This is a book review of 'The Micro-Doppler Effect in Radar' by V. C. Chen. Artech House, 16 Sussex Street, London, SW1V 4RW, UK. 2011. 290pp + diskette. Illustrated. £90. ISBN 978-1-60807-057-2.

'The Micro-Doppler Effect in Radar' by V.C. Chen

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r2=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.

Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images - eScholarship

Low-cost consumer radar integrated circuits combined with recent advances in machine learning have opened up a range of new possibilities in smart sensing. In this paper, we use a miniature radar sensor to capture Doppler signatures of 14 different hand gestures and train a deep convolutional neural network (DCNN) to classify these captured gestures. We utilize two receiving antennas of a continuous-wave Doppler radar capable of producing the in-phase and quadrature components of the beat signals. We map these two beat signals into three input channels of a DCNN as two spectrograms and an angle of arrival matrix. The classification results of the proposed architecture show a gesture classification accuracy exceeding 95% and a very low confusion between different gestures. This is almost 10% improvement over the single-channel Doppler methods reported in the literature.

Hand-Gesture Recognition Using Two-Antenna Doppler Radar With Deep Convolutional Neural Networks

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

Machine Learning in Agriculture: A Comprehensive Updated Review.

A survey of deep learning techniques for weed detection from images

Weed control is one of the biggest challenges in organic farms or nature reserve areas where mass spraying is prohibited. Recent advancements in remote sensing and airborne technologies provide a f...

An open source workflow for weed mapping in native grassland using unmanned aerial vehicle: using Rumex obtusifolius as a case study

Moving targets induce unique micro-Doppler signatures. After conducting several measurements with human, vehicle, UAV and animal as targets using a 24 GHz radar, the micro-Doppler signatures are observed through time-velocity diagrams. A simple set of micro-Doppler features are then selected and extracted from the measurement data. Results show the potential of radar as a ground surveillance applications, which can operate under wide range of weather and lighting conditions with high privacy standards and low false alarm rate. This paper also indicates that with I/Q modulation in radar, the direction of the target's motion can be preserved, thus provides a more detailed description of the measured moving target.

Classification of moving targets using mirco-Doppler radar

Rumex obtusifolius (R. obtusifolius) is one of the most common non-cultivated weed in European grasslands. Its broad-leaved and wide-spread nature make this weed competitive with the native pasture species reducing grass yield (van Evert et al., 2010), while its oxalic acid content makes this species poisonous for livestock if large doses are consumed (Hejduk and Doleal, 2004). Therefore, early removal is preferred especially in organic dairy farms or conservation areas where mass spraying is prohibited. Remote sensing and airborne technologies offer fast and efficient support in environmental monitoring allowing early detection of invasive species, yet current studies mostly rely on object-based image analysis (OBIA) and proprietary software to perform weed classification that require substantial human inputs. In this work, an open source workflow for automatic weed detection using unmanned aerial vehicle (UAV) RGB-imagery of native grassland had been developed using deep learning techniques, based on a previously developed OBIA approach (Lam et al., 2019). During the study, DJI Phantom 3 and 4 Pro were used for data acquisition throughout the vegetation period in 2018 and early 2019 at a nature conversation area in North Rhine-Westphalia, Germany. Images were processed using OpenDroneMap to produce orthomosaics. OBIA methods were then performed using Python and QGIS to assist the data labelling process for training a convolutional neural network (CNN), which was later used as an image classifier. Preliminary results of the proposed workflow achieved an overall accuracy of 93.8% and had demonstrated the capability in mapping R. obtusifolius in datasets collected at various flight altitudes, camera settings and light conditions. This shows the potential of developing a repeatable and robust system for semior fully-automated early weed detection in grassland using UAV-imagery.

https://www.mdpi.com/2504-3900/30/1/34/pdf

Mapping Invasive Rumex obtusifolius in Grassland Using Unmanned Aerial Vehicle

Scale Drone Mapping on K8S: Auto-scale Drone Imagery Processing on Kubernetes-orchestrated On-premise Cloud-computing Platform.

The acquisition, storage, and processing of huge amounts of data and their fast analysis to generate information is not a new approach, but it becomes challenging through smart decision-making on the choice of hardware and software improvements. In the specific cases of environment protection, nature conservation, and precision farming, where fast and accurate reactions are required, drone technologies with imaging sensors are of interest in many research groups. However, post-processing of the images acquired by drone-based sensors such as the generation of orthomosaics from aerial images and superimposing the orthomosaics on a global map to identify the exact locations of the interested area is computationally intensive and sometimes takes hours or even days to achieve desired results. Initial tests have shown that photogrammetry software takes less time to generate an orthomosaic by running them on a workstation with higher CPU, RAM and GPU configurations. Tasks like setting up the application environment with dependencies, making this setup portable and manage installed services can be challenging, especially for small-and-medium-sized enterprises that have limited resources in exploring different architectures. To enhance the competitiveness of the small and medium-sized enterprises and research institutions, the accessibility of the proposed solution includes the integration of open-source tools and frameworks such as Kubernetes (version v1.13.4, available online: https://kubernetes.io/) and OpenDroneMap (version 0.3, available online: https://github.com/OpenDroneMap/ODM) enabling a reference architecture that is as vendor-neutral as possible. Current work is based on an on-premise cluster computing approach for fast and efficient photogrammetry process using open source software such as OpenDroneMap combined with light-weight containerization techniques such as Docker (version 17.12.1, available online: https://www.docker.io/), orchestrated by Kubernetes. The services provided by OpenDroneMap enable microservice-based architecture. These container-based services can be administered easily by a container orchestrator like Kubernetes. After setting up the servers with core OpenDroneMap services on our container-based cluster with Kubernetes as the orchestrator engine, the plan is to use the advantages of Kubernetes' powerful management capabilities to help maximize resource efficiency as the basis for creating Service Level Agreements to provide a cloud service.

https://www.mdpi.com/2504-3900/30/1/38/pdf

Olee Hoi Ying Lam

Papers

An open source workflow for weed mapping in native grassland using unmanned aerial vehicle: using Rumex obtusifolius as a case study

Classification of moving targets using mirco-Doppler radar

Mapping Invasive Rumex obtusifolius in Grassland Using Unmanned Aerial Vehicle

Scale Drone Mapping on K8S: Auto-scale Drone Imagery Processing on Kubernetes-orchestrated On-premise Cloud-computing Platform.

Applicability of Remote Sensing Workflow in Kubernetes-Managed On-premise Cluster Environment