| 1. |
- Arvidsson, H., et al.
(författare)
-
Easily Applicable Methods for Measuring the Mental Load on Tractor Operators
- 2020
-
Ingår i: Journal of Agricultural Safety and Health. - : American Society of Agricultural & Biological Engineers. - 1074-7583 .- 1943-7846. ; 26:1, s. 5-14
-
Tidskriftsartikel (refereegranskat)abstract
- Agriculture technology is moving toward automation, placing operators in a supervisory role. This change in operator workload may lead to increased stress and higher mental load, resulting in reduced attention and hence greater risk of illness or injury to humans and damage to equipment. This study investigated the use of easily applicable equipment to measure mental load. Three methods were used to measure the mental load on machine operators: heart rate monitoring, two types of electroencephalograph (EEG) evaluation, and an assessment protocol. Three driving exercises (general driving, slalom driving, and loading) and a counting exercise were used in a driving simulator to create different levels of mental load. Due to the number of exercises, a single-scale assessment protocol was used to save time. We found that only the assessment protocol gave clear results and would work well as an evaluation tool. The heart rate and EEG measurements did not provide clear data for mental load assessment.
|
|
| 2. |
- Hammar, T., et al.
(författare)
-
Life cycle assessment of climate impact of bioenergy from a landscape
- 2017
-
Ingår i: European Biomass Conference and Exhibition Proceedings 2017. - : ETA-Florence Renewable Energies. ; , s. 1493-1497
-
Konferensbidrag (refereegranskat)abstract
- Bioenergy is a renewable energy source that can replace fossil energy sources in order to decrease greenhouse gas emissions. Assessing the climate impact of bioenergy systems involves methodological choices that may influence the result. Choice of climate metric is one example that has been discussed in several papers recently, and choice of spatial scale is another factor that can impact the results. In this paper, different types of spatial scales (stand, theoretical landscape and real landscape) were used for assessing the time-dependent climate impact of bioenergy from short-rotation coppice willow and stumps harvested from conventional forests in Sweden. The result showed that the spatial scale has importance for the climate impact, especially for long-rotation forestry. However, the climate impact of both types of bioenergy systems was lower than for fossil coal over time, independently of spatial scale used. A landscape perspective was considered to be most relevant from a climate policy perspective.
|
|
| 3. |
|
|
| 4. |
- Hamid Muhammed, Hamed, et al.
(författare)
-
Feature vector based analysis of hyperspectral crop reflectance data for discrimination and quantification of fungal disease severity in wheat
- 2003
-
Ingår i: Biosystems Engineering. - 1537-5110 .- 1537-5129. ; 86:2, s. 125-134
-
Tidskriftsartikel (refereegranskat)abstract
- The impact of plant pathological stress on crop reflectance can be measured both in broad-band vegetation indices and in narrow or local characteristics of the reflectance spectra. This work is concerned with using the whole spectra in the objective examination of how different parts of the spectrum contribute in describing disease severity in wheat. A hyperspectral reflectance spectrum was considered as a mixed signal, i.e. the integration of the effects of all active objects in the investigated area. Independent component analysis (ICA) was used to blindly separate mixed statistically independent signals. Principal component analysis (PCA) was also used to extract interesting components. The ICA or PCA results had then to be interpreted efficiently. This was achieved by using a technique called feature-vector-based analysis (FVBA), which produces a number of 'component-feature vector' pairs, which represent the spectral signatures and the corresponding weighting coefficients of the different constituting source signals. These weighting coefficients were proportional to field assessments of fungal disease severity in a spring wheat crop, in percentage necrosis of leaf area, and high correlations were shown. Two effects of increased disease severity were observed: (1) a flattening of the green reflectance peak together with a general decrease in reflectance in the near-infrared region and (2) a decrease of the shoulder of the near-infrared reflectance plateau together with a general increase in the visible region between 550 and 750 nm.
|
|
| 5. |
|
|
| 6. |
- LarsOlle, Anders, et al.
