Precision Agriculture '23.

• Author: Stafford, John V.
• Place / Publishing House: Boston : : Wageningen Academic Publishers,, 2023.; ©2023.
• Year of Publication: 2023
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (1127 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. Sensors now in use in agriculture are generating 'Big Data' leading to the use of machine learning and AI - an increasing challenge for agriculture. Research continues to be necessary, and needs to be reported and disseminated to a wide audience. These edited proceedings contain peer reviewed papers presented at the 14th European Conference on Precision Agriculture, held in Bologna, Italy. 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, data analysis 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 -- Precision agriculture '23 -- Copyright -- Preface -- Foreword -- Scientific committee -- 14th ECPA organising team 2023 -- Table of contents -- Section 1. PA applications and on-farm experimentation in arable crops, horticulture, vineyards and orchards, pasture, livestock -- 1. Grape counting in RGB videos - comparing two instance segmentation models -- 2. Changing how agronomic trials are conducted: modulated on farm response surface experiments (MORSE) -- 3. Quantifying real-time opening disk load to assess compaction and potential for planter control -- 4. Water status estimation using thermal imagery at different scales in the vineyard -- 5. Smart irrigation system for precision water management: effect on yield and fruit quality of yellow fleshed kiwifruit in nort -- 6. Comparison between 60° and 30° hollow cone nozzles for targeted UAV-spray applications in vineyards -- 7. Using a vegetation index to define homogeneous zones for variable rate irrigation in vineyard -- 8. Evaluation of the PROMET model in on-farm research at the 'Digital Experimental Field BeSt-SH' -- 9. Data requirements for forecasting tree crop yield - a macadamia case study -- 10. Precision monitoring of vine water stress using UAVs and opensource processing chains -- 11. Detection of -- by hyperspectral imaging in strawberry plants -- 12. Testing crop protection performances in a vineyard subjected to real-time volume adjustment through an adapted conventional -- 13. Limitations of grain yield monitor data to evaluate treatment differences within on-farm experimentation -- 14. Effects of canopy density-based airblast fan airflow adjustment on vines spray deposit -- 15. Farmer-led on-farm experimentation enhanced with digital agronomy -- 16. Evaluation of portable tools for fast field assessment of winter wheat grain quality.

17. Redesigning spatial on-farm precision experiments for innovative vineyard crop protection -- 18. Evaluation of the soil quality of Chilean orchards using a gamma radiation sensor -- 19. Follow the leader: a path generator and controller for precision tree scanning with a robotic manipulator -- 20. Monitoring chickpea physiological traits by Sentinel-2 imagery -- 21. Investigation of spraying applications using a UAS in viticulture -- 22. A new leafiness-LiDAR index to estimate light interception in intensive olive orchards -- 23. Long-term evaluation of the Grassmaster II probe used to estimate productivity of dryland pastures -- 24. Technological approach to evaluating the effect of livestock trampling on soil compaction -- 25. Tracking wheat senescence based on UAV multispectral imaging -- 26. Variable-rate fertiliser application to manage spatial variability in a hilly vineyard of Prosecco PDO -- 27. Synthetic data generation for validating site-specific crop yield response modelling using WOFOST and gaussian geostatistica -- 28. Implementation of variable rate of inputs in winter crops under rainfed conditions -- 29. Strawberry flower and fruit detection based on an autonomous imaging robot and deep learning -- Section 2. Precision protection, nutrition, water management -- 30. Variable rate nitrogen in potato cropping systems -- 31. Using an oat cover crop as a reflector of the spatial variation of soil nutrient availability -- 32. On-farm evaluation of variable rate irrigation for winter wheat in semi-arid western USA -- 33. Assessing the ability of ECa and drone data to capture spatial patterns in soil moisture for more precise turfgrass irrigati -- 34. How can precision agriculture prescription maps contribute to the 50% pesticide reduction goal of the farm-to-fork strategy.

