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Modeling pheromone dispensers using genetic programming

We employ genetic programming to model the release kinetics of a pheromone dispenser used to combat pests in an ecofriendly manner, by means of mating disruption

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Modeling pheromone dispensers using genetic programming

  1. 1. Modeling pheromone dispensers using genetic programming Eva Alfaro-Cid, Anna Esparcia-Alcázar, Pilar Moya, Beatriu Femenia-Ferrer, Ken Sharman, J.J. Merelo
  2. 2. Contents • Objetive • Introduction: mating disruption • Problem description • Strongly typed genetic programming • Modeling results • Conclusions and future work
  3. 3. Objetives • Modeling the pheromone release kinetics of an experimental dispenser developed in the Centro de Ecología Química Agrícola (CEQA) of the Universidad Politécnica de Valencia. • To validate the hypothesis (which is based on experimental results) that the performance of the CEQA dispenser is independent of the atmospheric conditions, as opposed to the most widely used commercial dispenser, Isomate CPlus.)
  4. 4. Mating disruption technique • Mating disruption by sexual confusion is an agricultural technique that intends to substitute the use of insecticides for pest control. • Sexual confusion is achieved by the diffusion of large amounts of sexual pheromone, so that the males are confused and mating is disrupted. How? → using pheromone dispensers •)
  5. 5. Pheromone dispensers •The Centro de Ecología Química Agrícola (CEQA) of the Universidad Politécnica de Valencia has developed biodegradable dispensers which work effectively during the whole flight period of the pest.
  6. 6. A few figures • 1 kg of pheromone costs 1000 € • 1 dispenser takes 200 mg of pheromone → i.e. 1 dispenser costs 20 cents (+ manufacturing) • In 1 Ha there must be 500 or 1000 dispensers (depending on the pest) – i.e cost is 100 or 200 € per Ha (+ handwork) On the other hand, • Spraying with a classical pesticide costs 20-30 €/Ha
  7. 7. Problem description • Let the residual r be the percentage of product that has not been released into the atmosphere • For a given dispenser, find a function r (∙), so that r = r ( t, T, H ) where: t = time T = temperature H = humidity r ≈ r (t) Our hypothesis is that for the CEQA dispenser
  8. 8. Available data Residual available data 120 100 Residual (%) CPlus 2005 80 CPlus 2006 60 CEQA 2005 40 CEQA 2006 20 0 1 16 31 46 61 76 91 106 121 136 151 166 Day Year 2005 15 data of dispenser CEQA 13 data of dispenser Isomate CPlus Year 2006 7 data of both
  9. 9. Genetic programming Algorithm Strongly typed GP, generational with elitism (0.1 %). Inicialization Ramped half and half Selection Tournament selection for all genetic operators Genetic operators Replacement, crossover and mutation Tree internal nodes are selected with a probability of 0.9, terminals are selected with a probability of 0.1 and the root cannot be selected as crossover or mutation point. The resulting trees are accepted in the population only if their length is smaller than 18. Termination criterion 51 generations (including the initial generation) Parameters Population size, popSize = 2000 Tournament size, tSize = 7 Mutation rate, pM = 0.1 Crossover rate, pC = 0.8 Replacement rate, pR = 0.1 Number of runs, n = 10
  10. 10. Strongly typed genetic programming 4 types of variables were considered : • temperature • humidity • time • real value Cost function: Mean Squared Error (MSE) (rcalculated - rmeasured)2 MSE = 1/n * n
