FIELD IMAGE SEGMENTATION IN PRECISION AGRICULTURE

Abstract

Precision farming methods require consideration of subtle differences in plant growth processes in different areas of cultivated arable land. Differences in the relief of the field, its water supply, the thickness of the humus layer, etc. cause the need to rank the arable land for the application of agrotechnical operations of different intensities, which ultimately leads to higher yields and lower costs of agricultural crops. The ranking of arable land within a field is usually accomplished by segmentation of remotely sensed data. The creation of a segmentation system requires periodic remote and ground monitoring of fields, collection and processing of the received information with its geographical reference. Both satellite remote sensing systems and unmanned aerial platforms (UAPs) can be used for this purpose. The volume of information received, especially when using UAVs, is very significant, and the requirements for the speed of processing are high. In this regard, efficient methods of systematization and processing of the received images, which rely on sufficiently fast segmentation algorithms, are relevant. This paper considers methods of image segmentation from various remote sensing systems, which allow to increase the economic performance of agronomic measures in the precision farming loop. An example of a threshold segmentation program is given, which can be used separately or as part of an information system to support precision farming processes. The paper presents the results of its application for segmentation of satellite images of a field by NDVI index value. The conducted analysis and recommendations on the segmentation data will contribute to the prevention of environmental violations, yield losses due to sudden changes in weather conditions and differences in the relief of cultivated arable land.