In the manual forestry, the worker carries his equipment to a tree by foot. There the tree is felt and processed. Depending on the surrounding, this needs more or less time. This paper automatically analyses the needed time for different activities. Therefore, the worker gets an estimate of his time spends on different tasks and can estimate his productivity. The approach uses therefor a mobile phone and a smartwatch. This work could also be used to conduct general time studies for time and equipment comparison. The focus is on the frequency analysis, feature creation and the obstacles, such as asynchrony of the recordings and labeling errors during the annotation of the ground truth, which are described and calculated.

Fish quality is primarily effected by the number of days elapsed since harvesting, while bad storage conditions can also lead to quality degradation similar to the impact time. Existing approaches require laboratory testing, a laborious and timeconsuming process. In this work, we investigate technologies for quantifying fish quality though the development of deep learning models for analyzing imagery of fish. We first demonstrate that such a quantification is possible, to a certain degree, from multispectral images provided a sufficient number of training examples is available. Given that, we explore how knowledge distillation can be utilized for achieving similar fish quality estimation accuracy, but instead of using high-end multispectral imaging systems, using off-the-shelf RGB cameras. Experimental evaluation on individuals from the Mullus Marbatus family demonstrates that the proposed methodology constitutes a valid approach.

Functional lighting can control a specific wavelength in order to emphasize a desired color signal of an object. In this study, for the purpose of designing functional lighting for cheese, the effect of lighting on the palatability of cheese was analyzed from reflected light. To investigate the palatability difference caused by different illuminants, a psychophysical experiment was conducted using five types of cheese under metameric lighting with fixed color temperature and illuminance. A total of eight observers participated in this experiment: four of them who loved eating cheese were classified as group A, and the remaining four who disliked eating it were classified as group B. The experiment revealed that observers in group A agreed that illumination sources made the cheese look most palatable, whereas observers in group B showed variability in their preferred light sources. Based on these results, guidelines for designing an illumination source that can improve cheese palatability by controlling the wavelength band were determined, under the constraint that the reflected light exists within a specific chrominance region.