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Combining spectrum, thermal, and texture features using machine learning algorithms for wheat nitrogen nutrient index estimation and model transferability analysis

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 222卷

作者： Zhang, Shaohua Duan, Jianzhao Qi, Xinghui Gao, Yuezhi He, Li Liu, Linru Guo, Tiancai Feng, Wei Henan Agr Univ Agron Coll State Key Lab Wheat & Maize Crop Sci Zhengzhou 450046 Henan Peoples R China Natl Wheat Technol Innovat Ctr Zhengzhou 450046 Henan Peoples R China CIMMYT China Henan Joint Ctr Wheat & Maize Improve Zhengzhou 450046 Henan Peoples R China Henan Agr Univ Agron Coll 15 Longzihu Coll Dist Zhengzhou 450046 Henan Peoples R China

The nitrogen nutrition index (NNI) has been extensively applied for the diagnosis of crop nitrogen status, providing insights into efficient nitrogen utilization and plant growth. In this study, we utilized a low-altitude unmanned aerial vehicle (UAV) platform, equipped with multispectral (MS), red-green-blue (RGB), and thermal infrared (TIR) cameras, to comprehensively capture wheat spectral information. The analysis of the relationship between NNI and relative yield revealed an initially linear relationship, which saturated for high NNI values. To enhance accuracy and minimize complexity, we employed a random forest (RF) - recursive feature elimination (RFE) method to select features as inputs for four machine learning (ML) models: back propagation neural network (BPNN), extreme learning machine (ELM), support vector regression (SVR), and Gaussian process regression (GPR). After feature selection, the prediction accuracies of single-sensor models were ranked as: MS > RGB > TIR. The R-2 values for the four ML models were in the range of 0.54-0.75. Among multi-sensor combinations, the GPR with MS + RGB + TIR input features achieved the best results with R-2 = 0.89 and RPD = 2.52. Further, the dataset was partitioned into six subsets based on location and cultivar variety to evaluate model transferability. The results showed that the transferability largely suffered during the bivariate conditions of different varieties at different locations;the transferability of the model was average improved by 11 % when GPR was combined with transfer component analysis (TCA). The accuracy and transferability of the NNI estimation models significantly improved, offering valuable guidance and methodological support for diagnosing the nitrogen nutrient status of wheat.

关键词： Multi -sensor data machine learning algorithms Feature selection Nitrogen nutrient index Transferability analysis

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Rapid and accurate screening of cystic echinococcosis in sheep based on serum Fourier-transform infrared spectroscopy combined with machine learning algorithms

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JOURNAL OF BIOPHOTONICS 2023年第5期16卷 e202200320-e202200320页

作者： Dawuti, Wubulitalifu Dou, Jingrui Zheng, Xiangxiang Lu, Xiaoyi Zhao, Hui Yang, Lingfei Lin, Renyong Lu, Guodong Xinjiang Med Univ Sch Publ Hlth Urumqi Peoples R China First Affiliated Hosp Xinjiang Med Univ Clin Med Res Inst State Key Lab Pathogenesis Prevent & Treatment Ce 137 Liyushan South Rd Urumqi 830054 Peoples R China Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China Xinjiang Univ Coll Software Urumqi Peoples R China First Affiliated Hosp Xinjiang Med Univ Dept Clin Lab Urumqi Peoples R China First Affiliated Hosp Xinjiang Med Univ Dept Abdominal Ultrasound Diag Urumqi Peoples R China

Cystic echinococcosis (CE) in sheep is a serious zoonotic parasitic disease caused by Echinococcus granulosus sensu stricto (s.s.). Presently, the screening technology for CE in sheep is time-consuming and inaccurate, and novel screening technology is urgently needed. In this work, we combined machine-learning algorithms with Fourier transform infrared (FT-IR) spectroscopy of serum to establish a quick and accurate screening approach for CE in sheep. Serum samples from 77 E. granulosus s.s.-infected sheep to 121 healthy control sheep were measured by FT-IR spectrometer. To optimize the classification accuracy of the serum FI-TR method for the E. granulosus s.s.-infected sheep and healthy control sheep, principal component analysis (PCA), linear discriminant analysis, and support vector machine (SVM) algorithms were used to analyze the data. Among all the bands, 1500-1700 cm(-1) band has the best classification effect;its diagnostic sensitivity, specificity, and accuracy of PCA-SVM were 100%, 95.74%, and 96.66%, respectively. The study showed that serum FT-IR spectroscopy combined with machine learning algorithms has great potential for rapid and accurate screening methods for the CE in sheep.

