The Effect of Partial Root Drying on Antioxidant Activity in Different Agricultural Crops

Partial root drying (PRD) is a new irrigation strategy which applies alternating regimes of irrigation to half the root system while the other half dries out. Many published results showed that PRD may save water without significant effect on yield. The aim of this work was to compare the effects of PRD with full irrigation (FI) on yield and antioxidant activity in grape berry, potato tuber and tomato fruit. in both experimental conditions (field and polytunnel), the soil water content in FI treatment was kept close to field capacity, although in PRD treatment, 70o/o of the irrigation water in FI was uppti"a to one half of the root system, and irrigation was shifted according to soil water content decrease in the dry side of the root zone. At the end of the vegetation season, analyses of total yield of fruit and tubers and their quality were carried out. Antioxidant activity of tomato fruit ethanolic extract was evaluated against 2,2'-azinobis (3-eyhylbenzothiazoline-6-sulfonic acid) radical cation (ABTS'*) and expressed as Trolox (6-hydroxy-2,5,7,8-tetramethylchtoman-2- carboxylic acid) equivalent antioxidant capacity (TEAC)' In general, treatment differences in yield were not significant for either crop although WUE and anti,oxidant activity in the PRD treatments were higher than in the FI treatment for investigated crops. These results for all PRD-treated crops showed that PRD could be a useful strategy to save irrigation water without significantly sacrificing either the quantity or quality of yield

Activity recognition from sparsely labeled data using multi-instance learning

Activity recognition has attracted increasing attention in recent years due to its potential to enable a number of compelling contextaware applications. As most approaches rely on supervised learning methods, obtaining substantial amounts of labeled data is often an important bottle-neck for these approaches. In this paper, we present and explore a novel method for activity recognition from sparsely labeled data. The method is based on multi-instance learning allowing to significantly reduce the required level of supervision. In particular we propose several novel extensions of multi-instance learning to support different annotation strategies. The validity of the approach is demonstrated on two public datasets for three different labeling scenarios

Multi-graph based semi-supervised learning for activity recognition

On-body sensing has enabled scalable and unobtrusive activity recognition for context-aware wearable computing. Common methods for activity recognition are based on supervised learning requiring substantial amounts of labeled training data. Obtaining accurate and detailed annotations of activities is a great challenge for these approaches preventing their applicability in real-world settings. This paper introduces a new activity recognition method that combines small amounts of labeled data with easily obtainable unlabeled data in a semi-supervised learning process. The method propagates information through a graph that contains both labeled and unlabeled data. We propose two different ways of combining multiple graphs based on feature similarity and time. We evaluate both the quality of the label propagation process itself and the performance of classifiers trained on the propagated labels. Experimental results on two public datasets indicate that our approach outperfo rms a recently proposed multi-instance learning approach and in some cases even outperforms fully supervised approaches

Exploring Semi-Supervised and Active Learning for Activity Recognition

In recent years research on human activity recognition using wearable sensors has enabled to achieve impressive results on real-world data. However, the most successful activity recognition algorithms require substantial amounts of labeled training data. The generation of this data is not only tedious and error prone but also limits the applicability and scalability of today's approaches. This paper explores and systematically analyzes two different techniques to significantly reduce the required amount of labeled training data. The first technique is based on semi-supervised learning and uses self-training and co-training. The second technique is inspired by active learning. In this approach the system actively asks which data the user should label. With both techniques, the required amount of training data can be reduced significantly while obtaining similar and sometimes even better performance than standard supervised techniques. The experiments are conducted using one of the largest and richest currently available datasets

A statistical-relational activity recognition framework for ambient assisted living systems

Smart environments with ubiquitous sensing technologies are a promising perspective for reliable and continuous healthcare systems with reduced costs. A primary challenge for such assisted living systems is the automated recognition of everyday activities carried out by humans in their own home. In this work, we investigate the use of Markov Logic Networks as a framework for activity recognition within intelligent home-like environments equipped with pervasive light-weight sensor technologies. In particular, we explore the ability of MLNs to capture temporal relations and background knowledge for improving the recognition performance