FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4220683037> ?p ?o ?g. }

• W4220683037 endingPage "1337" @default.
• W4220683037 startingPage "1337" @default.
• W4220683037 abstract "Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R2) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R2 (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods." @default.
• W4220683037 created "2022-04-03" @default.
• W4220683037 creator A5032714629 @default.
• W4220683037 creator A5043001970 @default.
• W4220683037 creator A5060301380 @default.
• W4220683037 creator A5074349822 @default.
• W4220683037 creator A5079281593 @default.
• W4220683037 creator A5083824197 @default.
• W4220683037 creator A5088304035 @default.
• W4220683037 date "2022-03-09" @default.
• W4220683037 modified "2023-10-06" @default.
• W4220683037 title "Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images" @default.
• W4220683037 cites W1442930683 @default.
• W4220683037 cites W1858903126 @default.
• W4220683037 cites W1963915078 @default.
• W4220683037 cites W1965696795 @default.
• W4220683037 cites W1965763199 @default.
• W4220683037 cites W1978996791 @default.
• W4220683037 cites W1980467157 @default.
• W4220683037 cites W1981768943 @default.
• W4220683037 cites W1983279516 @default.
• W4220683037 cites W1986738039 @default.
• W4220683037 cites W1997882914 @default.
• W4220683037 cites W2009542758 @default.
• W4220683037 cites W2011231327 @default.
• W4220683037 cites W2017859040 @default.
• W4220683037 cites W2017903276 @default.
• W4220683037 cites W2019088823 @default.
• W4220683037 cites W2025757188 @default.
• W4220683037 cites W2025782027 @default.
• W4220683037 cites W2029118156 @default.
• W4220683037 cites W2032469401 @default.
• W4220683037 cites W2035527955 @default.
• W4220683037 cites W2039604550 @default.
• W4220683037 cites W2040403200 @default.
• W4220683037 cites W2044465660 @default.
• W4220683037 cites W2063623478 @default.
• W4220683037 cites W2064219338 @default.
• W4220683037 cites W2066834920 @default.
• W4220683037 cites W2072490792 @default.
• W4220683037 cites W2074607920 @default.
• W4220683037 cites W2084857239 @default.
• W4220683037 cites W2088223830 @default.
• W4220683037 cites W2107163906 @default.
• W4220683037 cites W2110017687 @default.
• W4220683037 cites W2132424470 @default.
• W4220683037 cites W2133125644 @default.
• W4220683037 cites W2133144887 @default.
• W4220683037 cites W2137651245 @default.
• W4220683037 cites W2139294397 @default.
• W4220683037 cites W2139514605 @default.
• W4220683037 cites W2145058632 @default.
• W4220683037 cites W2153635508 @default.
• W4220683037 cites W2155632266 @default.
• W4220683037 cites W2157026765 @default.
• W4220683037 cites W2157287989 @default.
• W4220683037 cites W2162600997 @default.
• W4220683037 cites W2167433403 @default.
• W4220683037 cites W2231576311 @default.
• W4220683037 cites W2261059368 @default.
• W4220683037 cites W2471041305 @default.
• W4220683037 cites W2475633080 @default.
• W4220683037 cites W2598998899 @default.
• W4220683037 cites W2602690536 @default.
• W4220683037 cites W2728224506 @default.
• W4220683037 cites W2739254607 @default.
• W4220683037 cites W2749555407 @default.
• W4220683037 cites W2754118801 @default.
• W4220683037 cites W2765366036 @default.
• W4220683037 cites W2782239312 @default.
• W4220683037 cites W2788506577 @default.
• W4220683037 cites W2803133125 @default.
• W4220683037 cites W2804616917 @default.
• W4220683037 cites W2811208991 @default.
• W4220683037 cites W2811447860 @default.
• W4220683037 cites W2888150988 @default.
• W4220683037 cites W2890513934 @default.
• W4220683037 cites W2891621712 @default.
• W4220683037 cites W2891975230 @default.
• W4220683037 cites W2902273998 @default.
• W4220683037 cites W2912130932 @default.
• W4220683037 cites W2916306081 @default.
• W4220683037 cites W2923833971 @default.
• W4220683037 cites W2932685513 @default.
• W4220683037 cites W2943422163 @default.
• W4220683037 cites W2950734190 @default.
• W4220683037 cites W2964415981 @default.
• W4220683037 cites W2997382416 @default.
• W4220683037 cites W3006705642 @default.
• W4220683037 cites W3037068037 @default.
• W4220683037 cites W3040221110 @default.
• W4220683037 cites W3083611323 @default.
• W4220683037 cites W3084417673 @default.
• W4220683037 cites W3087070249 @default.
• W4220683037 cites W3092616522 @default.
• W4220683037 cites W3109606616 @default.
• W4220683037 cites W3126645936 @default.
• W4220683037 cites W3128511351 @default.

• next