Ole Green

A DSS for planning of soil-sensitive field operations

The current increased size of agricultural vehicles aggravates the problem of soil compaction causing increased energy requirements, increased CO2 emissions, and reduced yields. The aim of this paper was to develop a DSS for optimize route planning in terms of minimized risk for soil compaction for agricultural vehicles carrying time-depended loads. The developed system uses as input field and operational characteristics, including a potential risk indicator map based on specific measure of distributed soil physical-chemical properties. It provides the optimal field-work tracks traversal sequence which can be executed using state-of-the-art auto-steering and navigation-aiding systems available on modern agricultural vehicles. The system has been demonstrated and tested for heavy application units used for organic fertilizer. The risk factor was reduced up to 61% by using the corresponding optimal plans instead of the non-optimal conventional ones that an operator would follow. Highlights? Route planning for agricultural vehicles carrying time-depended loads. ? Evaluating potential risk for soil compaction imposed by field traffic. ? Integration of soil-sensitivity indicators and navigation systems.

Automatic detection of animals in mowing operations using thermal cameras.

During the last decades, high-efficiency farming equipment has been developed in the agricultural sector. This has also included efficiency improvement of moving techniques, which include increased working speeds and widths. Therefore, the risk of wild animals being accidentally injured or killed during routine farming operations has increased dramatically over the years. In particular, the nests of ground nesting bird species like grey partridge (Perdix perdix) or pheasant (Phasianus colchicus) are vulnerable to farming operations in their breeding habitat, whereas in mammals, the natural instinct of e.g., leverets of brown hare (Lepus europaeus) and fawns of roe deer (Capreolus capreolus) to lay low and still in the vegetation to avoid predators increase their risk of being killed or injured in farming operations. Various methods and approaches have been used to reduce wildlife mortality resulting from farming operations. However, since wildlife-friendly farming often results in lower efficiency, attempts have been made to develop automatic systems capable of detecting wild animals in the crop. Here we assessed the suitability of thermal imaging in combination with digital image processing to automatically detect a chicken (Gallus domesticus) and a rabbit (Oryctolagus cuniculus) in a grassland habitat. Throughout the different test scenarios, our study animals were detected with a high precision, although the most dense grass cover reduced the detection rate. We conclude that thermal imaging and digital imaging processing may be an important tool for the improvement of wildlife-friendly farming practices in the future.

Novel Wireless Sensor System for Monitoring Oxygen, Temperature and Respiration Rate of Horticultural Crops Post Harvest

In order to design optimal packages, it is of pivotal importance to determine the rate at which harvested fresh fruits and vegetables consume oxygen. The respiration rate of oxygen (RRO2) is determined by measuring the consumed oxygen per hour per kg plant material, and the rate is highly influenced by temperature and gas composition. Traditionally, RRO2 has been determined at discrete time intervals. In this study, wireless sensor networks (WSNs) were used to determine RRO2 continuously in plant material (fresh cut broccoli florets) at 5 °C, 10 °C and 20 °C and at modified gas compositions (decreasing oxygen and increasing carbon dioxide levels). Furthermore, the WSN enabled concomitant determination of oxygen and temperature in the very close vicinity of the plant material. This information proved a very close relationship between changes in temperature and respiration rate. The applied WSNs were unable to determine oxygen levels lower than 5% and carbon dioxide was not determined. Despite these drawbacks in relation to respiration analysis, the WSNs offer a new possibility to do continuous measurement of RRO2 in post harvest research, thereby investigating the close relation between temperature and RRO2. The conclusions are that WSNs have the potential to be used as a monitor of RRO2 of plant material after harvest, during storage and packaging, thereby leading to optimized consumer products.

A Vocal-Based Analytical Method for Goose Behaviour Recognition

Since human-wildlife conflicts are increasing, the development of cost-effective methods for reducing damage or conflict levels is important in wildlife management. A wide range of devices to detect and deter animals causing conflict are used for this purpose, although their effectiveness is often highly variable, due to habituation to disruptive or disturbing stimuli. Automated recognition of behaviours could form a critical component of a system capable of altering the disruptive stimuli to avoid this. In this paper we present a novel method to automatically recognise goose behaviour based on vocalisations from flocks of free-living barnacle geese (Branta leucopsis). The geese were observed and recorded in a natural environment, using a shielded shotgun microphone. The classification used Support Vector Machines (SVMs), which had been trained with labeled data. Greenwood Function Cepstral Coefficients (GFCC) were used as features for the pattern recognition algorithm, as they can be adjusted to the hearing capabilities of different species. Three behaviours are classified based in this approach, and the method achieves a good recognition of foraging behaviour (86–97% sensitivity, 89–98% precision) and a reasonable recognition of flushing (79–86%, 66–80%) and landing behaviour(73–91%, 79–92%). The Support Vector Machine has proven to be a robust classifier for this kind of classification, as generality and non-linear capabilities are important. We conclude that vocalisations can be used to automatically detect behaviour of conflict wildlife species, and as such, may be used as an integrated part of a wildlife management system.

Field robotics in sports: automatic generation of guidance lines for automatic grass cutting, striping and pitch marking of football playing fields

Progress is constantly being made and new applications are constantly coming out in the area of field robotics. In this paper, a promising application of field robotics in football playing fields is introduced. An algorithmic approach for generating the way points required for the guidance of a GPS-based field robotic through a football playing field to automatically carry out periodical tasks such as cutting the grass field, pitch and line marking illustrations and lawn striping is represented. The manual operation of these tasks requires very skilful personnel able to work for long hours with very high concentration for the football yard to be compatible with standards of Federation Internationale de Football Association (FIFA). In the other side, a GPS-based guided vehicle or robot with three implements; grass mower, lawn stripping roller and track marking illustrator is capable of working 24 h a day, in most weather and in harsh soil conditions without loss of quality. The proposed approach for the automatic operation of football playing fields requires no or very limited human intervention and therefore it saves numerous working hours and free a worker to focus on other tasks. An economic feasibility study showed that the proposed method is economically superimposing the current manual practices.

