CGMap: A Geospatially Aware Deep Learning Framework for Crop Gap Mapping Using UAV

2026-07-21

AI Summary

This study addresses the inefficiency and inaccuracy of manual sugarcane emergence monitoring, which struggles to reliably identify missing-plant areas (“bald patches”). To overcome this, the authors propose an automated pipeline leveraging UAV imagery and YOLOv8-based object detection, enhanced by a novel use of Minimum Spanning Trees (MST) to normalize planting orientation. This approach effectively accommodates diverse field layouts and robustly extracts sugarcane rows. Trained on UAV data from multiple agro-climatic zones, the model converts detection outputs into geospatial point clouds and exports them in Well-Known Text (WKT) format for integration with GIS platforms. The resulting high-resolution emergence maps enable precise replanting guidance, thereby enhancing crop yield, resource-use efficiency, and overall agricultural sustainability.

Abstract

In India, crop germination is primarily monitored by visual inspection and manual counting, which are prone to errors, despite their crucial role in determining eventual yield potential. This paper highlights a deep learning based pipeline which uses object detection methods and drone imagery to assess and provide a precise count of sugarcane germination in fields. The approch uses a pre-trained AI model to find germinated plant sampling and identify gaps, also known as ``bald spots'', which restricts field productivity. The techniques used here relies on the YOLOV8 architecture, which was trained on a carefully selected dataset of UAV photos taken in various agroclimatic zones of India. Here, we bring upon a novel orientation-normalization technique that uses minimum Spanning Trees (MST) to account for variations in planting geometry, allowing for dependable row and column extraction across a variety of field layouts. By converting detected seedlings into spatial point clouds, emergence gaps can be inferred from the anticipated spacing between plants. A geospatial germination map exported in Well-Known Text (WKT) format is the end result, and it can be easily incorporated into GIS platforms used by sugar mills and agronomists to direct transplant initiatives. Timely interventions based on the insights provided by the algorithm can significantly increase yield, resulting in higher profits. Hence, support proper allocation of resources, avoid wastage, and enhance long-term sustainability.

Problem

• crop gap mapping
• germination monitoring
• UAV imagery
• bald spots
• geospatial analysis

Innovation

• geospatial deep learning
• YOLOv8
• minimum spanning tree
• crop gap mapping
• UAV imagery