Shichao Jin

Nanjing Agricultural University | NAU · Academy for Advanced Interdisciplinary Studies

The rapid development of light detection and ranging (Lidar) provides a promising way to obtain three-dimensional (3D) phenotype traits with its high ability of recording accurate 3D laser points. Recently, Lidar has been widely used to obtain phenotype data in the greenhouse and field with along other sensors. Individual maize segmentation is the...

Accurate and high throughput extraction of crop phenotypic traits, as a crucial step of molecular breeding, is of great importance for yield increasing. However, automatic stem-leaf segmentation as a prerequisite of many precise phenotypic trait extractions is still a big challenge. Current works focus on the study of the 2-D image-based segmentati...

Separating structural components is important but also challenging for plant phenotyping and precision agriculture. Light detection and ranging (LiDAR) technology can potentially overcome these difficulties by providing high quality data. However, there are difficulties in automatically classifying and segmenting components of interest. Deep learni...

Plant phenomics is a new avenue for linking plant genomics and environmental studies, thereby improving plant breeding and management. Remote sensing techniques have improved high-throughput plant phenotyping. However, the accuracy, efficiency, and applicability of three-dimensional (3D) phenotyping are still challenging, especially in field enviro...

The accurate plant organ segmentation is crucial and challenging to the quantification of plant architecture and selection of plant ideotype. The popularity of point cloud data and deep learning methods make plant organ segmentation a feasible and cutting-edge research. However, current plant organ segmentation methods are specially designed for on...

Accurate counting of cereals crops, e.g., maize, rice, sorghum, and wheat, is crucial for estimating grain production and ensuring food security. However, existing methods for counting cereal crops focus predominantly on building models for specific crop head; thus, they lack generalizability to different crop varieties. This paper presents Countin...

Three-dimensional (3D) phenotyping is important for studying plant structure and function. Light detection and ranging (LiDAR) has gained prominence in 3D plant phenotyping due to its ability to collect 3D point clouds. However, organ-level branch detection remains challenging due to small targets, sparse points, and low signal-to-noise ratios. In...

Automated guard cell detection and measurement are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Most current methods for measuring guard cells and stomata are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utili...

The real-time monitoring of wheat phenology variations among different varieties and their adaptive responses to environmental conditions is essential for advancing breeding efforts and improving cultivation management. Many remote sensing efforts have been made to relieve the challenges of key phenophase detection. However, existing solutions are...

Digital elevation models (DEMs) are crucial geographical data source whereas the resolution of commonly used DEM products is low and cannot meet requirement of some detailed geo-related applications. Deep learning-based methods have been demonstrated to be effective in super-resolution (SR) techniques, which reconstruct high-resolution (HR) images...

Exploring the relationship between spike phenotypes and wheat yield is crucial for selecting wheat ideotypes, but remains a subject of ongoing debate, primarily due to the lack of efficient spike phenotyping methods, particularly in field environments with complex light conditions. Light detection and ranging (lidar) can precisely capture three-dim...

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 b...

Plant stomata are essential channels for gas exchange between plants and the environment. The infrared gas-exchange system has greatly accelerated the studies of stomatal conductance (g s). Nevertheless, due to the lack of in-situ monitoring techniques, the behavior of stomata themselves remains poorly understood, especially in nocturnal environmen...

Automated stomata detection and measuring are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Current methods are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utilizing empirical and theoretical algorithms and co...

Canopy height (CH) is an important trait for crop breeding and production. The rapid development of 3D sensing technologies shed new light on high-throughput height measurement. However, a systematic comparison of the accuracy and heritability of different 3D sensing technologies is seriously lacking. Moreover, it is questionable whether the field-...

The three-dimensional (3D) structure of forests has long been recognized to have profound effects on forest ecosystems. However, the use of spectral and radar remotely sensed data for forest structure quantification is insensitive to changes in forest vertical structure. LiDAR has emerged as a robust means to measure forest structures. Numerous stu...

Vegetation community complexity is a critical factor influencing terrestrial ecosystem stability. China, the country leading the world in vegetation greening resulting from human activities, has experienced dramatic changes in vegetation community composition during the past 30 years. However, how China's vegetation community complexity varies spat...

Forested environments feature a highly complex radiation regime, and solar radiation is hindered from penetrating into the forest by the 3D canopy structure; hence, canopy shortwave radiation varies spatiotemporally, seasonally, and meteorologically, making the radiant flux challenging to both measure and model. Here, we developed a synergetic meth...

Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. However, the contributions of proximal sensing data characteristics (spectral, temporal, and spatial) to yield estimation have not been systematically evaluated. We collected long-term, hyper-temporal, and large-volume light detection...

Accurate estimates of forest aboveground biomass (AGB) are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets. However, combining the advantages of active and passive data sources to improve estimation accuracy rem...

Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly...

Wheat yield and grain protein content (GPC) are two main optimization targets for breeding and cultivation. Remote sensing provides nondestructive and early predictions of yield and GPC, respectively. However, whether it is possible to simultaneously predict yield and GPC in one model and the accuracy and influencing factors are still unclear. In t...

Regional landslide identification is important for the risk management of landslide hazards. The traditional methods of regional landslide identification were mainly conducted by a human being. In previous studies, automatic landslide recognition mainly focused on new landslides distinct from the environment induced by rainfall or earthquake, using...

无人机是低空领域准确、灵活、高效获取多种类型高分辨率遥感数据的重要载体，无人机遥感技术在行业应用创新和管理部门科学决策之间构筑起信息沟通的关键桥梁。随着科技的进步、大数据时代的来临，无人机遥感系统的硬件设备、信息提取方法都取得了飞速的发展；同时其在国民经济主要行业领域的应用也面临着前所未有的机遇和挑战。论文首先介绍了无人机遥感系统的硬件研发进展，并指出轻小型、高精度、标准化与集成化是未来无人机遥感系统发展的总体趋势。其次，详细介绍了目前轻小型无人机遥感应用在农业、林草业、电力、测绘、大气探测和地质灾害等行业的应用现状，指出实现无人机多源遥感数据获取、融合、分析和提取的综合平台是未来轻小型无人机在民用领域行业应用创新的关键所在。最后，针对载荷与飞行平台的一体化集成应用、无人机组网作业、海量数据...

Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage...

Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment. Terrestrial laser scanning (TLS) is a well-suited tool to study structural rhythm under field conditions. Recent studies have used TLS to describe the structural rhythm of trees, but no consistent patterns have been drawn....

Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability and the ability to analyse big data collected. Here, we present a large-scale phenotyping solution that combines a commercial b...

The advent of lidar has revolutionized the way we observe and measure vegetation structure from the ground and from above and represents a major advance toward the quantification of 3D ecological observations. Developments in lidar hardware systems and data processing algorithms have greatly improved the accessibility and ease of use of lidar obser...

The priority area of Taihang Mountains biodiversity conservation (Beijing-Tianjin-Hebei region) has a unique geographical location and provides significant ecosystem services. The conservation of vegetation diversity is particularly urgent because of its fragile ecosystem. Based on the reviews of previous studies and remote-sensing analyses, we con...

Vegetation maps serve as the key source information for ecological studies, biodiversity conservation, and vegetation management and restoration. The latest version of the Vegetation Map of China (1:1000000) was generated in the 1980s. Since then, the vegetation distribution pattern of China has changed dramatically during these 40 years. Classific...

Objectives: The knowledge of regional landslides detection plays a fundamental role in the landslide risk management. However, most of that recognition was taken manually in the past, which is rather time‑ and labor‑ consuming. As the development of technologies of remote sensing and artificial intelligence, the automatic detection of landslides be...

Space-earth integrated stereoscopic mapping promotes the progress of earth observation technologies. The method which combined remote sensing images with zenith perspectives and ground-level landscape photos with slanted viewing angles improves the efficiency and accuracy of land surveys. Recently, numerous efforts have been devoted to combining de...

Microfossils, tiny fossils whose study requires the use of a microscope, have been widely applied in many fields of earth, life, and environmental sciences. The abundance and high diversity of microfossils, as well as the need for rapid identification, call for automated methods to classify microfossils. In this study, we constructed an open datase...

Airborne laser scanning (ALS) data is one of the most commonly used data for terrain products generation. Filtering ground points is a prerequisite step for ALS data processing. Traditional filtering methods mainly use handcrafted features or predefined classification rules with pre-processing/post-processing operations to filter ground points iter...

Forest inventory holds an essential role in forest management and research, but the existing field inventory methods are highly time-consuming and labor-intensive. Here, we developed a simultaneous localization and mapping-based backpack light detection and ranging (LiDAR) system with dual orthogonal laser scanners and an open-source Python package...

