Advanced Pest Control of Olive Trees by Using Modern Technologies (GPS, GIS and Geodata)

Transcription

1 Advanced Pest Cntrl f Olive Trees by Using Mdern Technlgies (GPS, GIS and Gedata) EVANGELOS VOZIKIS GEOMET Ltd. 6, Vyrns str., Halandri, GREECE NIKOLAOS SIDIROPOULOS Reginal Department f the Greek Ministry f Agriculture GR Amfissa, GREECE GEORGE VOZIKIS Institute f Phtgrammetry and Remte Sensing, Vienna University f Technlgy Gusshausstrasse 27-29, 1040 Vienna, AUSTRIA Abstract: - The gal f the described pilt prject was t reduce time, cst and pesticides fr efficient cntrlling f live tree pests. The integratin f mdern technlgies such as GPS (Glbal Psitining System), GIS (Gegraphic Infrmatin Systems) and the use f Gedata seem t prvide a slutin mre eclgical and beneficial. The prpsed methd aims at spraying nly in the necessary regin and nt ver the whle area as it had been dne in the past. Thrugh the cmbinatin f agrnmic knwledge and technical knw-hw a GIS was built as a decisin making tl in rder t achieve the pursued gal. The results f the pilt prject are als discussed in this paper and shw that such (mdern surveying) techniques becme inevitable if we want t achieve maximum results (in pest cntrl) and at the same time manage t reduce the pesticides and hence prtect the envirnment. Key-Wrds: - GIS, GPS, Risk Management, Gedata, Expert System, Database 1 Intrductin In the past, the fight against live tree diseases in Greece was carried ut by spraying the live grves frm the air using light-weight airplanes r helicpters. As this way f dealing with the prblem had a lng-lasting negative eclgical effect n the envirnment, the Greek Ministry f Agriculture and the Eurpean Unin decided in 1996 t replace the airbrne sprinkling prcedures with terrestrial spraying vehicles r even back-sprayers. Thus it was decided t make a pilt prject t see whether the efficiency f pest cntrl can be increased by using mdern surveying techniques such as GIS, Remte Sensing, GPS and Gedata. Thrugh the cllabratin between the Lcal Authrities f the prefecture f Fkida (Τοπική Αυτοδιοίκηση Φωκίδας -ΤΑΦ) and cmpany GEOMET Ltd. (Athens) a specific prcedure and the necessary tls (hard- and sftware) were develped. This develpment was based n the lng experience and agrnmic knwledge f ΤΑΦ and the technical understanding and knw-hw f GEOMET Ltd. in the fields f GPS, GIS and GeData.

2 The area f the pilt prject was apprximately acres with a tree density f trees per acre. The main gals were the testing f a lgical mdel f the applicatin under real cnditins, the testing f the perability f the decisin-making tl, the chice f the mst suitable spatial data which wuld be used fr the applicatin and final specificatins fr the hard- and sftware. While the hardware specificatins culd be fulfilled by cmmercially ffered equipment, this was nt the case fr the sftware. Thus the develpment f a specific prgram became a necessity. This sftware package was develped by cmpany Terra Ltd. (Athens) under the supervisin f Gemet Ltd. The final prduct (called DAC) cnsited f a decisin-making system / GIS (develped in ERDAS and ArcInf envirnment), units f GPS (Leica GS5 with PalmV and ARCPAD, Garmin GPSMap 76S) and GPRS units. The present paper describes the technical and ecnmical factrs f the DAC-system, as well as the utcmes f the pilt prject. Furthermre, the imprvement f the sprinkling prcedure depending n the lcatin f insect traps, the prcessing f the multi-tempral data, the planning and executin f the sprinkling, the surveying f the areas under investigatin and the analysis f wrking time, cst and amunt f used pesticides are discussed. Figure 1: Input data fr the GIS.

3 2 GIS Preparatin The task was t create an Expert System in frm f a GIS in rder t fulfil the demanded requirements. Since the behaviur and expansin f the live tree pests depend n many factrs (distance t varius bjects, terrain slpe and rientatin, humidity, vegetatin etc.) the input data fr the GIS primary database had t be carefully planned (Figure 1): Raster data: aerial and spacebrne rthphts, thermal Landsat images, (DEM) Digital Elevatin Mdel Vectr data: rads, wells, creeks, castlines, bridges, water drilling, canals, regins f agricultural activities, regins f cattle activities, etc. Other data: meterlgical data (temperature, humidity, infrmatin frm ther agrnmic surces. Each data-set was put n a different layer in the GIS and wuld prvide imprtant infrmatin fr deducing the behaviur f the live tree pests. Additinally, aspects and slpes f the terrain tgether with their rientatin were calculated, since they t affect the prliferatin and spreading-ut f the pests and als infrm us abut the accessibility t certain areas. After all needed infrmatin was imprted int the database, rules depending n multiple cmpnents (entries f the database) were defined, in rder fr the GIS t wrk as a decisin making tl. During the whle prcedure f the prject (as described in the fllwing wrkflw) the GIS database gt mre and mre infrmatin, in rder t prvide the end-user with mre and mre precise infrmatin abut where t perfrm the spraying. Figure 2: Prpsed Wrkflw. 3 Prpsed Wrkflw In this sectin the prpsed wrkflw is described (see als Figure 2). The first step was t place insect traps (McPhail type) ver the area under investigatin. It was decided t place ne insect trap every 1000 trees. Since the GIS hlds tree density infrmatin, interim lcatins fr placing the insect traps were

