Kamera polarimetriko bidezko markatzaile naturalen detekzioa

Azken urteotan, ikusmen artifiziala, izandako garapen handiagatik, geroz eta erabiliagoa izaten ari da industria arloan. Lan honetan mundu errealeko industriaren arazo jakin baten aurrean, objektuen detekzioan sakontzen den ikusmen artifizial sistema bat garatu da. Sistema honen helburua, hegazkinen muntaketa garaian bere fuselajean ezarri beharreko zuakerrak modu egokian lerrokatzeko, zuaker hauetan dauden markatzaile naturalak detektatzea da. Horrez gain, ohiko kamerak baztertuz, kamera polarimetrikoak deituriko kamera berezien onura eta desabantailak aztertu dira, hauekin lortutako irudiekin datuen gehikuntza (data augmentation) nola aplikatu daitekeen ikertuz. Horretarako, lehenengo kamera polarimetrikoaren funtzionamendua eta ezaugarriak ulertu dira. Segidan, kamera berezi horrekin ateratako argazkiak, zeinak detektatu beharreko markatzaile motak dituzten, bildu dira datu multzo bat osatuz. Ondoren, objektuen detekziorako dauden metodoen azterketa sakona egin eta arazo honetarako interesgarrienak diren metodoak sortutako datu multzoarekin erabili dira. Metodo aukeratuen artean, objektuen detekziorako sortutako Faster R-CNN metodoa dago eta horrez aparte, objektuen detekzioa irudien segmentaziotik burutzen duen metodo bat definitu da HRNet eredua erabiliz. Hori egitean, ahalik eta zehaztasun handiena bilatuz, metodoen detekzioetatik abiatuta eta teknika klasikoagoak erabilita, markatzaileen zentroa lortzeko prozedurak definitu eta inplementatu dira. Azkenik, proposatutako sistema Jetson Nano mikroordenagailu batean erabili da, integrazio prozesuarekin hasiz. Ondorio bezala, emaitzei erreparatuz, markatzaileen detekziorako proposatutako prozedura baliozkoa dela ikusi da. Frogatutako bi ereduek emaitza onak eman dituzte, baina zentroen detekzio zehatza burutzeko HRNet erabili da itzulitako segmentazio mapen informazio gehigarriagatik. Horrez gain, kamera polarimetrikoaren polarizazio fisikak arazo honetarako markatzaileen detekzioan ez duela emaitzak hobetzen gehiegi lagundu frogatu da. Markatzaileen zentroen detekzio zehatzean berriz, markatzaile guztien zentroa detektatu ezin izan den arren, polarizazioa erabiltzen duten irudiek detekzioan lagundu dutela ikusi da. Azkenik, proiektuarekin aurrera egin eta sakontzeko interesgarriak izan daitezkeen hurrengo pausuak definitu dira

Demonstrative simulations of L-PEACH: a computer-based model to understand how peach trees grow

L-PEACH is a computer-based model that simulates source-sink interactions, architecture and physiology of peach trees (Allen et al., 2005, 2006, 2007). The model integrates important concepts related to water transport and carbon assimilation, distribution, and use within the tree (DeJong et al., 2011). L-PEACH is able to simulate crop yield responses to commercial practices such as fruit thinning (Lopez et al., 2008) and pruning (Smith et al., 2008) and could be useful for making fruit growers understand how to optimize these operations. In this work we present several demonstrative simulations of L-PEACH to complement the existing references about L-PEACH and demonstrate its value to study, understand and teach how trees grow (DeJong et al., 2008). The FIRST SIMULATION corresponds with the version of L-PEACH that runs on a daily time-step (L-PEACH-d) (Lopez et al., 2008, 2010). The simulation shows the growth of a peach tree over three years. The color of the stem indicates the direction of the movement of carbon within the tree (white indicates no flux of carbon, increasing apical flux of carbon from light yellow to red, and increasing basal flux of carbon from light blue to deep purple) (see details of colors in Allen et al., 2005). During this simulation the tree was stopped during the dormant season between years and the trees were pruned by the model operator in a manner that is similar to how trees would be pruned when growing in an orchard. Also during the first year of tree growth, grafting is simulated by cutting the tree back in early spring and allowing the tree to grow again as it would in a tree nursery. After this first year the tree is cut back to a single trunk in the same manner as is commonly done when a tree is transplanted from a tree nursery to a commercial fruit orchard. In the SECOND SIMULATION a detailed section of the tree was selected to better appreciate the realism of leaf and fruit growth and in the THIRD SIMULATION we show how to prune a peach tree to a V-system. Responses to pruning were modelled based on the concept of apical dominance as described in Smith et al. (2008) and Lopez et al. (2008). Subsequent simulations correspond to the last version of the L-PEACH model that includes a xylem circuit so that the diurnal water potential of each organ could be simulated along with its physiological functioning and growth. Sub-models for leaf transpiration, soil water potential and the soil-plant interface were also incorporated to provide the driving force and pathway for water flow. In the FOURTH SIMULATION we presented the effect of different irrigation treatments (control irrigation and drought irrigation) on tree development, growth and fruit yield (Da Silva et al., 2011; 2014). L-PEACH-h was also use to illustrate the effect of severity of pruning in tree growth (FIFTH SIMULATION). We tested three levels of pruning: soft, control, and hard. The simulation indicates how trees that received hard pruning are able to recover a similar tree size than control and soft pruned trees due to the generation of vigorous shoots in response to hard pruning. The SIXTH SIMULATION was generated to demonstrate that L-PEACH can be also used to simulate the effect of size-controlling rootstock in tree growth (Da Silva et al., 2015). In this simulation we compared tree growth with a standard rootstock (Control) and a size-controlling rootstock (Rootstock) by reducing the hydraulic conductance of the ‘rootstock” piece (base of the trunk) by 50% in the size-controlling rootstock to simulate a reduction in vessel diameters and consequently reduced hydraulic conductance in that part of the tree. After four years of simulated growth, the virtual tree on the dwarfing rootstock was substantially smaller than the virtual tree on the control rootstock. What you can’t see in the movies is that the L-PEACH model calculates the distribution of light in the tree canopy as the tree grows and the rate of photosynthesis in each leaf during a simulated day or hour (depending on whether the daily or hourly models are used for the simulation). Then the distribution and use of photo-assimilates are calculated by the methods described in the papers cited below. The simulations are based on real environmental input data (light, temperature, day length, etc. collected from a real weather station located near a peach orchard) and development of tree architecture is based on developmental principles governing tree growth and detailed measurements of shoots of peach trees (see references). Description of files Simulation 1: L-PEACH-d over three years of growth. Simulation 2: Detailed growth of leaves and fruit using L-PEACH. Simulation 3: Pruning L-PEACH-d to a v-system. Simulation 4: Control irrigation vs. Drought irrigation using L-PEACH-h. Simulation 5: Reactions to soft, control and hard pruning using L-PEACH-h. Simulation 6: Simulating the effect of size-controlling rootstock using L-PEACH-h