A framework for the management of agricultural resources with automated aerial imagery detection

Abstract

The acquisition of data through remote sensing represents a significant advantage in agriculture, as it allows researchers to perform faster and cheaper inspections over large areas. Currently, extensive researches have been done on technical solutions that can benefit simultaneously from both: vast amounts of raw data (big data) extracted from satellite images and Unmanned Aerial Vehicle (UAV) and novel algorithms in Machine Learning for image processing. In this experiment, we provide an approach that fulfills the necessities of rapid food security, assessment, planning, exploitation, and management of agricultural resources by introducing a pipeline for the automatic localization and classification of four types of fruit trees (coconut, banana, mango, and papaya) and the segmentation of roads in the Kingdom of Tonga, using high-resolution aerial imagery (0.04 m).We used two supervised deep convolutional neural network (CNN): the first, to localize and classify trees (localization) and the second, to mask the streets from the aerial imagery for transportation purposes (semantic segmentation). Additionally, we propose auxiliary methods to determine the density of groupings of each of these trees species, based on the detection results from the localization task and render it in Density Maps that allow comprehending the condition of the agriculture site quickly. Ultimately, we introduce a method to optimize the harvesting of fruits, based on specific sceneries, such as maximum time, path length, and location of warehouses and security points.