaut1, L. Carotta1, R. Polese1, A. Dramis2, C. Miele3, A. Persichetti3, L. Nicoli4 and F. Morari1 -- 38. Integrating vegetation vigour in a thermal sensitivity index for mapping the variability of orchard water stress -- V. Gonzalez-Dugo1*, P.J. Zarco-Tejada1,2, D.S. Intrigliolo3 and J.M. Ramírez-Cuesta3 -- 39. Semi-supervised semantic segmentation for grape bunch identification in natural images -- J. Heras1, R. Marani2* and A. Milella2.

40. Detection of irrigation malfunctions based on thermal imaging -- N. Kalo1,2*, Y. Edan1 and V. Alchanatis2 -- 41. Comparing maize leaf area index retrieval from aerial hyperspectral images through radiative transfer model inversion and machine learning techniques -- A. Kayad1*, F. Rodrigues2, M. Sozzi1, S. Macias2 and F. Marinello1 -- 42. Validation of precision agriculture soil mapping services under practical conditions -- C. Kempenaar1,2*, F. Tigchelhoff2, J.A. Booij1, S. Nysten2 and C.G. Kocks2 -- 43. Distortion and mosaicking of close-up multi-spectral images -- A. Krus1, C. Valero1*, J. Ramirez1, C. Cruz2, A. Barrientos2 and J. del Cerro2 -- 44. Estimating water status of wheat canopy using spectral reflectance in the 400-900 nm range -- K. Kusnierek* and A. Korsaeth -- 45. The use of remote sensing for variable rate irrigation in cotton -- L.N. Lacerda1*, J. Snider1, Y. Cohen2, V. Liakos1 and G. Vellidis1 -- 46. A sensory system for adaptive distribution of plant protection agents -- P. Lepej1, M. Lakota2 and J. Rakun2* -- 47. Multi-beam LiDAR-derived data analysis for optimal canopy 3D monitoring in super-intensive almond (Prunus dulcis) orchards -- J. Llorens*, A. Alsina, J. Arnó, J.A. Martínez-Casasnovas and A. Escolà -- 48. VIs-image segmentation method for the estimation of agronomic traits in durum- and winter-wheat cultivars -- S. Marino*, U. Ahmad and A. Alvino -- 49. Testing the potential of a new low-cost multispectral sensor for decision support in agriculture -- S. Moinard1*, G. Brunel1, A. Ducanchez1, T. Crestey1, J. Rousseau2 and B. Tisseyre1 -- 50. Robust vegetation segmentation for image-based field survey -- Y. Moriuchi1*, K. Nakamura1, H. Mihara1, T. Sasaki1, A. Ito1, E. Itakura2, H. Mori2 and J. Murayama1 -- 51. Real-time spectral information to measure crop water stress for variable rate irrigation scheduling.

A. Nagy, A. Szabó*, B. Gálya Farkasné and J. Tamás.