  11. 11. Genetic programming: Functions and terminals Available atmospheric data (daily): maximum temperature and mean temperature, maximum humidity and mean humidity. Data obtained through the Xarxa Agrometeorològica de Catalunya. Temperature and humidity values until 9 days prior to the residual measurement were considered, i.e. T0 is the temperature the day the residual was measured Tn is the temperature n days before, n = 1..9 Terminal sets: • { mean temperature, mean humidity, time, } • { maximum temperature, maximum humidity, time, } • { time, } Function set: { +, -, *, /, exp, log}
  12. 12. Results - CEQA Year 2006 Year 2005 Mean T 120 120 100 100 Residual (%) Residual (%) and H 80 80 60 60 values 40 40 20 20 0 0 1 21 41 61 81 101 121 141 161 1 21 41 61 81 101 121 141 161 Day Day Year 2006 Year 2005 120 120 Maximum 100 100 Residual (%) Residual (%) 80 80 T and H 60 60 values 40 40 20 20 0 0 1 21 41 61 81 101 121 141 161 1 21 41 61 81 101 121 141 161 Day Day Year 2006 Year 2005 120 120 100 100 Residual (%) Residual (%) Only time 80 80 60 60 40 40 20 20 0 0 1 21 41 61 81 101 121 141 161 1 21 41 61 81 101 121 141 161 Day Day
  13. 13. Results - CEQA
  14. 14. Results - CEQA Time as the only variable: 2 t t t r ( t ) 96 . 03 0 . 23 t log 79 . 47 t 70 . 77 t log( t ) 679 . 29 4t
  15. 15. Results – Isomate CPlus Year 2006 Year 2005 Mean T 120 120 100 100 and H Residual (%) Residual (%) 80 80 60 60 values 40 40 20 20 0 0 1 21 41 61 81 101 1 21 41 61 81 101 121 Day Day Year 2006 Year 2005 120 120 Maximum 100 100 Residual (%) Residual (%) 80 80 T and H 60 60 40 40 values 20 20 0 0 1 21 41 61 81 101 1 21 41 61 81 101 121 Day Day Year 2006 Year 2005 120 120 100 100 Only time Residual (%) Residual (%) 80 80 60 60 40 40 20 20 0 0 1 21 41 61 81 101 121 1 21 41 61 81 101 121 Day Day
  16. 16. Results – Isomate CPlus
  17. 17. Results – Isomate CPlus Time as the only variable: 74 . 32 81 . 36 1 . 29 t N r (t ) 76 . 46 0 . 23 t log t 93 . 95 log t t log 81 . 46 1 . 29 t D 5642 . 08 N log log t 0 . 77 t 15 . 67 0 . 02 t 1 . 47 log t 71 . 6 74 . 93 log log t t 83 . 38 71 . 6 79 . 8 log 2 log t D 56 . 67 log log t 74 . 93 74 . 93 log t t 83 . 38 t
  18. 18. Results – Isomate CPlus Maximum temperature, humidity and time as variables: 271 . 53 4 6 . 64 10 exp t t r (t ) 92 . 29 log 1 . 42 T 9 T 7 t log T 0 T1 t T1 L 271 . 53 179 . 87 2 exp 1 . 32 10 exp t t L log log 2 T0 T1 t 271 . 53 T1 t 2 T 7 t log T 2 t log 83 . 3 t log log exp T1 t H 9 exp 271 . 53 t 43 . 93 T 7
  19. 19. Conclusions • Genetic programming has proven to be capable of finding functions that fit well the performance of both dispensers. • For the CEQA dispenser the fitting of the functions is better when the only variable under consideration is time. Although this is not a conclusive proof of the independence of the pheromone residual from the atmospheric conditions, it can be considered as an evidence in that sense. • The statistical test performed on the results obtained with the data of CPlus dispenser reveals that there is a significant difference between the results obtained using maximum values of temperature and humidity and the rest. This confirms prior experimental evidence that the atmospheric conditions have a big influence in the performance of these dispensers.
  20. 20. Future work Long term • Modeling the release of pheromone in the environment. – Great economic interest → it would allow the optimisation of the placement of dispensers in the plot, hence minimising the number of dispensers needed to guarantee an efficient pest control. Short term • Inclusion of the gradient of temperature as a terminal for the GP algorithm, as it may be the case that the dispensers are more sensitive to sharp changes of temperature than to the temperature itself.
  21. 21. Thanks! aesparcia@iti.upv.es

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  • mikepham12

    Jul. 5, 2017

We employ genetic programming to model the release kinetics of a pheromone dispenser used to combat pests in an ecofriendly manner, by means of mating disruption

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