Mitigating Illumination-, Leaf-, and View-Angle Dependencies in Hyperspectral Imaging Using Polarimetry [0.03%]
基于偏振的高光谱成像中光照、叶片倾角影响抑制方法研究
Daniel Krafft,Clifton G Scarboro,William Hsieh et al.
Daniel Krafft et al.
Automation of plant phenotyping using data from high-dimensional imaging sensors is on the forefront of agricultural research for its potential to improve seasonal yield by monitoring crop health and accelerating breeding programs. A common...
Handling the Challenges of Small-Scale Labeled Data and Class Imbalances in Classifying the N and K Statuses of Rubber Leaves Using Hyperspectroscopy Techniques [0.03%]
基于高光谱技术分类橡胶叶片氮钾营养状态的挑战及应对措施研究
Wenfeng Hu,Weihao Tang,Chuang Li et al.
Wenfeng Hu et al.
The nutritional status of rubber trees (Hevea brasiliensis) is inseparable from the production of natural rubber. Nitrogen (N) and potassium (K) levels in rubber leaves are 2 crucial criteria that reflect the nutritional status of the rubbe...
Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence [0.03%]
基于计算智能的玉米群体三维建模
Yandong Wu,Weiliang Wen,Shenghao Gu et al.
Yandong Wu et al.
The 3-dimensional (3D) modeling of crop canopies is fundamental for studying functional-structural plant models. Existing studies often fail to capture the structural characteristics of crop canopies, such as organ overlapping and resource ...
High-Throughput Spike Detection in Greenhouse Cultivated Grain Crops with Attention Mechanisms-Based Deep Learning Models [0.03%]
基于注意力机制深度学习模型的温室粮食作物高通量花 spike 检测研究
Sajid Ullah,Klára Panzarová,Martin Trtílek et al.
Sajid Ullah et al.
Detection of spikes is the first important step toward image-based quantitative assessment of crop yield. However, spikes of grain plants occupy only a tiny fraction of the image area and often emerge in the middle of the mass of plant leav...
Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review [0.03%]
成像传感器和人工智能在植物应力检测中的进展：系统性文献回顾
Jason John Walsh,Eleni Mangina,Sonia Negrão
Jason John Walsh
Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection...
Dynamic Analysis of Chlorophyll a Fluorescence in Response to Time-Variant Excitations during Strong Actinic Illumination and Application in Probing Plant Water Loss [0.03%]
强光照条件下叶绿素荧光对时间变化激励响应的动态分析及在植物水分流失检测中的应用
Junqing Chen,Ya Guo,Jinglu Tan
Junqing Chen
Magnitude measurement of chlorophyll a fluorescence (ChlF) involves challenges, and dynamic responses to variable excitations may offer an alternative. In this research, ChlF was measured during strong actinic light by using a pseudo-random...
Practical Identifiability of Plant Growth Models: A Unifying Framework and Its Specification for Three Local Indices [0.03%]
植物生长模型的实际可识别性：统一框架及其在三个局部指数中的应用
Jean Velluet,Antonin Della Noce,Véronique Letort
Jean Velluet
Amid the rise of machine learning models, a substantial portion of plant growth models remains mechanistic, seeking to capture an in-depth understanding of the underlying phenomena governing the system's dynamics. The development of these m...
Identifying Regenerated Saplings by Stratifying Forest Overstory Using Airborne LiDAR Data [0.03%]
利用机载LiDAR数据分层森林林冠来识别再生幼树
Liming Du,Yong Pang
Liming Du
Identifying the spatiotemporal distributions and phenotypic characteristics of understory saplings is beneficial in exploring the internal mechanisms of plant regeneration and providing technical assistances for continues cover forest manag...
Establishing a Gross Primary Productivity Model by SIF and PRI on the Rice Canopy [0.03%]
水稻冠层中由SIF和PRI建立的总初级生产力模型
Zhanhao Zhang,Jianmao Guo,Shihui Han et al.
Zhanhao Zhang et al.
Solar-induced chlorophyll fluorescence (SIF) has shown remarkable results in estimating vegetation carbon cycles, and combining it with the photochemical reflectance index (PRI) has great potential for estimating gross primary productivity ...
DomAda-FruitDet: Domain-Adaptive Anchor-Free Fruit Detection Model for Auto Labeling [0.03%]
DomAda-FruitDet：用于自动标注的无锚域自适应水果检测模型
Wenli Zhang,Chao Zheng,Chenhuizi Wang et al.
Wenli Zhang et al.
Recently, deep learning-based fruit detection applications have been widely used in the modern fruit industry; however, the training data labeling process remains a time-consuming and labor-intensive process. Auto labeling can provide a con...