A5101852230- Smart Agriculture and AI
- Remote Sensing in Agriculture
- Horticultural and Viticultural Research
- Spectroscopy and Chemometric Analyses
- Leaf Properties and Growth Measurement
- Plant Water Relations and Carbon Dynamics
- Remote Sensing and LiDAR Applications
- Wheat and Barley Genetics and Pathology
- Advanced SAR Imaging Techniques
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Soil Moisture and Remote Sensing
- Soil erosion and sediment transport
- Plant Pathogens and Fungal Diseases
- Plant Physiology and Cultivation Studies
- Water resources management and optimization
- Remote Sensing and Land Use
- Plant Disease Resistance and Genetics
- Genetic Mapping and Diversity in Plants and Animals
- Water Quality Monitoring Technologies
- Mobile and Web Applications
- Fungal Plant Pathogen Control
- IoT-based Smart Home Systems
- Land Use and Ecosystem Services
- Botanical Research and Applications
- Plant Disease Management Techniques
Northwest A&F University
2015-2024
Ministry of Agriculture and Rural Affairs
2018-2024
Agriculture and Forestry University
2020
Hokkaido University
2010
Wageningen University & Research
2005
Agriculture is facing severe challenges from crop stresses, threatening its sustainable development and food security. This article exploits aerial visual perception for yellow rust disease monitoring, which seamlessly integrates state-of-the-art techniques algorithms, including unmanned vehicle sensing, multispectral imaging, vegetation segmentation, deep learning U-Net. A field experiment designed by infecting winter wheat with inoculum, on top of images are captured DJI Matrice 100...
Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on yield and quality, posing serious threats food security. Disease severity grading plays a paramount role in disease management including breeding disease-resistant varieties. Manual inspection time-consuming, labor-intensive prone to human errors, therefore, there clearly urgent need develop more effective efficient strategy by using automated approaches. However, differences between...
Disease and pest are the main factors causing grape yield reduction. Correct timely identification of these symptoms necessary for vineyard. However, commonly used CNN models limit their performance on leaf images with complex backgrounds, due to lack global receptive field. In this article, we propose an effective accurate approach based Ghost-convolution Transformer networks diagnosing in First, a disease dataset containing 11 classes 12,615 images, namely GLDP12k is collected. Ghost...
Crop-type classification is one of the most significant applications in polarimetric synthetic aperture radar (PolSAR) imagery. As a remote sensing technique, PolSAR has been proved to have ability provide high-resolution information illustrated objects. However, single-temporal data are restricted sufficient for crop identification due complicated condition varying morphology within various growing stages. With an increasing number spaceborne systems launched, large amount real being...
Disease and pest detection of grape foliage is essential for yield quality. RGB image (RGBI), multispectral (MSI), thermal infrared (TIRI) are widely used in the health plants. In this study, we collected three types images with six common classes (anthracnose, downy mildew, leafhopper, mites, viral disease, healthy) field. ShuffleNet V2 was to build up models. According accuracy RGBI, MSI, TIRI, multi-source data concatenation (MDC) models, a fusion (MDF) decision-making method proposed...
High throughput phenotyping (HTP) for wheat (Triticum aestivum L.) stay green (SG) is expected in field breeding as SG a beneficial phenotype high yield and environment adaptability. The RGB multispectral imaging based on the unmanned aerial vehicle (UAV) are widely popular multi-purpose HTP platforms crops field. purpose of this study was to compare potential UAV images (MSI) diversified germplasm. multi-temporal 450 samples (406 genotypes) were obtained color indices (CIs) from MSI...
Accurately detecting and segmenting grape cluster in the field is fundamental for precision viticulture. In this paper, a new backbone network, ResNet50-FPN-ED, was proposed to improve Mask R-CNN instance segmentation so that detection performance can be improved under complex environments, shape variations, leaf shading, trunk occlusion, grapes overlapping. An Efficient Channel Attention (ECA) mechanism first introduced network correct extracted features better detection. To obtain detailed...
Stay-green (SG) in wheat is a beneficial trait that increases yield and stress tolerance. However, conventional phenotyping techniques limited the understanding of its genetic basis. Spectral indices (SIs) as non-destructive tools to evaluate crop temporal senescence provide an alternative strategy. Here, we applied SIs monitor dynamics 565 diverse accessions from anthesis maturation stages over 2 field seasons. Four (normalized difference vegetation index, green normalized red edge...
Abstract: Accurate data acquisition and analysis to obtain crop canopy information are critical steps understand plant growth dynamics assess the potential impacts of biotic or abiotic stresses on development. A versatile easy use monitoring system will allow researchers growers improve follow-up management strategies within farms once problems have been detected. This study reviewed existing remote sensing platforms relevant applied crops specifically grapevines equip a simple Unmanned...
To locate and identify weeds in a wheat field efficiently, an unmanned aerial vehicle (UAV) based imaging method was developed this study. A weed detection model on image data through deep learning implemented. The uses the YOLOV3-tiny network to detect pixel coordinates of images. It acquires position by converting geodetic coordinates. identified were marked prescription map. algorithm implemented tested using commercial DJI Phantom 3 UAV. This study performance YOLOV3 found that more...
With the continuous expansion of wine grape planting areas, mechanization and intelligence harvesting have gradually become future development trend. In order to guide picking robot pick grapes more efficiently in vineyard, this study proposed a bunches segmentation method based on Pyramid Scene Parsing Network (PSPNet) deep semantic network for different varieties natural field environments. To end, Convolutional Block Attention Module (CBAM) attention mechanism atrous convolution were...
The identification of Chinese medicinal plants was conducted to rely on ampelographic manual assessment by experts. More recently, machine learning algorithms for pattern recognition have been successfully applied leaf in other plant species. These new tools make the classification easier, more efficient and cost effective. This study showed comparative results between models obtained from two methods: i) a morpho-colorimetric method ii) visible (VIS)/Near Infrared (NIR) spectral analysis...
Traditional vine variety identification methods usually rely on the sampling of leaves followed by physical, physiological, biochemical and molecular measurement, which are destructive, time-consuming, labor-intensive require experienced grape phenotype analysts. To mitigate these problems, this study aimed to develop an application (App) running Android client identify wine automatically in real-time, can help growers quickly obtain information. Experimental results showed that all...
The eastern foothills of Helan Mountain are a production area high-quality wine grapes, but the low content water-soluble calcium in alkaline soil this has become an important limiting factor for high-end wines. In study, 7-year-old Cabernet Sauvignon grapes grown at Lilan Winery, which is located foot Ningxia, China, were used to examine effect exogenous supplementation on fruit growth and berry quality. Calcium sugar alcohol was applied as foliar spray 1.2 L/hm2 (T1), 2.4 (T2), 3.6 (T3),...
Information about canopy vigor and growth are critical to assess the potential impacts of biotic or abiotic stresses on plant development. By implementing a Digital Surface Model (DSM) imagery obtained using Unmanned Aerial Vehicles (UAV), it is possible filter information effectively based height, which provides an efficient method discriminate from soil lower vegetation such as weeds cover crops. This paper describes DSM (CG) well missing plants kiwifruit orchard plant-by-plant scale. The...
Information about canopy vigor and growth are critical to assess the potential impacts of biotic or abiotic stresses on plant development. By implementing a Digital Surface Model (DSM) imagery obtained using Unmanned Aerial Vehicles (UAV), it is possible filter information effectively based height, which provides an efficient method discriminate from soil lower vegetation such as weeds cover crops. This paper describes DSM (CG) well missing plants kiwifruit orchard plant-by-plant scale. The...