An analysis of damage caused by climate change on the wheat crop in each of its stages of development was carried out, for this, a Backpropagation Artificial Neural Network and an analysis module for the permanence of climatic conditions were used, ten variables were used meteorological and 68685 daily records from various regions of the world, using 79% for training and 21% to validate the network. Regarding the analysis of the damage by stage of development in function of the climate, that is to say, of fenológico damage, the criteria proposed by the Organization of the United Nations for the Feeding and the Agriculture (FAO) and the scale Zadoks were considered. The technological strategy reached an accuracy of 84%, making it suitable for diagnosing phenological damage in the wheat plant, and constitutes an alternative to strengthen sustainable adaptation strategies and food security.
An analysis of damage caused by climate change on the wheat crop in each of its stages of development was carried out, for this, a Backpropagation Artificial Neural Network and an analysis module for the permanence of climatic conditions were used, ten variables were used meteorological and 68685 daily records from various regions of the world, using 79% for training and 21% to validate the network. Regarding the analysis of the damage by stage of development in function of the climate, that is to say, of fenológico damage, the criteria proposed by the Organization of the United Nations for the Feeding and the Agriculture (FAO) and the scale Zadoks were considered. The technological strategy reached an accuracy of 84%, making it suitable for diagnosing phenological damage in the wheat plant, and constitutes an alternative to strengthen sustainable adaptation strategies and food security.