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This study aims to develop a real-time algorithm, which can detect people even in arbitrary poses. To cover poor and changing light conditions, it does not rely on color information. The developed method is expected to run on computers with low computational resources so that it can be deployed on autonomous mobile robots.

The method is designed to have a people detection pipeline with a series of operations. Efficient point cloud processing steps with a novel head extraction operation provide possible head clusters in the scene. Classification of these clusters using support vector machines results in high speed and robust people detector.

The method is implemented on an autonomous mobile robot and results show that it can detect people with a frame rate of 28 Hz and equal error rate of 92 per cent. Also, in various non-standard poses, the detector is still able to classify people effectively.

The main limitation would be for point clouds similar to head shape causing false positives and disruptive accessories (like large hats) causing false negatives. Still, these can be overcome with sufficient training samples.

The method can be used in industrial and social mobile applications because of its robustness, low resource needs and low power consumption.

The paper introduces a novel and efficient technique to detect people in arbitrary poses, with poor light conditions and low computational resources. Solving all these problems in a single and lightweight method makes the study fulfill an important need for collaborative and autonomous mobile robots.

This paper aims to provide details of a number of recent and significant agricultural robot research and development activities.

Following an introduction, this first provides a brief overview of agricultural robot research. It then discusses a number of specific activities involving robots for precision weed control and fertiliser application. A selection of harvesting robots and allied technological developments is then considered and is followed by concluding comments.

Agricultural robots are the topic of an extensive research and development effort. Several autonomous robots aimed at precision weed control and fertiliser application have reached the pre-production stage. Equally, harvesting robots are at an advanced stage of development. Both classes exploit state-of-the-art machine vision and image processing technologies which are the topic of a major research effort. These developments will contribute to the forecasted rapid growth in the agricultural robot markets during the next decade.

Robots are expected to play a significant role in meeting the ever increasing demand for food, and this paper provides details of some recent agricultural robot research and development activities.

The purpose of this paper is to study the effects of the herbicide triclopyr, nitrogen and their combinations on Striga incidence and sorghum growth.

A greenhouse study was undertaken in season 2013. Sorghum cv Wad Ahmed, urea and triclopyr were employed. Treatments were arranged in a randomized complete design with four replicates.

Nitrogen alone suppressed the parasite completely early in the season. Triclopyr at 0.3 and 0.4 kg a.e. ha−1 reduced Striga emergence by 92.9 and 58.3 per cent early and late in the season, respectively. Triclopyr at 0.3 kg a.e. ha−1 applied subsequent to nitrogen at 43.8 kg ha−1 effected poor control of the parasite. Unrestricted Striga parasitism reduced sorghum height and chlorophyll content by 50.38 and 16.62 per cent, respectively. Triclopyr, nitrogen and their combination improved sorghum growth considerably.

The results suggest that the herbicide when applied subsequent to nitrogen afforded the most consistent performance and resulted in the highest suppression of the parasite.

The purpose of this paper is to propose going one step further in the simulation tools related to agriculture by integrating fleets of mobile robots for the execution of precision agriculture techniques. The proposed new simulation environment allows the user to define different mobiles robots and agricultural implements.

With this computational tool, the crop field, the fleet of robots and the different sensors and actuators that are incorporated into each robot can be configured by means of two interfaces: a configuration interface and a graphical interface, which interact with each other.

The system presented in this article unifies two very different areas – robotics and agriculture – to study and evaluate the implementation of precision agriculture techniques in a 3D virtual world. The simulation environment allows the users to represent realistic characteristics from a defined location and to model different variabilities that may affect the task performance accuracy of the fleet of robots.

This simulation environment, the first in incorporating fleets of heterogeneous mobile robots, provides realistic 3D simulations and videos, which grant a good representation and a better understanding of the robot labor in agricultural activities for researchers and engineers from different areas, who could be involved in the design and application of precision agriculture techniques. The environment is available at the internet, which is an added value for its expansion in the agriculture/robotics family.

This study aims to illustrate the growing role that sensors play in agriculture, with an emphasis on precision agricultural practices.

