A Deep Learning Driven Algorithmic Pipeline for Autonomous Navigation in Row-Based Crops

Abstract

Expensive sensors and inefficient algorithmic pipelines significantly affect the overall cost of autonomous machines. However, affordable robotic solutions are essential to practical usage, and their financial impact constitutes a fundamental requirement to employ service robotics in most fields of application. Among all, researchers in the precision agriculture domain strive to devise robust and cost-effective autonomous platforms in order to provide genuinely large-scale competitive solutions. In this article, we present a complete algorithmic pipeline for row-based crops autonomous navigation, specifically designed to cope with low-range sensors and seasonal variations. Firstly, we build on a robust data-driven methodology to generate a viable path for the autonomous machine, covering the full extension of the crop with only the occupancy grid map information of the field. Moreover, our solution leverages on latest advancement of deep learning optimization techniques and synthetic generation of data to provide an affordable solution that efficiently tackles the well-known Global Navigation Satellite System unreliability and degradation due to vegetation growing inside rows. Extensive experimentation and simulations against computer-generated environments and real-world crops demonstrated the robustness and intrinsic generalizability of our methodology that opens the possibility of highly affordable and fully autonomous machines.

GNSS-Free End-of-Row Detection and Headland Maneuvering for Orchard Navigation Using a Depth Camera

“…Their method does not utilize deep learning and relies on traditional feature extraction. Cerrato et al [11] utilized deep learning techniques and synthetic data generation to train a segmentation network to segment crops/foliage in RGB images. They also designed a segment-based control algorithm to steer the robot inside the row by keeping it in the row center.…”

Towards Infield Navigation: leveraging simulated data for crop row detection

“…Cerrato et al [19] has used a set of realistic augmentations to generate simulated dataset in an orchard setting. They have generated a large dataset with sufficient augmentations to train a deep learning model to predict crops in a simulated environment.…”

SMR-RS: An Improved Mask R-CNN Specialized for Rolled Rice Stubble Row Segmentation

“…As a research hotspot in the field of deep learning, convolutional neural networks have achieved excellent research results in agriculture in recent years, and they are widely used in various agricultural vision applications [30][31][32][33][34] and have demonstrated higher accuracy and wider applicability than other normal algorithms [35]. Deep learning-based crop row segmentation methods have also achieved excellent results: Silva [36] et al and Cao [37] et al used the Unet [38] model, and the improved Enet [39] model implemented segmentation of crop rows from an open dataset containing images of sugar beet rows in various complex environments.…”