35. An optical trapezoid model for actual evapotranspiration and winter wheat yield estimation -- 36. A novel approach to map-sensor-based site-specific nitrogen fertilisation in winter wheat -- 37. Second-generation ultrasonic sensor in precision spraying: testing and actuation range refinement -- 38. Target-N: Sentinel-2 based nitrogen optimisation in Swedish cereal production -- 39. Testing irrigation management based on an unoccupied aerial vehicle and an artificial neural network -- 40. Classifying potato yield in North Spain from Sentinel-2 data based on Random Forest and multitemporal auxiliary data -- 41. A low cost sensor to improve surface irrigation management -- 42. Grapevine water status in a variably irrigated vineyard with NIR hyperspectral imaging from a UAV -- 43. Potential of the dark green color index for dynamic monitoring of N requirements in wheat crop -- 44. Combining crop growth modeling, active sensing and machine learning to improve in-season nitrogen management of maize -- Section 3. Software for precision agriculture (e.g. DSS, big data applications, machine/deep learning, etc.) -- 45. Weeds detection in winter wheat field using improved-YOLOv4 with attention module from UAV imagery -- 46. Towards a digital twin for optimal field management -- 47. Generalization of deep learning models applied to semantic segmentation of in-field natural images in vineyards -- 48. Detecting and locating mushroom clusters by a Mask R-CNN model in farm environment -- 49. Recognition of weeds in cereals using AI architecture -- 50. Data augmentation techniques for grape bunch segmentation in natural images -- 51. The SCARECRO system: open-source design for precision agriculture adoption gaps -- 52. Early prediction of durum wheat yield in Italy using a machine learning modelling framework.

53. A Bayesian Network approach for grain protein content prediction of winter wheat -- 54. SiaPy - a user friendly Python software for hyperspectral image segmentation -- 55. Potato plant disease classification by using deep learning and sparse sensing -- 56. Enhancing navigation benchmarking and perception data generation for row-based crops in simulation -- 57. An online fruit counting application in apple orchards -- 58. Optimizing agricultural coverage path to minimize soil compaction -- 59. Cassava detection under real field conditions using YOLOv5 -- 60. Quantifying wheat spikes through smartphone camera and YOLOv5 under open field conditions -- 61. Weed25: a weed database for machine learning -- 62. Automatic estimation of trunk cross sectional area using deep learning -- 63. A scalable approach to nowcasting soil water at the within-field scale -- 64. Improving the generalization ability of random forest for potato chlorophyll estimation through integrating experimental and -- Section 4. Geostatistics, mapping and spatial data &amp -- image analysis -- 65. Integration of mechanistic model outputs as inputs into datadriven models for yield prediction: a case study on canola -- 66. Post-processing yield maps of winter wheat using data from satellites and combines -- 67. Apple fruit sizing through low-cost depth camera and neural network application -- 68. A novel approach for field sampling optimization incorporating a generic operational cost constraint -- 69. Mapping soil constraints as a cube/volume (true 3D) with gaussian processes and machine learning -- 70. Mapping grape yield with low-cost vehicle tracking devices -- 71. Comparative study of interpolation methods for low-density sampling -- 72. Using geostatistical tools to assess the correlation between soil spatial variability and cotton yield in an irrigated syste.

73. Yield prediction in winter wheat using machine learning -- improving implemented farm management tool -- 74. A new precision soil sampling approach in support of reforestation experiment in dry Mediterranean ecosystem -- 75. Impact of changing attributes on the management zones for integrated crop-livestock system -- 76. A new metric to evaluate spatial crop model performances -- 77. Establishment of a UAV-based phenotyping method for European pear rust in fruit orchards -- 78. Grape yield prediction based on vine canopy morphology obtained by 3D point clouds from UAV images -- 79. A generalised approach to downscale areal-averaged yield and production data: a use-case in cotton quality -- 80. How to best compare remote sensing data versus proximal sensing data? -- 81. Delimiting management zones for variable rate irrigation in an olive grove with highly-variable soil and complex topography -- 82. Introducing Bayesian priors to semi-variogram parameter estimation using fewer observations -- Section 5. Environmental monitoring, observation &amp -- measurement applied to precision agriculture -- 83. Evaluation of crop evapotranspiration from the fusion of spectral and SAR data together with various reference evapotranspir -- 84. Unleashing precision agriculture data for improved soil carbon accounting -- 85. Evaluating the spectral response of cotton and corn to different cover crops using UAV imagery -- 86. A multitemporal decision-making approach in vineyard using remote and proximal sensing -- 87. Evaluation of the competition between barley and different weed species in a controlled environment in relation to selective -- 88. Assessment of new non-invasive mobile sensing techniques for mapping soil spatial variabilities -- 89. Investigating factors influencing within-vineyard variability under different pedological contexts.

90. Parameters to increase LiDAR mounted UAV efficiency on agricultural field elevation measurements.