3D MOSAIC ADVANCED MONITORING OF TREE CROPS FOR OPTIMIZED MANAGEMENT ICT-AGRI PROJECT 3D-MOSAIC. Final project overview Contact:

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1 ADVANCED MONITORING OF TREE CROPS FOR OPTIMIZED MANAGEMENT ICT-AGRI PROJECT 3D-MOSAIC Final project overview Contact:

2 Partners Kick off meeting: June 14-15, 2011 Potsdam/Berlin, ATB

3 Conditions Transnational Project ICT-AGRI ERANET, FP7 Coordinator: Leibniz Institute for Agricultural Engineering Potsdam-Bornim (ATB) Funding: Ministries and Agencies from participating partner countries (1.1 Mio Euro) Duration: 05/ /2013 Partners: 11 partners from 7 countries (EU member and AC)

4 Concept 3D-Mosaic targets the monitoring of spatial variability in tree crops and delineation of management zones considering soil and plant data by means of ICT and robotic solutions for approaching the automation of horticultural processes 4

5 Data management & Application Instrument implementation & Testing intro Project structure

6 WP 1 Autonomous Platform Partners: University of Hohenheim (DE), Universität Kassel (DE), Zürcher Hochschule für angewandte Wissenschaften (CH), Aristotle University of Thessaloniki (GR) Contact: Claes L. Dühring Jæger University of Hohenheim hw.griepentrog@uni-hohenheim.de Competencies Precision Farming Robotics and Automation Navigation Data analysis and Simulation Real Time Applications

7 WP 1 Autonomous Platform Armadillo Scout: Electrical powered robot for agricultural applications like scouting and weeding. It has a modular design which makes it easy to adjust height and width. Battery Type: LiFeYPO4 Battery Capacity: 160 Ah Primary Voltage: 48 VDC Secondary Voltage: 12/24 VDC Weight: 430 kg Operation time: 11 h Power: 7 kw Average Power Consumption: 450 W Computer: Frobobox Armadillo Scout Controlling Software: MobotWare / FroboMind

8 WP 1 Autonomous Platform In the 3D-Mosaic, an algorithm was developed for mapping trees in an orchard. The algorithm is currently being implemented in a SLAM solution for our platforms Armadillo with Sensor Tower equipped with s

9 WP 2 Vision Systems Partners: Agroscope Reckenholz-Tänikon ART Research station (CH), Universität Kassel (DE), Zürcher Hochschule für angewandte Wissenschaften (CH) Contact: Thomas Anken, ART thomas.anken@art.admin.ch Competencies of work groups 2D leaf and fruit analyses Plant recognition system (e.g. SmartWeeder) Real Time 3D data acquisition in high resolution Real Time 3D plant recognition

10 WP 2 Vision Systems In the 3D-Mosaic, systems were integrated on the platform for data acquisition and software tools were developed for canopy analyses. CCD camera and LiDAR were mounted with vertical top-down viewing direction Heights: 2.7 m (CCD) and 3.5 m (LiDAR) Vision NIR GigE cameras were applied, providing 1600x1200 pixels, 8bit intensity images

11 WP 3 Fruit Information Partners: Politecnico di Milano (IT), Leibniz Institute for Agricultural Engineering Potsdam-Bornim, ATB (DE), Sintéleia S.r.l. (IT), Aristotle University of Thessaloniki (GR) Competencies of work groups (Commercial) multispectral s Fluorescence lifetime imaging and spectroscopy of diffusive media Physical models for photon transport in biological material Contact: Alessandro Torricelli, Politecnico di Milano alessandro.torricelli@polimi.it

12 WP 3 Facilities time-domain workstation for diffuse spectroscopy of turbid media in the nm spectral range based on super-continuum laser and time-correlated single photon counting In the 3D-Mosaic, software for fitting of photon transport in biological samples with one or more layers. Novel sensors for measuring fruit water content were approached.

13 WP 3 Background

14 WP 4 Field tests Partners: Leibniz Institute for Agricultural Engineering Potsdam- Bornim, ATB (DE), University of Cukurova (TR), all partners Contact: Manuela Zude, ATB mzude@atb-potsdam.de Competencies Fruit physiology Mechanical and optical properties of fruits in-situ fruit analysis by means of sensors

15 Experimental orchards Citrus paradisii Adana, Turkey Prunus domestica Marquardt, Germany

16 Field trial 1 Citrus paradisii Adana, Turkey

17 Evapotranspiration_Bowen, mm/day intro Citrus paradisii Environment Total ET=345.3 mm 0.0 Date Variation of evapotranspiration values measured by Bowen ratio-energy balance method (BREB) at the period from DOY:249 (2011) to DOY:97 (2012)