关键词： cystic echinococcosis in sheep Fourier transform infrared spectra machine learning algorithms screening serum

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UHPLC-UV-Q-Orbitrap HRMS combined with machine learning algorithms reveals the chemical markers of Euodiae Fructus among closely related cultivars

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INDUSTRIAL CROPS AND PRODUCTS 2021年 162卷 113279-113279页

作者： Li, Caihong Zhao, Yan Li, Wenjing Xing, Rongrong Lu, Jing Liu, Rongxia Yantai Univ Key Lab Mol Pharmacol & Drug Evaluat Collaborat Innovat Ctr Adv Drug Delivery Syst & B Sch PharmMinist Educ Yantai 264005 Peoples R China

Euodiae Fructus (EF, the fruit of Tetradium ruticarpum) belongs to the family Rutaceae which has a complex genetic background, making classification a challenge. In order to classify the three closely related cultivars (small flower EF, medium flower EF, and big flower EF) and simplify the identification procedure in the future, an approach integrating ultra-high performance liquid chromatography-ultraviolet spectrum-hybrid quadrupoleOrbitrap high resolution mass spectrometry (UHPLC-UV-Q-Orbitrap HRMS) with machine learning algorithms was developed. Orthogonal partial least squares-discriminant analysis (OPLS-DA) and random forest (RF) were used to assort the processed EF products and select the chemical markers to differentiate among the three EF cultivars, respectively. Then the thresholds of classification were determined using the eXtreme Gradient Boosting (XGboost) model based on the quantitative data of chemical markers by the UHPLC-Q-Orbitrap HRMS. The results demonstrated that the approach successfully discriminated among three closely related cultivars of EF, and five chemical markers (neochlorogenic acid, (+)-catechin, chlorogenic acid, dehydroevodiamine, and limonin) were selected. Thresholds were obtained to classify the three cultivars. If the content of neochlorogenic acid was greater than 7.52 mg/g, the sample belonged to small flower EF, otherwise, it belonged to medium flower EF or big flower EF. Furthermore, if the content of dehydroevodiamine was greater than 3.84 mg/g, the sample was medium flower EF, otherwise, it was big flower EF. In general, this research provides a novel strategy for the discrimination of Euodiae Fructus cultivars to avoid misapplication and possible side effects.

关键词： Euodiae Fructus UHPLC-UV-Q-Orbitrap HRMS machine learning algorithms Chemical markers Classification thresholds

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Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2021年 135卷 110114-110114页

作者： Agbulut, Umit Gurel, Ali Etem Bicen, Yunus Duzce Univ Fac Technol Dept Mech Engn TR-81620 Duzce Turkey Duzce Univ Vocat Sch Dept Elect & Energy TR-81010 Duzce Turkey Duzce Univ Fac Technol Dept Elect & Elect Engn TR-81620 Duzce Turkey

