A5073768392- Smart Agriculture and AI
- Plant Disease Management Techniques
- Date Palm Research Studies
- Plant Pathogens and Fungal Diseases
- Spectroscopy and Chemometric Analyses
- Horticultural and Viticultural Research
- Advanced Chemical Sensor Technologies
- Plant Virus Research Studies
- Plant Surface Properties and Treatments
- COVID-19 diagnosis using AI
- Diverse Educational Innovations Studies
- IoT Networks and Protocols
- Political Influence and Corporate Strategies
- Orthopaedic implants and arthroplasty
- Power Line Communications and Noise
- Dental Research and COVID-19
- Manufacturing Process and Optimization
- International Business and FDI
- Animal and Plant Science Education
- Plant Toxicity and Pharmacological Properties
- Remote Sensing and Land Use
- Particle Dynamics in Fluid Flows
- Plant and fungal interactions
- Food Supply Chain Traceability
- Hip disorders and treatments
Nanjing Forestry University
2013-2025
Peking University
2025
Shandong Academy of Agricultural Sciences
2022-2025
Gansu Agricultural University
2025
Wuhu Hit Robot Technology Research Institute
2024
Zhejiang University
2014
General Hospital of Guangzhou Military Command
2012
Weed identification in vegetable plantation is more challenging than crop weed due to their random plant spacing. So far, little work has been found on identifying weeds plantation. Traditional methods of used be mainly focused directly; however, there a large variation species. This paper proposes new method contrary way, which combines deep learning and image processing technology. Firstly, trained CenterNet model was detect vegetables draw bounding boxes around them. Afterwards, the...
Precision weed control in vegetable fields can substantially reduce the required inputs. Rapid and accurate detection is a challenging task due to presence of wide variety species at various growth stages densities. This paper presents novel deep-learning-based method for that recognizes crops classifies all other green objects as weeds.The optimal confidence threshold values YOLO-v3, CenterNet, Faster R-CNN were 0.4, 0.6, 0.4/0.5, respectively. These deep-learning models had average...
In-field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds close proximity with crop. The objective this research was evaluate feasibility using deep convolutional neural networks for detecting broadleaf seedlings growing wheat.The object networks, including CenterNet, Faster R-CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient because recall never exceeded 0.58 testing dataset. image classification AlexNet, DenseNet,...
Abstract BACKGROUND Accurate detection of weeds and estimation their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used weed by analyzing information at the pixel or individual plant level, which requires a substantial amount annotated data training. This study aims to evaluate effectiveness using image‐classification neural networks (NNs) detecting estimating in bermudagrass turf. RESULTS Weed‐detection NNs, including...
Precision spraying of synthetic herbicides can reduce herbicide input. Previous research demonstrated the effectiveness using image classification neural networks for detecting weeds growing in turfgrass, but did not attempt to discriminate weed species and locate on input images. The objectives this were to: (i) investigate feasibility training deep learning models grid cells (subimages) detect location by identifying whether or contain weeds; (ii) evaluate DenseNet, EfficientNetV2, ResNet,...
Industrial defect segmentation is critical for manufacturing quality control. Due to the scarcity of training samples, few-shot semantic (FSS) holds significant value in this field. However, existing studies mostly apply FSS tackle defects on simple textures, without considering more diverse scenarios. This paper aims address gap by exploring broader industrial products with various types. To end, we contribute a new real-world dataset and reorganize some datasets build comprehensive (FDS)...
Abstract BACKGROUND Precision weed mapping in turf according to its susceptibility selective herbicides allows the smart sprayer spot‐spray most pertinent onto susceptible weeds. The objective of this study was evaluate feasibility implementing herbicide susceptibility‐based using deep convolutional neural networks (DCNNs) facilitate targeted and efficient applications. Additionally, applying path‐planning algorithms data guide spraying nozzle ensures minimal travel paths for application....
Continuous cropping obstacles pose significant constraints and urgent challenges in the production of Tussilago farfara L. This experiment investigated effects consecutive on T. over periods 1, 2, 3 years. It assessed yield quality flower buds, addition to physicochemical properties rhizosphere soil. The microbial community was analyzed through 16S rDNA ITS sequencing using Illumina Novaseq high-throughput technology, while also examining correlations among these factors. results reveal that...
Under complex climatic conditions, variable application parameters and a two-dimensional route, it is difficult to ensure the accurate deposition of pesticide droplets during helicopter aerial applications. This especially true when area forested. The Agricultural Dispersal (AGDISP) model was used with an optimization procedure study influence flight height, speed, ambient wind speed. Optimization techniques were obtain best fit between simulation results. Three objectives propose new...
This study focuses on multiple origins of green-back purple and dual-faced Perilla frutescens, employing field cultivation experiments combined with detection methods, such as HPLC, LC-MS, GC-MS, to compare the differences in yield, quality, metabolic products different colored P. frutescens. The results indicate that frutescens significantly outperformed terms leaf, stem, seed yields, while effective component contents leaves seeds are higher than those green An analysis anthocyanin...
Recent developed tea plucking machines mainly bases on the shearing principle which cut top of trees with no selectivity.Automatic flushes machinery is essential to harvest for high-quality green manufacture, as labor cost increases rapidly in recent years.This paper focuses researches a parallel robot high efficiency.Color features are extracted recognize natural conditions using machine vision.Fringe projection profilometry employed realize 3 D reconstruction and measurement trees.A...
<abstract> <bold>Abstract.</bold> Crop/weed recognition is a crucial step for selective herbicide application. A machine vision based sensing system was developed to detect intra-row weeds when crops were at their early growth stages. The proposed methods used color feature extract vegetation from the background, whilst height and plant spacing information analysis techniques applied discriminate between weeds. Firstly identification of that lower than done by height-based segmentation...
In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, AGVs struggle automatically navigate optimized routes. Additionally, rapid acceleration deceleration can pose safety hazards. This paper proposes velocity-adaptive model predictive control (MPC)-based path tracking method for AGVs. The movement forklift-type is categorized...
Tea flushes identification from their natural background is the first key step for intelligent tea-picking robot. This paper focuses on algorithms of identifying tea based color image analysis. A system was developed as a means guidance robotic manipulator in picking high-quality tea. Firstly, several indices, including y-c, y-m, (y-c)/(y+c) and (y-m)/(y+m) CMY space, S channel HSI U YUV were studied tested. These indices enhanced highlighted against background. Afterwards, grey level...
College student education and management can be enhanced through a data‐driven approach involving surveys, academic records, text analysis to understand interests concerns. Effective categorization of relevant topics enables universities provide tailored support educational content, thus improving the quality fostering success well‐being by adapting evolving needs aspirations. The primary contribution this work is demonstrating effectiveness semisupervised learning methods in content...