18 Field trial 2 Prunus domestica Marquardt, Germany 18

19 intro Field trial 2 automatical and not so automatical data acquisition

20 Soil pattern stable over 3 years Small scale variability due to sand lenses

21 Six years old trees Five years old trees intro Plum Statistics Plant parameter ECa topsoil Elevation F p < F F p < F Flower set Fruit set 1.15 n. s n. s. Fruit drop 0.91 n. s # Fruits 0.72 n. s n. s. Fruit height 7.02 < < Yield 0.33 n. s n. s. Fruit NDVI DA-index Flower set Fruit set Fruit drop n.s. # Fruits n. s. Fruit height* < < Yield n. s. Fruit NDVI DA-index

22 Spatial pattern of soil and yield Number Spatial pattern of fruits per tree and soil electrical resistivity, R = 0.46 (2011)

23 Vision system: LiDAR and camera Left side drive Right side drive Top view 137

24 intro. Vision system: LiDAR and camera (a) (b) (c) unprocessed image fragmentation using watershed procedure segmented binary image (a) (b) (c) Figure 1 Processing steps for the determination of the per cent leaf cover of two exemplary plum tree images taken under different lightning conditions: (a) unprocessed image; (b) fragmentation into touching area objects with the help of a watershed procedure; (c) segmented binary image

25 Platform: Software Configuration

26 Tree Mapping using LiDAR & GNSS Using border data of trees detected by histogram - and direct least square ellipse fitting on resulting point cloud, the position of each tree can be calculated. Calculated tree positions, marked with green +, and the path driven by the tractor, marked with blue o. Axis are in meters, (0,0) is equivalent to UTM zone 36 S; N E

27 Fruit sensors / information GOAL Non-destructive, continuous monitoring of fruit parameters in the orchard aiming at optimising cultivation processes METHOD Optical multispectral sensors in the canopy PROBLEM Varying scattering coefficient in the growing fruit SOLUTION Physical calibration correction of VIS/NIR optical sensors by means of advanced optical techniques and models

28 Novel sensors laboratory data

29 Novel sensors in the orchard 1. Fruit sensor DA-Meter for manual use in the orchard 2. Fruit sensor spider equipped with Xbee Pro 2.4GHz RF modules for data transfer

30 Novel sensors in the orchard

31 Data transfer of spider : Radio path loss Wave propagation modes in the presence of trees: around tree canopies - diffracted wave (black) directly through tree foliage - scattered wave (blue) ground reflected wave (red)

32 The prediction of radio path loss and ultimately the maximum range among the nodes of wireless sensor networks is critical for the successful deployment of this technology in orchards. Our goals were to: Radio path loss 1. Evaluate the influence of leaves on electromagnetic attenuation at various transmission heights. 2. Compare the prediction power of empirical radio propagation models 3. Compare empirical and computational models. 32

33 Radio path loss Empirical models were used to predict propagation losses through vegetation. The MED, ITU-R, and FITU-R could not account for the presence of leaves. There was significantly higher attenuation at 1.5 m when leaves were present. More advanced computational modelling is needed. 33

34 InertiaCube3 3-DOF orientation tracking sensor Geometric tree models Euler angles (roll, pitch, yaw) of main tree branches Length and circumference of branches Digitization of cherry tree geometry with computer software using the collected data as input data for all executed experiment scenarios. COMSOL MULTIPHYSICS: EM solution for simplified case. 34

35 WP 5 Data management Management Zones Expert Analysis 3D-Mosaic Graphic Geo-positioned Sensor Data Tree Unit Plant Life BBDD OLAP Analysis Farmer DSS 3D-Chart Tile organization

36 WP 5 Layer structure Trees + cover Tile organization Farmer Management Zones Map Raw sensor data -Tree - Leaf Area - Canopy - Fruit count - Fruit quality -Soil measurements - ECa,.. -Weather station - Temperature - Humidity,.. -Irrigation - L/m²,.. Expert Spatial Analysis Irrigation Map Soil Map

37 WP 5 DSS Tool OLAP Analysis Dashboard 3D Graphic User Interface In 3D-Mosaic, the commercial application were extended by algorithms for hotspot analysis to enable DSS Google plug-in Decision Support System Features: Correlation & dependences between measures Dispersion Analysis Measurement Data GUI: Multidata web upload. Object (tree, MZ ) & Dimensions (ECa, yield ) dynamic representation. web interface.