The prediction of global solar radiation for the regions is of great importance in terms of giving directions of solar energy conversion systems (design, modeling, and operation), selection of proper regions, and even future investment policies of the decision-makers. With this viewpoint, the objective of this paper is to predict daily global solar radiation data of four provinces (Kirklareli, Tokat, Nevsehir and Karaman) which have different solar radiation distribution in Turkey. In the study, four different machine learning algorithms (support vector machine (SVM), artificial neural network (ANN), kernel and nearest-neighbor (k-NN), and deep learning (DL)) are used. In the training of these algorithms, daily minimum and maximum ambient temperature, cloud cover, daily extraterrestrial solar radiation, day length and solar radiation of these provinces are used. The data is supplied from the Turkish State Meteorological Service and cover the last two years (01.01.2018-31.12.2019). To decide on the success of these algorithms, seven different statistical metrics (R-2, RMSE, rRMSE, MBE, MABE, t-stat, and MAPE) are discussed in the study. The results shows that R2, MABE, and RMSE values of all algorithms are ranging from 0.855 to 0.936, from 1.870 to 2.328 MJ/m(2), from 2.273 to 2.820 MJ/m(2), respectively. At all cases, k-NN exhibited the worst result in terms of R-2, RMSE, and MABE metrics. Of all the models, DL was the only model that exceeded the t-critic value. In conclusion, the present paper is reporting that all machine learning algorithms tested in this study can be used in the prediction of daily global solar radiation data with a high accuracy;however, the ANN algorithm is the best fitting algorithm among all algorithms. Then it is followed by DL, SVM and k-NN, respectively.

关键词： Daily global solar radiation machine learning algorithms Solar energy Prediction Model comparison

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Using Multi-Sensor Data Fusion Techniques and machine learning algorithms for Improving UAV-Based Yield Prediction of Oilseed Rape

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DRONES 2024年第11期8卷 642-642页

作者： Zhu, Hongyan Liang, Shikai Lin, Chengzhi He, Yong Xu, Jun-Li Guangxi Normal Univ Sch Elect & Informat Engn Guangxi Key Lab Brain Inspired Comp & Intelligent Guilin 541004 Peoples R China Guangxi Normal Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Integrated Circuits & Microsyst Guilin 541004 Peoples R China Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Peoples R China Univ Coll Dublin UCD Sch Biosyst & Food Engn Dublin Ireland

Accurate and timely prediction of oilseed rape yield is crucial in precision agriculture and field remote sensing. We explored the feasibility and potential for predicting oilseed rape yield through the utilization of a UAV-based platform equipped with RGB and multispectral cameras. Genetic algorithm-partial least square was employed and evaluated for effective wavelength (EW) or vegetation index (VI) selection. Additionally, different machine learning algorithms, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), least squares support vector machine (LS-SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and radial basis function neural network (RBFNN), were developed and compared. With multi-source data fusion by combining vegetation indices (color and narrow-band VIs), robust prediction models of yield in oilseed rape were built. The performance of prediction models using the combination of VIs (RBFNN: Rpre = 0.8143, RMSEP = 171.9 kg/hm2) from multiple sensors manifested better results than those using only narrow-band VIs (BPNN: Rpre = 0.7655, RMSEP = 188.3 kg/hm2) from a multispectral camera. The best models for yield prediction were found by applying BPNN (Rpre = 0.8114, RMSEP = 172.6 kg/hm2) built from optimal EWs and ELM (Rpre = 0.8118, RMSEP = 170.9 kg/hm2) using optimal VIs. Taken together, the findings conclusively illustrate the potential of UAV-based RGB and multispectral images for the timely and non-invasive prediction of oilseed rape yield. This study also highlights that a lightweight UAV equipped with dual-image-frame snapshot cameras holds promise as a valuable tool for high-throughput plant phenotyping and advanced breeding programs within the realm of precision agriculture.

关键词： data fusion machine learning algorithms oilseed rape remote sensing unmanned aerial vehicle (UAV) yield prediction

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A novel approach for asphalt pavement invisible distress classification prediction by machine learning algorithms

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INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING 2024年第1期25卷

作者： Shi, Jicun Jia, Shili Bai, Chengxiang Wang, Duanyi Xinxiang Univ Sch Civil Engn & Architecture Xinxiang Peoples R China Xinxiang Univ Sch Foreign Languages Xinxiang Peoples R China South China Univ Technol Sch Civil Engn & Transportat Guangzhou Peoples R China

