Presentations

P01: Deep Learning for Medicinal Plant Species Classification and Recognition: A Systematic Review

P02: The Phototactic Rhythm of Pests for the Solar Insecticidal Lamp: A Review

P03: Embracing Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learning

P04: TasselLFANet: A Novel Lightweight Multi-Branch Feature Aggregation Neural Network for High-throughput Image-based Maize Tassels Detection and Counting

P05: Early-Season and Refined Mapping of Winter Wheat based on Phenology Algorithms – A Case of Shandong, China

P06: Colorimetric Loop-Mediated Isothermal Amplification Assay for Detection and Ecological Monitoring of Sarocladium Qryzae, An Important Seed-Borne Pathogen of Rice

P07: Complete Area ϵ-Probability Coverage in Solar Insecticidal Lamps Internet of Things

P08: Security and Privacy in Solar Insecticidal Lamps Internet of Things: Requirements and Challenges

P09: A Partition-Based Mobile-Crowdsensing-Enabled Task Allocation for Solar Insecticidal Lamp Internet of Things Maintenance

P10: SILOS: An Intelligent Fault Detection Scheme for Solar Insecticidal Lamp Iot with Improved Energy Efficiency

P11: A Novel Accurate Insecticidal Counting Method Based on Solar Insecticidal Lamp using Machine Learning

P12: Target-Barrier Coverage Improvement in an Insecticidal Lamps Internet of UAVs