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Artificial Intelligence has enabled the implementation of more accurate andefficient solutions to problems in various areas. In the agricultural sector,one of the main needs is to know at all times the extent of land occupied ornot by crops in order to improve production and profitability. The traditionalmethods of calculation demand the collection of data manually and in person inthe field, causing high labor costs, execution times, and inaccuracy in theresults. The present work proposes a new method based on Deep Learningtechniques complemented with conventional programming for the determination ofthe area of populated and unpopulated crop areas. We have considered as a casestudy one of the most recognized companies in the planting and harvesting ofsugar cane in Ecuador. The strategy combines a Generative Adversarial NeuralNetwork (GAN) that is trained on a dataset of aerial photographs of natural andurban landscapes to improve image resolution; a Convolutional Neural Network(CNN) trained on a dataset of aerial photographs of sugar cane plots todistinguish populated or unpopulated crop areas; and a standard imageprocessing module for the calculation of areas in a percentage manner. Theexperiments performed demonstrate a significant improvement in the quality ofthe aerial photographs as well as a remarkable differentiation betweenpopulated and unpopulated crop areas, consequently, a more accurate result ofcultivated and uncultivated areas. The proposed method can be extended to thedetection of possible pests, areas of weed vegetation, dynamic cropdevelopment, and both qualitative and quantitative quality control.

Estimaci\'on de \'areas de cultivo mediante Deep Learning y programaci\'on convencional