The detection and identification of road invisible distress (ID) has always been a key issue of concern for pavement maintenance and rehabilitation (M&R) optimisation, especially for asphalt pavement with semi-rigid base. This study is to build a supervised machine learning model based on the detected characteristic variable data of the pavement structure strength, roughness, cracking, permeability, traffic volume, etc. It is to fit the model with class labelled data on the training set and to train an algorithm model that can intelligently detect the degree of ID. In addition, K-fold cross-validation and hyperparameter search are employed to find the optimal machine learning suitable for road ID category prediction. The results show that Gradient Boosting machine (GBM) model, LightGBM model and Random Forest (RF) model are marketed out from the other six models, for their classification performance of the models is the best, with statistical indicators up to 96%. Additionally, the LightGBM model achieves the best results with shortest computation time and 99% the accuracy of the test set. Furthermore, it also indicates that the three main factors affecting the ID are permeability, alligator cracking and equivalent single axle load (ESAL).

关键词： Asphalt pavement invisible distress machine learning algorithms K-fold cross validation hyperparameter search

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Influence of Optimal Hyperparameters on the Performance of machine learning algorithms for Predicting Heart Disease

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PROCESSES 2023年第3期11卷 734页

作者： Ahamad, Ghulab Nabi Shafiullah Fatima, Hira Imdadullah Zakariya, S. M. Abbas, Mohamed Alqahtani, Mohammed S. Usman, Mohammed Mangalayatan Univ Inst Appl Sci Aligarh 202145 Uttar Pradesh India BRA Bihar Univ KCTC Coll Dept Math Muzaffarpur 842001 India Aligarh Muslim Univ Univ Polytech Elect Engn Sect Aligarh 202002 Uttar Pradesh India King Khalid Univ Coll Engn Elect Engn Dept Abha 61421 Saudi Arabia King Khalid Univ Coll Appl Med Sci Radiol Sci Dept Abha 61421 Saudi Arabia Univ Leicester Space Res Ctr BioImaging Unit Michael Atiyah Bldg Leicester LE1 7RH Leics England

One of the most difficult challenges in medicine is predicting heart disease at an early stage. In this study, six machine learning (ML) algorithms, viz., logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest classifier, and extreme gradient boosting, were used to analyze two heart disease datasets. One dataset was UCI Kaggle Cleveland and the other was the comprehensive UCI Kaggle Cleveland, Hungary, Switzerland, and Long Beach V. The performance results of the machine learning techniques were obtained. The support vector machine with tuned hyperparameters achieved the highest testing accuracy of 87.91% for dataset-I and the extreme gradient boosting classifier with tuned hyperparameters achieved the highest testing accuracy of 99.03% for the comprehensive dataset-II. The novelty of this work was the use of grid search cross-validation to enhance the performance in the form of training and testing. The ideal parameters for predicting heart disease were identified through experimental results. Comparative studies were also carried out with the existing studies focusing on the prediction of heart disease, where the approach used in this work significantly outperformed their results.

关键词： heart disease prediction UCI Kaggle dataset machine learning algorithms GridSearchCV hyperparameters

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A comparative UHPLC-Q/TOF-MS-based metabolomics approach coupled with machine learning algorithms to differentiate Keemun black teas from narrow-geographic origins

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FOOD RESEARCH INTERNATIONAL 2022年 158卷 111512页

作者： Peng, Chuan-yi Ren, Yin-feng Ye, Zhi-hao Zhu, Hai-yan Liu, Xiao-qian Chen, Xiao-tong Hou, Ru-yan Granato, Daniel Cai, Hui-mei Anhui Agr Univ State Key Lab Tea Plant Biol & Utilizat Hefei 230036 Anhui Peoples R China Anhui Agr Univ Sch Tea & Food Sci & Technol Key Lab Food Nutr & Safety Hefei 230036 Anhui Peoples R China Univ Limerick Fac Sci & Engn Dept Biol Sci Limerick V94 T9PX Ireland

