Inspection of rice seeds is a crucial task for plant nurseries and farmers since it ensures seed quality when growing seedlings. Conventionally, this process is performed by expert inspectors who manually screen large samples of rice seeds to identify their species and assess the cleanness of the batch. In the quest to automate the screening process through machine vision, a variety of approaches utilise appearance-based features extracted from RGB images while others utilise the spectral information acquired using Hyperspectral Imaging (HSI) systems. Most of the literature on this topic benchmarks the performance of new discrimination models using only a small number of species. Hence, it is unclear whether or not model performance variance confirms the effectiveness of proposed algorithms and features, or if it can be simply attributed to the inter-class/intra-class variations of the dataset itself. In this paper, a novel method to automatically screen and classify rice seed samples is proposed using a combination of spatial and spectral features, extracted from high resolution RGB and hyperspectral images. The proposed system is evaluated using a large dataset of 8,640 rice seeds sampled from a variety of 90 different species. The dataset is made publicly available to facilitate robust comparison and benchmarking of other existing and newly proposed techniques going forward. The proposed algorithm is evaluated on this large dataset and the experimental results show the effectiveness of the algorithm to eliminate impure species by combining spatial features extracted from high spatial resolution images and spectral features from hyperspectral data cubes. INDEX TERMS Hyperspectral imaging, rice seed variety, spatio-temporal feature fusion. JINCHANG REN (Senior Member, IEEE) received the B.E. degree in computer software, the M.Eng. degree in image processing, and the D.Eng. degree in computer vision from Northwestern Polytechnical University, Xi'an, China, and the Ph.D. degree in electronic imaging and media communication from the
Multipath routing in mobile ad-hoc networks allows the establishment of multiple paths for routing between a source-destination pair. It exploits the resource redundancy and diversity in the underlying network to provide benefits such as fault tolerance, load balancing, bandwidth aggregation and the improvement in quality-of-service metrics such as delay. Previous work shows that on-demand multipath routing schemes achieve better performance under certain scenarios with respect to a number of key performance metrics when compared with traditional single-path routing mechanisms. A multipath routing scheme, referred to as shortest multipath source (SMS) routing based on dynamic source routing (DSR) is proposed here. The mechanism has two novel aspects compared with other on-demand multipath routing schemes: it achieves shorter multiple partial-disjoint paths and allows more rapid recovery from route breaks. The performance differentials are investigated using NS-2 under conditions of varying mobility, offered load and network size. Results reveal that SMS provides a better solution than existing source-based approaches in a truly mobile ad-hoc environment
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