Search Results (2)

As an efficient approach to risk management in agriculture, the elimination of losses due to plant diseases and insect pests is one of the most important and urgent technological challenges for improving the crop yield. Therefore, we have developed a polydimethylsiloxane (PDMS)-based microfluidic device for the multiplex genetic diagnosis of plant diseases and pests. It offers unique features, such as rapid detection, portability, simplicity, and the low-cost genetic diagnosis of a wide variety of plant viruses. In this study, to realize such a diagnostic device, we developed a method for the autonomous dispensing of fluid into a microchamber array, which was integrated with a set of three passive stop valves with different burst pressures (referred to as phaseguides) to facilitate precise fluid handling. Additionally, we estimated the mixing efficiencies of several types of passive mixers (referred to as chaotic mixers), which were integrated into a microchannel, through experimental and computational analyses. We first demonstrated the ability of the fabricated diagnostic devices to detect DNA-based plant viruses from an infected tomato crop based on the loop-mediated isothermal amplification (LAMP) method. Moreover, we demonstrated the simultaneous detection of RNA-based plant viruses, which can infect cucurbits, by using the reverse transcription LAMP (RT-LAMP) method. The multiplex RT-LAMP assays revealed that multiple RNA viruses extracted from diseased cucumber leaves were successfully detected within 60 min, without any cross-contamination between reaction microchambers, on our diagnostic device. Full article

Robust detection and recognition of water surfaces are critical for autonomous navigation of unmanned surface vehicles (USVs), since any none-water region is likely an obstacle posing a potential danger to the sailing vehicle. A novel water region visual detection method is proposed in this paper. First, the input image pixels are clustered into different regions and each pixel is assigned a label tag and a confidence value by adaptive multistage segmentation algorithm. Then the resulting label map and associated confidence map are fed into a convolutional neural network (CNN) as training samples to train the network online. Finally, the online trained CNN is used to segment the input image again but with greater precision and stronger robustness. Compared with other deep-learning image segmentation algorithms, the proposed method has two advantages. Firstly, it dispenses with the need of manual labeling training samples which is a costly and painful task. Secondly, it allows real-time online training for CNN, making the network adaptive to the navigational environment. Another contribution of this work relates to the training process of neuro network. An effective network training method is designed to learn from the imperfect training data. We present the experiments in the lake with a various scene and demonstrate that our proposed method could be applied to recognize the water region in the unknown navigation environment automatically. Full article