Maximum Data Generation Rate Routing Protocol Based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

For rechargeable wireless sensor networks,limited energy storage capacity,dynamic energy supply,low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario.Therefore,before data delivery,a sensor has to update its waking schedule continuously and share them to its neighbors,which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets.In this work,we propose the maximum data generation rate routing protocol based on data flow controlling technology.For a sensor,it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors.Hence,the energy consumption for time synchronization,location information and waking schedule shared will be reduced significantly.The saving energy can be used for improving data collection rate.Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.

A Multiscale Point-Supervised Network for Counting Maize Tassels in the Wild

Accurate counting of maize tassels is essential for monitoring crop growth and estimating crop yield.Recently,deep-learning-based object detection methods have been used for this purpose,where plant counts are estimated from the number of bounding boxes detected.However,these methods suffer from 2 issues:(a)The scales of maize tassels vary because of image capture from varying distances and crop growth stage;and(b)tassel areas tend to be affected by occlusions or complex backgrounds,making the detection inefficient.In this paper,we propose a multiscale lite attention enhancement network(MLAENet)that uses only point-level annotations(i.e.,objects labeled with points)to count maize tassels in the wild.Specifically,the proposed method includes a new multicolumn lite feature extraction module that generates a scale-dependent density map by exploiting multiple dilated convolutions with different rates,capturing rich contextual information at different scales more effectively.In addition,a multifeature enhancement module that integrates an attention strategy is proposed to enable the model to distinguish between tassel areas and their complex backgrounds.Finally,a new up-sampling module,UP-Block,is designed to improve the quality of the estimated density map by automatically suppressing the gridding effect during the up-sampling process.Extensive experiments on 2 publicly available tassel-counting datasets,maize tassels counting and maize tassels counting from unmanned aerial vehicle,demonstrate that the proposed MLAENet achieves marked advantages in counting accuracy and inference speed compared to state-of-the-art methods.The model is publicly available at.

From Laboratory to Field:Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild

Plant disease recognition is of vital importance to monitor plant development and predicting crop production.However,due to data degradation caused by different conditions of image acquisition,e.g.,laboratory vs.field environment,machine learning-based recognition models generated within a specific dataset(source domain)tend to lose their validity when generalized to a novel dataset(target domain).To this end,domain adaptation methods can be leveraged for the recognition by learning invariant representations across domains.In this paper,we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via uncertainty regularization,namely,Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification(MSUN).Our simple but effective MSUN makes a breakthrough in plant disease recognition in the wild by using a large amount of unlabeled data and via nonadversarial training.Specifically,MSUN comprises multirepresentation,subdomain adaptation modules and auxiliary uncertainty regularization.The multirepresentation module enables MSUN to learn the overall structure of features and also focus on capturing more details by using the multiple representations of the source domain.This effectively alleviates the problem of large interdomain discrepancy.Subdomain adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass variation.Finally,the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain transfer.MSUN was experimentally validated to achieve optimal results on the PlantDoc,Plant-Pathology,Corn-Leaf-Diseases,and Tomato-Leaf-Diseases datasets,with accuracies of 56.06%,72.31%,96.78%,and 50.58%,respectively,surpassing other state-of-the-art domain adaptation techniques considerably.