4 autmatically suggested by the GIS. Of curse it may happen that an insect trap might nt fall exactly n a tree (because individual trees are nt registered in the GIS); hence pstprcessing (relcating f misplaced insect traps) had t be carried ut. This crrectin f the psitin f the traps culd be dne in tw ways: Either by manually mving the insect trap n screen s it was placed exactly n tp f a tree in the rthpht (in this case the user had t g afterwards int the field with a GPS/palmV device in rder t place the insect trap at the previusly planned lcatin), r by ging utdrs in the grve with a remte GPRS unit and placing the insect trap n a tree (clse t the lcatin that was suggested by the GIS). Afterwards the GPRS data was imprted int the GIS and the system was autmatically updated with the new (true) crdinates f the in-situplaced insect traps. Figure 3: Traps infrmatin. Anther imprtant data surce fr updating the GIS cntinuusly was infrmatin abut the live crps. This infrmatin was cllected every 15 days frm 10 trees ver the whle prject area. The prperties entered int the database f the cllected lives were: Next step was t perfrm a segmentatin ver the regin. This was dne by applying a stratificatin (density classificatin), where each segment cntained 30 insect traps. An additinal cnstraint was that at least ne edge f each cmputed segment shuld lie n a rad, s that gd accessibility t each segment was guaranteed. fertile infertile exit (Did any pests exit the live fruit?) PrBe (Is there any indicatin f the Prlasiptera Berlesiana parasite? This parasite nests clse t pest eggs and eats them.) Every five days the insect traps were inspected and the fllwing data was entered in-situ int a palm device: number f insects number f males number f fertile insects ttal number bigger than 3 rati between female and male bigger than 7/3 This palm device was afterwards synchrnised with the GIS, s that all data was stred in the database with the crrespnding date (Figure 3). Frm this infrmatin the percentage f already damaged trees culd be derived. Mrever, every July lives were cllected t examine their pip. This was dne fr ne tree per segment. The prperties f each kernel culd be hard r sft. After the GIS had been fully updated, the targeted spraying prcedures culd be cncluded (see Figure 2). In case the system reprted that n spraying at all was needed, that is t say that there were n pests, an additinal diagnsis was carried ut in rder t cnfirm the GIS decisin. A few randmly chsen trees (that where well

5 distributed ver the whle area) where heavily sprayed. Under these trees special receptin nets were placed in rder t see (1,2 and 3 hurs after the spraying) whether any live pests had existed and thus fallen ff the tree. This way it culd be verified if the system s decisins was crrect r nt. In areas were the system predicted pests, the spraying was carried ut n a (segment-wise) tree-by-tree basis. Again all available infrmatin (pest situatin in traps after the spraying) was imprted int the GIS and the decisin making-tl prvided the end user the infrmatin which tree had t be re-sprayed again. The GIS accentuated certain regins with 4 different kinds f ALERTS and marked the trees/areas where the pests persisted. Hw these ALERTS were cncluded can be seen in the fllwing flwchart (Figure 4). Figure 4: Flwchart fr the decisin f spraying. Fr example, if an area was labelled with alert1, alert2, alert 3 and mre than 30% f alert 4, repeated targeted spraying was bligatry. Althugh mst f the alteratins f different alerts gave the end-user a hint what t d in the crrespnding regins, there existed sme cases were still an agrnmist s pinin was needed in rder t decide whether t spray again r nt. and develped by the cmpanies Gemet Ltd. and Heletel (Figure 5). Thrugh this system the headquarters culd always keep track (nline) where the tractrs (which were equipped with GPRS units) where and which areas had been sprayed. In case f a mistake (e.g. spraying trees utside the spraying radius etc.) an interventin was very easy. The repeated targeted spraying fllwed a certain iterative scheme that depended n the number f persisting pests in an area that had been sprayed befre. The system cmputed autmatically hw big the spraying radius arund an insect trap shuld be, in rder t remve all the remaining pests. Fr the trained spraying peratr wh went ut int the grve fr the spraying it was very difficult r even impssible t knw precisely which trees were inside the previusly determined spraying radius and which nt. Thus an AVL (Autmatic Vehicle Lcatin System) named GPS-tracker was designed Figure 5: GPS-tracker screensht In the future the tractrs will als be equipped with PalmV/GPS units, s that the specialized

6 spraying teams are navigated t the spraying lcatins as fast as pssible. Here als the terrain s tpgraphy will be cnsidered (e.g. fr cases where slpes are t steep fr the tractr). 4 Results and Cnclusins We described an advanced methd fr pest cntrl in live grves. The suggested methdlgy is based n an expert system (GIS that is trained by getting and mre and mre infrmatin during the prcedure and hence is in psitin t deliver mre and mre accurate and reliable results). The gal was t minimize the amunt f pesticides used fr fighting the pests and make the prcedures as efficient as pssible in rder t reduce cst and time. The pilt prject shwed that when using mdern surveying technlgies tgether with multi-tempral data cming frm varius surces, the verall time can be reduced up t 20%, the cst up t 18% and mst imprtant f all, the amunt f used pesticides is 23-25% less. Since the pilt prject cvers nly a small area ( acres) it can be said that if this prcedure is fllwed als fr bigger regins the pesticide reductin will be even higher (mre than 25%) and thus the highest pssible degree f prtectin f the envirnment is guaranteed.