Geographic-label is a remarkable feature for Chinese tea products. In this study, the UHPLC-Q/TOF-MS-based metabolomics approach coupled with chemometrics was used to determine the five narrow-geographic origins of Keemun black tea. Thirty-nine differentiated compounds (VIP > 1) were identified, of which eight were quantified. Chemometric analysis revealed that the linear discriminant analysis (LDA) classification accuracy model is 91.7%, with 84.7% cross-validation accuracy. Three machine learning algorithms, namely feedforward neural network (FNN), random forest (RF) and support vector machine (SVM), were introduced to improve the recognition of narrow-geographic origins, the performances of the model were evaluated by confusion matrix, receiver operating characteristic curve (ROC) and area under the curve (AUC). The recognition of RF, SVM and FNN for Keemun black tea from five narrow-geographic origins were 87.5%, 94.44%, and 100%, respectively. Importantly, FNN exhibited an excellent classification effect with 100% accuracy. The results indicate that metabolomics fingerprints coupled with chemometrics can be used to authenticate the narrow-geographic origins of Keemun black teas.

关键词： Camellia sinensis tea Narrow-geographic origin Phenolic compounds Metabolomics fingerprints machine learning algorithms

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Digital Mapping and Scenario Prediction of Soil Salinity in Coastal Lands Based on Multi-Source Data Combined with machine learning algorithms

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REMOTE SENSING 2024年第14期16卷 2681页

作者： Zhou, Mengge Li, Yonghua Chinese Acad Sci Inst Geog Sci & Nat Resources Res 11A Datun Rd Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Salinization is a major soil degradation process threatening ecosystems and posing a great challenge to sustainable agriculture and food security worldwide. This study aimed to evaluate the potential of state-of-the-art machine learning algorithms in soil salinity (EC1:5) mapping. Further, we predicted the distribution patterns of soil salinity under different future scenarios in the Yellow River Delta. A geodatabase comprising 201 soil samples and 19 conditioning factors (containing data based on remote sensing images such as Landsat, SPOT/VEGETATION PROBA-V, SRTMDEMUTM, Sentinel-1, and Sentinel-2) was used to compare the predictive performance of empirical bayesian kriging regression, random forest, and CatBoost models. The CatBoost model exhibited the highest performance with both training and testing datasets, with an average MAE of 1.86, an average RMSE of 3.11, and an average R2 of 0.59 in the testing datasets. Among explanatory factors, soil Na was the most important for predicting EC1:5, followed by the normalized difference vegetation index and soil organic carbon. Soil EC(1:5 )predictions suggested that the Yellow River Delta region faces severe salinization, particularly in coastal zones. Among three scenarios with increases in soil organic carbon content (1, 2, and 3 g/kg), the 2 g/kg scenario resulted in the best improvement effect on saline-alkali soils with EC1:5 > 2 ds/m. Our results provide valuable insights for policymakers to improve saline-alkali land quality and plan regional agricultural development.

关键词： soil salinity soil salinity digital mapping machine learning algorithms CatBoost random forest

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Development of tool condition monitoring system in end milling process using wavelet features and Hoelder's exponent with machine learning algorithms

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MEASUREMENT 2021年 173卷 108671-108671页

作者： Mohanraj, T. Yerchuru, Jayanthi Krishnan, H. Aravind, R. S. Nithin Yameni, R. Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Mech Engn Coimbatore Tamil Nadu India

An effort was made to monitor the flank wear using wavelet analysis by extracting the Hoelder's exponent as a feature and using various machine learning algorithms to forecast the tool condition. The test was conducted on a Tungsten carbide insert with selected cutting parameters and the acquired vibration signals were used to develop the prediction model. The wavelet coefficients, Hoelder's exponent, and statistical features were extracted from the vibration signals. These features were used in machine learning algorithms like SVM, KNN, Kernel Bayes, Multilayer perceptron, and Decision trees to forecast the flank wear. The accuracy of the machining algorithm was analyzed through the confusion matrix and accuracy. The results revealed that HE along with wavelet coefficients performed better than statistical features. From the analysis, it was found that DT and SVM had the highest accuracy of 100% and 99.86% respectively. The performance of the selected ML was verified with benchmarking datasets and proves its accuracy.

关键词： Inconel 625 End milling Flank wear Vibration signals Hoelder's exponent machine learning algorithms Tool condition monitoring

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