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Evaluation of conditioned Latin hypercube sampling for soil mapping based on a machine learning method
Conditioned Latin hypercube sampling Soil mapping Representativeness Sample randomness
2024/1/12
Sampling design plays an important role in soil survey and soil mapping. Conditioned Latin hypercube sampling (cLHS) has been proven as an efficient sampling strategy and used widely in digital soil m...
Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms
precision agriculture field crops machine learning deep learning image processing textural features
2023/12/21
The current mainstream approach of using manual measurements and visual inspections for crop lodging detection is inefficient, time-consuming, and subjective. An innovative method for wheat lodging de...
Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms-Case Study of Evergreen Conifer Planted Forests in Japan
UAS stem volume TLS SAR random forest support vector multiple regression forest biophysical parameter
2023/12/21
The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing...
Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China
Water resources Remote sensing Total nitrogen Hyperspectral imagery Machine learning Bootstrap
2023/12/19
In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality paramete...
Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches
land water segmentation remote sensing deep learning sparse labels
2023/12/5
Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife...
Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning
machine learning ratio-based indices orthogonal indices Koppen–Geiger climate regionalization landscape change remote sensing landcover
2023/12/4
Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural...
Wildfire Risk Assessment in Liangshan Prefecture, China Based on An Integration Machine Learning Algorithm
frequency ratio MCD64A1 Bayesian optimization support vector machine random forest extreme gradient boosting
2023/11/30
Previous wildfire risk assessments have problems such as subjectivity of weight allocation and the linearization of statistical models, resulting in generally low robustness and low generalization abi...
Machine learning-based atom contribution method for the prediction of surface charge density profiles and solvent design
atom contribution computer-aided molecular design decomposition-based algorithm machine learning
2023/6/19
Solvents are widely used in chemical processes. The use of efficient model-based solvent selection techniques is an option worth considering for rapid identification of candidates with better economic...
Classification of Toona Sinensis Young Leaves Using Machine Learning And UAV-Borne Hyperspectral Imagery
Classification Toona Sinensis Young Leaves
2023/6/2
Rapid and accurate distinction between young and old leaves of Toona sinensis in the wild is of great significance to the selection of T. sinensis varieties and the evaluation of relative yield. In th...
An integrated platform and meta-learner for feature engineering machine learning analysis and modeling of DNA, RNA and protein sequence data
python 工具包 蛋白质序列
2024/5/17
一个全面和通用的基于python的工具包，集成了特征提取、聚类、归一化、选择、降维、预测器构建、最佳描述符/模型选择、集成学习和DNA、RNA和蛋白质序列结果可视化的功能。用户只需要上传自己的数据集，并从中计算出自己需要的功能，所有必要的程序和优化设置都由软件自动完成。iLearn包括DNA、RNA和蛋白质的各种描述符，支持四种特征输出格式，以便于直接使用输出或与其他计算工具通信。总的来说，iLe...
A comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
棉花 纤维品质 QTL 遗传基因组
2024/4/16
本研究在sGK9708×0-153、LMY22×LY343、鲁绵研28×新陆早24等多个重组自交系群体中通过综合考评表型变异和共有置信区间筛选主效QTL，结果表明在A04、A07、A08和D08染色体上存在重要的纤维品质相关QTL。其中，通过SGK9708×0-153群体的转录组数据在纤维长度和强度QTL中分别检测到50和48个候选基因。这些基因的共表达网络模块在多个RIL群体中的基因对之间表现出...
Identification of weeds based on hyperspectral imaging and machine learning
weeds hyperspectral imaging machine learning
2023/6/5
Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control include manual and chemical approaches can be time consuming and costly. Some chemical herbici...
Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods
Spectrometric Classification Bamboo Shoot Machine Learning
2023/6/5
The nutrition and quality of bamboo shoots from different species have a large variation, and traditional methods used for the classification of different bamboo shoot species are expensive and time-c...
Machine learning and side channel analysis in a CTF competition
key recovery deep learning machine learning
2019/7/25
Machine learning is nowadays supplanting or extending human expertise in many domains ranging from board games to text translation. Correspondingly, the use of such tools is also on the rise in comput...
Breaking the Lightweight Secure PUF: Understanding the Relation of Input Transformations and Machine Learning Resistance
Physically Unclonable Function Machine Learning Modelling Attack
2019/7/15
Physical Unclonable Functions (PUFs) and, in particular, XOR Arbiter PUFs have gained much research interest as an authentication mechanism for embedded systems. One of the biggest problems of (strong...