Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields

Abstract

Automated robotic platforms are an important part of precision agriculture solutions for sustainable food production. Agri-robots require robust and accurate guidance systems in order to navigate between crops and to and from their base station. Onboard sensors such as machine vision cameras offer a flexible guidance alternative to more expensive solutions for structured environments such as scanning lidar or RTK-GNSS. The main challenges for visual crop row guidance are the dramatic differences in appearance of crops between farms and throughout the season and the variations in crop spacing and contours of the crop rows. Here we present a visual guidance pipeline for an agri-robot operating in strawberry fields in Norway that is based on semantic segmentation with a convolution neural network (CNN) to segment input RGB images into crop and not-crop (i.e., drivable terrain) regions. To handle the uneven contours of crop rows in Norway’s hilly agricultural regions, we develop a new adaptive multi-ROI method for fitting trajectories to the drivable regions. We test our approach in open-loop trials with a real agri-robot operating in the field and show that our approach compares favourably to other traditional guidance approaches.

Crop Row Segmentation and Detection in Paddy Fields Based on Treble-Classification Otsu and Double-Dimensional Clustering Method

“…. n k − 1), and the average distance between all these adjacent feature points is expressed as d k,avg , as shown in Equation (24).…”

“…Adhikari et al [23] trained a deep convolutional encoder decoder network to detect crop lines using semantic graphics. Ponnambalam et al [24] designed a convolution neural network to segment input images based on red, green and blue color system (RGB) into crop and non-crop regions. Although these approaches achieved good results, there is still no superiority of stereo vision and deep learning over traditional architecture in terms of time consumption, and this will cause a significant burden for computation devices.…”

Towards Autonomous Visual Navigation in Arable Fields

“…Kraemer et al [20] used a deep learning approach to reconcile the PSEP features by exploiting the likelihood maps of a deep neural networks (DNN). Also utilising DNNs, [21] proposed a convolutional neural network (CNN) for strawberry crop-row detection with accurate navigation. Lin et al [22] also showcase the potential of CNNs to reliably navigate a tea field by classifying tea rows.…”

Robot-supervised Learning of Crop Row Segmentation

“…Valada et al [13] collect data with RGB, NIR and depth from forest roads, and fuse these modalities in a CNN for segmentation that shows good results in challenging light conditions and appearance variations. Recently, learningbased semantic segmentation has also been applied for row following in agri-cultural environments, like tea plantations [3], and our earlier work in strawberry fields [14]. These works all relied on large quantities of manually-labelled training images to learn the different semantic classes.…”