Background: Precision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels is unknown and challenging due to the lack of acc...

生态资源是人类生存发展和自我实现的重要物质基础, 对其进行深入全面的研究和理解关系到人类社会的可持续发展. 随着观测技术的进步, 长时间、 跨尺度、 海量异构多源数据的获取能力得到了显著提升, 生态资源研究进入了数据驱动的新时代. 传统的统计学习和机器学习算法在海量数据面前存在饱和问题. 深度学习作为高维非线性复杂特征自动提取的新手段, 对海量数据具有不饱和性, 正成为学界和工业界数据处理的新引擎. 为推动深度学习在生态资源领域的应用, 文章首先介绍了深度学习的理论与生态资源研究的联系, 以及常用工具和 数据集. 其次, 通过物种识别、 作物育种和植被制图三个案例介绍了深度学习在分类识别、 检测定位、 语义分割和实例分割任务中的具体实践, 以及图神经网络在生态资源领域中典型的空间离散点数据上...

Vegetation maps are important sources of information for biodiversity conservation, ecological studies, vegetation management and restoration, and national strategic decision making. The current Vegetation Map of China (1:1000000) was generated by a team of more than 250 scientists in an effort that lasted over 20 years starting in the 1980s. Howev...

Ecological resources are an important material foundation for the survival, development, and self-realization of human beings. In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society. Advances in observation technology have improved the ability to acquire long-ter...

The capacity of canopy light interception is a key functional trait to distinguish the phenotypic variation over genotypes. High-throughput phenotyping canopy light interception in the field, therefore, would be of high interests for breeders to increase the efficiency of crop improvement. In this research, the Digital Plant Phenotyping Platform（D3...

Crown architecture is a critical component for a tree to interact with the ambient environment and to compete with neighbors. However, little is known regarding how climate variability may shape crown architecture traits across large geographical extents and whether crown architecture traits have coordinated variations with trunk and leaf traits to...

Aboveground biomass (AGB) is an important indicator for grassland ecosystem assessment, management and utilization. Remote sensing technologies have driven the development of grassland AGB estimation from labor-intensive to highly-efficient. However, optical image-based remote sensing methods are fraught with uncertainty issues due to the saturatio...

The emerging near-surface light detection and ranging (LiDAR) platforms [e.g., terrestrial, backpack, mobile, and unmanned aerial vehicle (UAV)] have shown great potential for forest inventory. However, different LiDAR platforms have limitations either in data coverage or in capturing undercanopy information. The fusion of multiplatform LiDAR data...

This study investigated the effects of forest type, leaf area index (LAI), canopy cover (CC), tree density (TD), and the coefficient of variation of tree height (CVTH) on the accuracy of different individual tree segmentation methods (i.e., canopy height model, pit-free canopy height model (PFCHM), point cloud, and layer stacking seed point) with L...

Airborne light detection and ranging is an emerging measurement tool for snowpack estimation, and data are now emerging to better assess multiscale snow depth patterns. We used airborne light detection and ranging measurements from four sites in the southern Sierra Nevada to determine how snow depth varies with canopy structure and the interactions...

Background Maize (Zea mays L.) is the third most consumed grain in the world and improving maize yield is of great importance of the world food security, especially under global climate change and more frequent severe droughts. Due to the limitation of phenotyping methods, most current studies only focused on the responses of phenotypes on certain...

表型是研究“基因型-表型-环境”作用机制的重要桥梁，研发具有自主知识产权的作物表型监测平台对于加速育种进程和辅助精准农业监测具有重要意义。Crop 3D表型监测系统以水稻和玉米等主要粮食作物为研究对象，实现了多尺度、多时序作物全生育期的生长动态监测，为育种提供了重要数据支撑。文章首先综述了国内外关于表型平台的研究进展，进而介绍了Crop 3D系统平台的主要研究进展，最后对未来表型研究的方向进行了展望。

Canopy height is an important forest structure parameter for understanding forest ecosystem and improving global carbon stock quantification accuracy. Light detection and ranging (LiDAR) can provide accurate canopy height measurements, but its application at large scales is limited. Using LiDAR-derived canopy height as ground truth to train the Ran...

It has been found that the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is systematically higher than the actual land surface in vegetated areas. This study developed a new globally corrected SRTM DEM through reducing its systematic bias in vegetated areas. Over 150, 000 km² airborne light detection and ranging (LiDAR) data...