Agricultural Field Delineation

Proper identification of boundaries of agricultural fields is one of the major necessities for modern agriculture, precision farming, land use planning, monitoring of crops, and crop insurance. Conventionally, boundaries of agricultural fields are determined through manual digitization of satellite or aerial imagery, which may prove to be a laborious, lengthy, and error-prone process, especially when performed over larger geographical areas.

In this deep learning-based solution, boundaries of agricultural fields can be automatically extracted from satellite imagery. Using state-of-the-art image segmentation and machine learning techniques, the model is able to determine boundaries of each agricultural field without requiring much manual intervention. Automated boundaries may facilitate the generation of geospatial datasets for analysis, monitoring, and decision-making related to agriculture.

The automated process will help organizations to handle large sets of satellite imagery in an efficient manner.

The model is capable of adjusting itself to the variability in crop types, field types, geographical positions, seasons, and the nature of satellite images. As a result, it becomes applicable to many agricultural regions, regardless of the nature of fields, crop cultivation methods used, or degree of vegetation coverage.

The extracted field boundaries may be utilized in a GIS system and remote sensing tasks related to crop inventory creation, field-based crop monitoring, crop area estimation, agricultural land management, precision farming, yield estimates, and insurance valuation. In combination with other satellite-based datasets, these field boundaries may be used as the basis for further agricultural analysis and decision support.

Replacing time-consuming and repetitive manual work with an automated process based on deep learning allows for quickening agricultural field mapping process and making it scalable enough to use in large-scale geospatial analysis of agriculture.