The effect on wireless sensor communication when deployed in biomass.

Wireless sensor networks (WSN) have been studied in a variety of scenarios over recent years, but work has almost exclusively been done using air as the transmission media. In this article some of the challenges of deploying a WSN in a heterogeneous biomass, in this case silage, is handled. The dielectric constant of silage is measured using an open-ended coaxial probe. Results were successfully obtained in the frequency range from 400 MHz to 4 GHz, but large variations suggested that a larger probe should be used for more stable results. Furthermore, the detuning of helix and loop antennas and the transmission loss of the two types of antennas embedded in silage was measured. It was found that the loop antenna suffered less from detuning but was worse when transmitting. Lastly, it is suggested that taking the dielectric properties of silage into account during hardware development could result in much better achievable communication range.

A diagnostic system for improving biomass quality based on a sensor network.

Losses during storage of biomass are the main parameter that defines the profitability of using preserved biomass as feed for animal husbandry. In order to minimize storage losses, potential changes in specific physicochemical properties must be identified to subsequently act as indicators of silage decomposition and form the basis for preventive measures. This study presents a framework for a diagnostic system capable of detecting potential changes in specific physicochemical properties, i.e., temperature and the oxygen content, during the biomass storage process. The diagnostic system comprises a monitoring tool based on a wireless sensors network and a prediction tool based on a validated computation fluid dynamics model. It is shown that the system can provide the manager (end-user) with continuously updated information about specific biomass quality parameters. The system encompasses graphical visualization of the information to the end-user as a first step and, as a second step, the system identifies alerts depicting real differences between actual and predicted values of the monitored properties. The perspective is that this diagnostic system will provide managers with a solid basis for necessary preventive measures.

Detection of Bird Nests during Mechanical Weeding by Incremental Background Modeling and Visual Saliency

Mechanical weeding is an important tool in organic farming. However, the use of mechanical weeding in conventional agriculture is increasing, due to public demands to lower the use of pesticides and an increased number of pesticide-resistant weeds. Ground nesting birds are highly susceptible to farming operations, like mechanical weeding, which may destroy the nests and reduce the survival of chicks and incubating females. This problem has limited focus within agricultural engineering. However, when the number of machines increases, destruction of nests will have an impact on various species. It is therefore necessary to explore and develop new technology in order to avoid these negative ethical consequences. This paper presents a vision-based approach to automated ground nest detection. The algorithm is based on the fusion of visual saliency, which mimics human attention, and incremental background modeling, which enables foreground detection with moving cameras. The algorithm achieves a good detection rate, as it detects 28 of 30 nests at an average distance of 3.8 m, with a true positive rate of 0.75.

Development of a Driverless Lawn Mower Using Co-simulation

This work examines the use of co-simulation in the development and optimisation of a steering system for a driverless industrial size lawn mower. Initial models of the kinematics, dynamics and steering control system are co-simulated to investigate the performance of the controller in a virtual setting. The co-simulation consists of a Continuous-Time (CT) model of the lawn mower kinematics and dynamics and a Discrete-Event (DE) model of the steering controller modelled in VDM-RT. The models are co-simulated by the use of the Co-simulation Orchestration Engine which is a core tool of the INTO-CPS project. The CT model of the lawn mower is calibrated and verified experimentally. The result of co-simulation is in a similar fashion verified by comparing the simulated and measured trajectories.

Sensor and control for consistent seed drill coulter depth

The novel position system detected the high-frequency drill coulter depth vibrations.By coulter down pressure control, the low-frequency depth variations were minimised.The system provided a mean depth deviation from the desired coulter depth of ź1.2mm.A three-position control system was found to be the best, cost-efficient solution. The consistent depth positioning of seeds is vital for achieving the optimum yield of agricultural crops. In state-of-the-art seeding machines, the depth of drill coulters will vary with changes in soil resistance. This paper presents the retrofitting of an angle sensor to the pivoting point of a drill coulter, providing sensor feedback to a control system that via an electro-hydraulic actuator delivers a constant coulter depth. The results showed a strong correlation between the angle of the coulter and the coulter depth under static (R2=1.00) and dynamic (R2=0.99) operations, verified by a sub-millimetre accurate positioning system (iGPS, Nikon Metrology NV, Belgium) mounted on the drill coulter. At a drill coulter depth of 55mm and controlled by an ordinary fixed spring loaded down force, the change in soil resistance reduced the mean depth by 23mm. By dynamically controlling the spring loaded down force based on the angle sensor, the mean depth was independent of the seedbed resistance change as shown from tests in soils ranging from sand to gravel. The PID controller was most effective because it providing a mean depth deviation from the target depth of -0.17mm and +0.08mm for sand and gravel, respectively. The most cost efficient control function was found to be the three-position control system, resulting in a mean depth deviation from the target depth of -0.89mm and -1.18mm for sand and gravel, respectively. A Fast Fourier Transform (FFT) analysis of the coulter depth measurements showed that the control system also provided a damping effect on the coulter depth variations. The research showed that it is possible to minimise the low-frequency drill coulter depth variations and provide a consistent coulter depth independent of soil conditions by using the developed sensor system and control system.