38 WP 6 Management Zones Goal: A methodology for defining management zones in orchards based on spatial statistical analysis Method: Spatial variability can be used as the basis for delineating agricultural fields into sub-units know as management zones that exhibit homogeneity in environmental or plant parameters that are considered yield-defining Problem: Existing clustering algorithms typically rely on the data structure to recognize natural groupings (clusters) and to partition n data observations into k clusters based on methods of similarity. However, while the location of data in the character space is taken into consideration, its geographical spatial context (spatial neighborhood) is not accounted for. Solution: The proposed method is based on quantifying the distribution in space of attributes associated with trees or with environmental parameters and evaluating whether or not, and to what extent, a recognized pattern is significantly clustered or dispersed

39 WP 6 SDSS R 2 = 0.57 R 2 = 0.59 Trunk circumference vs. yield Trunk circumference + ECa vs. yield Management zones Based on local R 2 BUILDING A GEOGRAPHICALLY WEIGHTED REGRESSION MODEL for field trial 2; Basemap from ESRI World Map Background, Copyright ESRI (ESRI 1982)

40 WP 6 Achievements & Outlook 3D-Mosaic: The developed methodology captures (i) evaluation of global spatial variability in the orchard using the General G statistic, (ii) detection of local spatial clusters using the Gi* statistic (hot-spot analysis), and (iii) delineation of management zones based on the identified clusters Spatial correlation analysis such as a geographically weighted regression (GWR) will be applied using the data collected in field trial II to recognize the parameters that most influence and/or represent yield variability

41 Conclusion Consistent with findings in precision agriculture of field crops, correlation was found between soil electric conductivity and plant parameters. In preliminary experiments in the subtropics, interactions of soil and vegetative growth, yield, and fruit quality were indicated. The data were supplied by means of novel sensors or new approaches in data processing, providing the necessary prerequisites for numerous spatially resolved measurements. Finally some notes from (i) a FARMER perspective: farming with sensors is so much easier ; and (ii) the external project advisor: the level of enthusiasm was infectious I feel that by the bringing facilities together we, were able to provide an integrated approach that benefits the AUTOMATION of agricultural processes and supports the concept. Hope to see you soon on similar questions, Manuela Zude

42 Literature please visit webpage a)pforte F, Selbeck J, Hensel O. (2012). Comparison of image analysis and laser scanner technique for automated estimation of the leaf area on tree level. Biosystems Engineering Volume 113, Issue 4, b)vougioukas S; Anastassiu HT; Regen C; Zude M (2012). Influence of foliage on radio path losses (PLs) for wireless sensor network (WSN) planning in orchards. Biosystems Engineering 114: c)zude M; Peeters A; Selbeck J; Käthner J; Gebbers R; Ben-Gal A; Hetzroni A; Jaeger-Hansen C; Griepentrog HW; Pforte F; Rozzi P; Torricelli A; Spinelli L; Ünlü M; Kanber R (2012). Methoden für die präzise obstbauliche Produktion / Advances in precise fruit production. Landtechnik / Agricultural Engineering 67 (5): d)lorente D; Zude M; Regen C; Palouc L; Gómez-Sanchis J; Blasco J (2013). Early decay detection in citrus fruit using laserlight backscattering imaging. Postharvest Biology and Technology, in press e)mollazade K; Omid M; Akhlaghian T; Rezaei Y; Mohtasebi KS; Zude M (2013). Analysis of texture-based features for predicting mechanical properties of horticultural products by laser light backscattering imaging. Computers and Electronics in Agriculture, in press f)torricelli A;Spinelli L; Kaethner J; Selbeck J; Franceschini A; Rozzi P; Zude M (2012). Non-destructive optical assessment of photon path lengths in fruit during ripening: implications on design of continuous-wave sensors. CIGR-AgEng International Conference of Agricultural Engineering, Proceedings ISBN-10: g)hetzroni, A., Peeters, A., Ben-Gal, A. (2012). Towards precision management of orchards: using automated monitoring to build a -based spatial decision support system. International Conference of Agricultural Engineering, Proceedings ISBN-10: h)thomas Anken, Andrea Battiato, Dejan Seatovic, Vincent Meiser, Jörn Selbeck, Florian Pforte. Canopy-Area Measurement of Plum Trees using Laser and Near-Infrared Imaging EFITA-WCCA-CIGR Conference Sustainable Agriculture through ICT Innovation, Turin, Italy, June 2013, p i)jana Käthner, Werner Herppich, Rolf Adamek and Manuela Zude: Influence of soil variability and topography on plant growth and yield parameters in Prunus domestica orchard, EFITA-WCCA-CIGR conference, June, 2013 Turin, Italy..

43 Acknowledgement This work was supported by the ICT-AGRI project 3D-Mosaic Advanced Monitoring of Tree Crops for Optimized Management How to Cope with Variability in Soil and Plant Properties? which is funded by the European Commission s ERA-NET scheme under the 7th Framework Program for Research. We thank the contributing funders group from seven countries.