Pre-Harvest Factors Optimization Using Genetic Algorithm for Lettuce - Valenzuela, I.; Baldovino, R.; Bandala, A.; Dadios, E. (Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 2018)
The agricultural sector is facing problems on crop development due to climate change and global warming. Crops such as rice, tomato, corn, lettuce, potato, wheat, soybeans and others are affected. Through analyzing the graphical representation of data, no optimum values are observed. In this study, the suitability of the genetic algorithm in finding the best condition for producing high quality lettuce crop was determined. The parameters that were optimized are the light intensity, temperature and CO2. These parameters were essential preharvest factors for lettuce. The system selected the 50 fittest individuals based on the fitness score and then proceeds to the recombination process. A mutation has been applied to test if the solution is the global one. When the iterations had reached the required number of generation, the system stopped and gave the best condition for lettuce. Critical design on GA was done and the best fitness plot was obtained. The GA results showed that the optimum conditions for a highquality lettuce crop needs a light intensity of 175.22296 μmol/m2/s, a temperature of 19.36228 ºC and a CO2 level of 803.01855 ppm.