Following a short introduction, this study first provides an overview of agricultural measurements and applications. It then discusses the importance of airborne and land-based optical sensing techniques and the role of the normalised difference vegetation index. Sensors used on conventional and robotic agricultural machines are considered next, and fixed sensors and sensor networks are then discussed. Finally, brief concluding comments are drawn.

This shows that much modern agriculture is a high-technology business which relies on a multitude of sensor-based measurements. Sensors are based on a diversity of optical and other technologies and measure a wide range of physical and chemical variables. They are deployed in the air, on agricultural machines and in the field and generate data that can be used to enhance productivity and reduce both costs and the impact on the environment.

This provides a detailed insight into the important role played by sensors in modern agricultural practices.

Although the environmental impacts of groundwater abstraction and water table recovery are now known, historically they were given scant recognition due to the ad hoc nature of the development of aquifers. This paper summarises the potential impacts of groundwater exploitation, and reviews the effects of rising water tables on urban areas following the cessation or reduction of pumping. The use of GIS in assessing the implications for the environment is considered, in particular the coupling of hydrological models to a GIS for evaluating the impact of the drawdown of shallow water tables on agriculture.

This article describes the image processing and tracking algorithms used in a mechanical hoe that accurately weeds between parallel rows of crops under variable outdoor illumination conditions.

To solve problems of low intelligence and poor robustness of traditional navigation systems, the purpose of this paper is to propose a brain-inspired localization method of the unmanned aerial vehicle (UAV).

First, the yaw angle of the UAV is obtained by modeling head direction cells with one-dimension continuous attractor neural network (1 D-CANN) and then inputs into 3D grid cells. After that, the motion information of the UAV is encoded as the firing of 3 D grid cells using 3 D-CANN. Finally, the current position of the UAV can be decoded from the neuron firing through the period-adic method.

Simulation results suggest that continuous yaw and position information can be generated from the conjunctive model of head direction cells and grid cells.

The proposed period-adic cell decoding method can provide a UAV with the 3 D position, which is more intelligent and robust than traditional navigation methods.

The safety of operators handling pesticides is still one of the main problems facing Thai agricultural workers. The purpose of this paper is to study the safety of farmers and the techniques recommended to farmers by the Department of Agriculture in Thailand, i.e. spraying to achieve optimum spray volume with consideration for wind direction – henceforth referred to as officer techniques.

Operator exposure was detected by verifying the deposition of dye tracer on the coveralls worn by ten spray service team leaders, for all applications between May and June 2017. For each technique, a total of 15 patches were attached at the lower legs, thigh, chest, forearms, upper arms, hands, face, forehead and back. Each individual technique was performed four times in the area of 1,800 m².

The results showed that the deposits with the farmers’ techniques was much higher than with officers’ techniques ranging from 2.32 to 23.91 times at the tillering stage and 9.90 to 56.79 times at the booting stage, respectively. These results indicate that the spray application technique has a considerable potential for reducing the contamination of spray operators by 56.96–98.23 percent. Operator safety can be considerably improved by the spray application technique employed. Without any investment and changing equipment, only by considering wind direction, officers’ techniques could avoid much deposition, which is the most practical operation in the field. The boom sprayer as a novel recommended technique is an alternative giving a positive result and it can be a substitute for the conventional method. Furthermore, the authors must pay attention to personal protective equipment (PPE) because depositions were discovered on the whole of the bodies of those tested. PPE is the best way to protect an operator from pesticide contamination.

Operator exposure data can be helpful in further development of exposure models and databases for risk assessment and pesticide registration in Thailand.

This paper aims to assess factors that affect carbon sequestration on green roofs.

The most current academic literature related to carbon sequestration and green roofs carbon sequestration performance was reviewed.

Factors affecting carbon sequestration were discussed and classified into the following factors: plants, physical and maintenance factors. The authors’ findings are significant because they can be used to optimize green roofs performance for carbon sequestration.

Factors affecting carbon sequestration will optimize intensive green roofs performance.