(författare)
-
A multi-criteria decision support model for optimal stump harvesting
- 2012
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- A multi-criteria decision support model for optimal stump harvesting Sweden was developed. The model quantifies the effect of harvesting each individual stump over a harvesting object in four criteria's: - Biodiversity (Biodiversity value index) - Economy (SEK) - Greenhouse gas emissions (CO2) - Soil and water (Soil and water preservation index) The four criteria's are sometimes in conflict to each other, and uses values that are not directly comparable. The intended use for this model is to contribute with the objective evaluation of all four criteria's in the decision in what stumps to harvest and what stumps to leave in the harvesting object. The model uses individual stump data (e.g. position, tree species and stump biomass) and harvesting object GIS data (roads, elevation map, soil map, terrain map). Primary data on individual stumps comes from the logging system in the stem harvesters: GPS and operator classification. Such data are routinely collected in harvesters. Official map data for the harvesting object are available from the Swedish mapping, cadastral and land registration authority (Lantmäteriet). This includes the topographic map and elevation maps data in 2 m resolution. Also, GIS data are collected in the inspections before harvesting the stems. The biodiversity sub-model considers different types of wood-dependent organisms (lichens, mosses, insects and fungi) in terms of their habitat requirements, vulnerability, sun exposure preferences, locality, etc. A panel of external experts has drawn up a grading scale of stump values for the different taxonomic groups. The proximity to key habitats and exposure to sunlight are derived from a spatial model. In the economic sub-model the potential net return from each stump is calculated based on estimated revenue from harvested stump biomass and the costs of stump harvesting and transport (based on cost functions and GIS calculations of transport distances). An energy and climate sub-model incorporates greenhouse gas (GHG) emissions from forest operations and the effect of advancing GHG emissions when stump biomass is incinerated instead of being left to decompose. Soil and water issues are handled within a sub-model estimating the consequences for long-term soil fertility (nutrient cycling and soil compaction) and water (leaching of plant nutrients and mercury, and particle transport due to soil damage by heavy machinery). Each criteria is evaluated in totally four sub-models. To be able to compare the resulting value from each of the criteria, a harvesting index from 0 to 1 is calculated for each stump. The value 0 represents ‘Not at all suitable for harvest’ and 1 ‘Highly suitable for harvest’. Through this, a stump of high biodiversity value is assigned a low harvesting index in the biodiversity sub-model and a large, easily accessible stump is assigned a high harvesting index in the economic sub-model. When calculating the total net index, the harvesting index from each criteria has to be weighed together using one coefficient for each criteria. The weighing coefficient for each criteria is chosen according to the preferences of the decision maker. The tool offers the end-user possibilities to prioritise and plan for cost-effective stump harvesting, while minimising negative environmental impacts.
|
|
| 7. |
|
|
| 8. |
- Larsolle, A., et al.
(författare)
-
Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density
- 2007
-
Ingår i: Precision Agriculture. - : Springer Science and Business Media LLC. - 1385-2256 .- 1573-1618. ; 8:1-2, s. 37-47
-
Tidskriftsartikel (refereegranskat)abstract
- Using spectral reflectance to estimate crop status is a method suitable for developing sensors for site-specific agricultural applications. When developing spectral analysis methods, it is important to know the influence of different crop parameters on the spectral reflectance profile. The objective of this report was to present and evaluate a multivariate method for objective hyperspectral analysis in the examination of how different parts of the reflectance spectrum are affected by disease severity and above ground plant density. Data from two field experiments were used; fungal disease severity assessments in wheat 1998 and above ground plant density measurements 2003. The analysis method consisted of two steps: a preprocessing step where the data was normalized and a classification step for estimating the crop variable. Using only 12% of the data as training data, the method resulted in coefficients of determination (R-2) of 94.3% for the disease severity data and 96.9% for the plant density data. The hyperspectral analysis method presented could also be used to extract spectral signatures of disease severity and plant density using the experimental data. In general, two types of spectral signatures for both data sets, with respect to increasing disease severity and decreasing plant density, were observed (1) a flattening of the green reflectance peak together with a general decrease in reflectance in the near infrared region and, (2) a decrease of the shoulder of the near infrared reflectance plateau together with a general increase in the visible region between 550 and 750 nm.
|
|
