  Artificial Intelligence has enabled the implementation of more accurate and
efficient 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 or
not by crops in order to improve production and profitability. The traditional
methods of calculation demand the collection of data manually and in person in
the field, causing high labor costs, execution times, and inaccuracy in the
results. The present work proposes a new method based on Deep Learning
techniques complemented with conventional programming for the determination of
the area of populated and unpopulated crop areas. We have considered as a case
study one of the most recognized companies in the planting and harvesting of
sugar cane in Ecuador. The strategy combines a Generative Adversarial Neural
Network (GAN) that is trained on a dataset of aerial photographs of natural and
urban landscapes to improve image resolution; a Convolutional Neural Network
(CNN) trained on a dataset of aerial photographs of sugar cane plots to
distinguish populated or unpopulated crop areas; and a standard image
processing module for the calculation of areas in a percentage manner. The
experiments performed demonstrate a significant improvement in the quality of
the aerial photographs as well as a remarkable differentiation between
populated and unpopulated crop areas, consequently, a more accurate result of
cultivated and uncultivated areas. The proposed method can be extended to the
detection of possible pests, areas of weed vegetation, dynamic crop
development, and both qualitative and quantitative quality control.
