FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3194684502> ?p ?o ?g. }

• W3194684502 endingPage "3241" @default.
• W3194684502 startingPage "3241" @default.
• W3194684502 abstract "Accurate, precise, and timely estimation of crop yield is key to a grower’s ability to proactively manage crop growth and predict harvest logistics. Such yield predictions typically are based on multi-parametric models and in-situ sampling. Here we investigate the extension of a greenhouse study, to low-altitude unmanned aerial systems (UAS). Our principal objective was to investigate snap bean crop (Phaseolus vulgaris) yield using imaging spectroscopy (hyperspectral imaging) in the visible to near-infrared (VNIR; 400–1000 nm) region via UAS. We aimed to solve the problem of crop yield modelling by identifying spectral features explaining yield and evaluating the best time period for accurate yield prediction, early in time. We introduced a Python library, named Jostar, for spectral feature selection. Embedded in Jostar, we proposed a new ranking method for selected features that reaches an agreement between multiple optimization models. Moreover, we implemented a well-known denoising algorithm for the spectral data used in this study. This study benefited from two years of remotely sensed data, captured at multiple instances over the summers of 2019 and 2020, with 24 plots and 18 plots, respectively. Two harvest stage models, early and late harvest, were assessed at two different locations in upstate New York, USA. Six varieties of snap bean were quantified using two components of yield, pod weight and seed length. We used two different vegetation detection algorithms. the Red-Edge Normalized Difference Vegetation Index (RENDVI) and Spectral Angle Mapper (SAM), to subset the fields into vegetation vs. non-vegetation pixels. Partial least squares regression (PLSR) was used as the regression model. Among nine different optimization models embedded in Jostar, we selected the Genetic Algorithm (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Particle Swarm Optimization (PSO) and their resulting joint ranking. The findings show that pod weight can be explained with a high coefficient of determination (R2 = 0.78–0.93) and low root-mean-square error (RMSE = 940–1369 kg/ha) for two years of data. Seed length yield assessment resulted in higher accuracies (R2 = 0.83–0.98) and lower errors (RMSE = 4.245–6.018 mm). Among optimization models used, ACO and SA outperformed others and the SAM vegetation detection approach showed improved results when compared to the RENDVI approach when dense canopies were being examined. Wavelengths at 450, 500, 520, 650, 700, and 760 nm, were identified in almost all data sets and harvest stage models used. The period between 44–55 days after planting (DAP) the optimal time period for yield assessment. Future work should involve transferring the learned concepts to a multispectral system, for eventual operational use; further attention should also be paid to seed length as a ground truth data collection technique, since this yield indicator is far more rapid and straightforward." @default.
• W3194684502 created "2021-08-30" @default.
• W3194684502 creator A5001361585 @default.
• W3194684502 creator A5043643386 @default.
• W3194684502 creator A5054912828 @default.
• W3194684502 creator A5061307460 @default.
• W3194684502 creator A5085740223 @default.
• W3194684502 date "2021-08-15" @default.
• W3194684502 modified "2023-10-11" @default.
• W3194684502 title "Broadacre Crop Yield Estimation Using Imaging Spectroscopy from Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean" @default.
• W3194684502 cites W1541794730 @default.
• W3194684502 cites W1574447377 @default.
• W3194684502 cites W1619226191 @default.
• W3194684502 cites W1974820932 @default.
• W3194684502 cites W1995806857 @default.
• W3194684502 cites W1997337571 @default.
• W3194684502 cites W2007356270 @default.
• W3194684502 cites W2017014096 @default.
• W3194684502 cites W2020868791 @default.
• W3194684502 cites W2022905067 @default.
• W3194684502 cites W2024168391 @default.
• W3194684502 cites W2030474245 @default.
• W3194684502 cites W2036003376 @default.
• W3194684502 cites W2038782607 @default.
• W3194684502 cites W2039596145 @default.
• W3194684502 cites W2041963641 @default.
• W3194684502 cites W2044229870 @default.
• W3194684502 cites W2046404820 @default.
• W3194684502 cites W2061280223 @default.
• W3194684502 cites W2062123859 @default.
• W3194684502 cites W2063371808 @default.
• W3194684502 cites W2067869061 @default.
• W3194684502 cites W2071423370 @default.
• W3194684502 cites W2083270190 @default.
• W3194684502 cites W2084546104 @default.
• W3194684502 cites W2086843634 @default.
• W3194684502 cites W2088477804 @default.
• W3194684502 cites W2102636708 @default.
• W3194684502 cites W2126105956 @default.
• W3194684502 cites W2127029462 @default.
• W3194684502 cites W2133097426 @default.
• W3194684502 cites W2137317896 @default.
• W3194684502 cites W2149772057 @default.
• W3194684502 cites W2151097131 @default.
• W3194684502 cites W2152004600 @default.
• W3194684502 cites W2160172778 @default.
• W3194684502 cites W2161073299 @default.
• W3194684502 cites W2163027738 @default.
• W3194684502 cites W2168747298 @default.
• W3194684502 cites W2170175538 @default.
• W3194684502 cites W2213612645 @default.
• W3194684502 cites W2294798173 @default.
• W3194684502 cites W2502406050 @default.
• W3194684502 cites W2733360954 @default.
• W3194684502 cites W2765366036 @default.
• W3194684502 cites W2779516725 @default.
• W3194684502 cites W2793218933 @default.
• W3194684502 cites W2794062624 @default.
• W3194684502 cites W2898710507 @default.
• W3194684502 cites W2925507233 @default.
• W3194684502 cites W2944892533 @default.
• W3194684502 cites W2971811912 @default.
• W3194684502 cites W3000098473 @default.
• W3194684502 cites W3007045993 @default.
• W3194684502 cites W3007651920 @default.
• W3194684502 cites W3029601471 @default.
• W3194684502 cites W3034643611 @default.
• W3194684502 cites W3037968153 @default.
• W3194684502 cites W3101640299 @default.
• W3194684502 cites W3108875334 @default.
• W3194684502 cites W4210580908 @default.
• W3194684502 cites W4238076109 @default.
• W3194684502 doi "https://doi.org/10.3390/rs13163241" @default.
• W3194684502 hasPublicationYear "2021" @default.
• W3194684502 type Work @default.
• W3194684502 sameAs 3194684502 @default.
• W3194684502 citedByCount "12" @default.
• W3194684502 countsByYear W31946845022021 @default.
• W3194684502 countsByYear W31946845022022 @default.
• W3194684502 countsByYear W31946845022023 @default.
• W3194684502 crossrefType "journal-article" @default.
• W3194684502 hasAuthorship W3194684502A5001361585 @default.
• W3194684502 hasAuthorship W3194684502A5043643386 @default.
• W3194684502 hasAuthorship W3194684502A5054912828 @default.
• W3194684502 hasAuthorship W3194684502A5061307460 @default.
• W3194684502 hasAuthorship W3194684502A5085740223 @default.
• W3194684502 hasBestOaLocation W31946845021 @default.
• W3194684502 hasConcept C126343540 @default.
• W3194684502 hasConcept C142724271 @default.
• W3194684502 hasConcept C1549246 @default.
• W3194684502 hasConcept C159078339 @default.
• W3194684502 hasConcept C205649164 @default.
• W3194684502 hasConcept C25989453 @default.
• W3194684502 hasConcept C2776133958 @default.
• W3194684502 hasConcept C39432304 @default.
• W3194684502 hasConcept C41008148 @default.
• W3194684502 hasConcept C5457282 @default.
• W3194684502 hasConcept C62649853 @default.

• next