FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3014187079> ?p ?o ?g. }

• W3014187079 endingPage "97" @default.
• W3014187079 startingPage "97" @default.
• W3014187079 abstract "Non-linear systems, such as biological systems, can be simulated by artificial neural network (ANN) techniques. This research aims to use ANN to simulate the accumulated aerial dry matter (leaf, stem, and fruit) and fresh fruit yield of a tomato crop. Two feed-forward backpropagation ANNs, with three hidden layers, were trained and validated by the Levenberg–Marquardt algorithm for weights and bias adjusted. The input layer consisted of the leaf area, plant height, fruit number, dry matter of leaves, stems and fruits, and the growth degree-days at 136 days after transplanting (DAT); these were obtained from a tomato crop, a hybrid, EL CID F1, with indeterminate growth habits, grown with a mixture of peat moss and perlite 1:1 (v/v) (substrate) and calcareous soil (soil). Based on the experimentation of the ANNs with one, two and three hidden layers, with MSE values less than 1.55, 0.94 and 0.49, respectively, the ANN with three hidden layers was chosen. The 7-10-7-5-2 and 7-10-8-5-2 topologies showed the best performance for the substrate (R = 0.97, MSE = 0.107, error = 12.06%) and soil (R = 0.94, MSE = 0.049, error = 13.65%), respectively. These topologies correctly simulated the aerial dry matter and the fresh fruit yield of the studied tomato crop." @default.
• W3014187079 created "2020-04-10" @default.
• W3014187079 creator A5010618467 @default.
• W3014187079 creator A5012213238 @default.
• W3014187079 creator A5048083064 @default.
• W3014187079 creator A5054130629 @default.
• W3014187079 creator A5075358407 @default.
• W3014187079 creator A5083137292 @default.
• W3014187079 date "2020-04-01" @default.
• W3014187079 modified "2023-10-02" @default.
• W3014187079 title "Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter" @default.
• W3014187079 cites W1573140146 @default.
• W3014187079 cites W1965621293 @default.
• W3014187079 cites W1965692103 @default.
• W3014187079 cites W1968764626 @default.
• W3014187079 cites W1970871687 @default.
• W3014187079 cites W1970903351 @default.
• W3014187079 cites W1971352677 @default.
• W3014187079 cites W1986736495 @default.
• W3014187079 cites W1988210813 @default.
• W3014187079 cites W1988298082 @default.
• W3014187079 cites W1988716993 @default.
• W3014187079 cites W1991405508 @default.
• W3014187079 cites W1993982416 @default.
• W3014187079 cites W2001379895 @default.
• W3014187079 cites W2003308027 @default.
• W3014187079 cites W2003562462 @default.
• W3014187079 cites W2011272431 @default.
• W3014187079 cites W2012796776 @default.
• W3014187079 cites W2017823670 @default.
• W3014187079 cites W2018334443 @default.
• W3014187079 cites W2021106676 @default.
• W3014187079 cites W2036191224 @default.
• W3014187079 cites W2039582052 @default.
• W3014187079 cites W2039769092 @default.
• W3014187079 cites W2042411821 @default.
• W3014187079 cites W2064087909 @default.
• W3014187079 cites W2067069641 @default.
• W3014187079 cites W2069565430 @default.
• W3014187079 cites W2074501882 @default.
• W3014187079 cites W2080010866 @default.
• W3014187079 cites W2087070363 @default.
• W3014187079 cites W2089596800 @default.
• W3014187079 cites W2090366490 @default.
• W3014187079 cites W2101275971 @default.
• W3014187079 cites W2114555006 @default.
• W3014187079 cites W2116905012 @default.
• W3014187079 cites W2125818553 @default.
• W3014187079 cites W2134281019 @default.
• W3014187079 cites W2136555411 @default.
• W3014187079 cites W2137862202 @default.
• W3014187079 cites W2167662918 @default.
• W3014187079 cites W2187946327 @default.
• W3014187079 cites W2256578114 @default.
• W3014187079 cites W2270594946 @default.
• W3014187079 cites W2317341387 @default.
• W3014187079 cites W2473505418 @default.
• W3014187079 cites W2559249636 @default.
• W3014187079 cites W2589188649 @default.
• W3014187079 cites W2591559980 @default.
• W3014187079 cites W2618400371 @default.
• W3014187079 cites W2889543275 @default.
• W3014187079 cites W2898280479 @default.
• W3014187079 cites W2910314772 @default.
• W3014187079 cites W2911287026 @default.
• W3014187079 cites W2912398505 @default.
• W3014187079 cites W2916373210 @default.
• W3014187079 cites W2943036395 @default.
• W3014187079 cites W2983803411 @default.
• W3014187079 cites W2991267471 @default.
• W3014187079 cites W2995726119 @default.
• W3014187079 cites W3006097175 @default.
• W3014187079 cites W55464642 @default.
• W3014187079 cites W68640414 @default.
• W3014187079 doi "https://doi.org/10.3390/agriculture10040097" @default.
• W3014187079 hasPublicationYear "2020" @default.
• W3014187079 type Work @default.
• W3014187079 sameAs 3014187079 @default.
• W3014187079 citedByCount "10" @default.
• W3014187079 countsByYear W30141870792020 @default.
• W3014187079 countsByYear W30141870792021 @default.
• W3014187079 countsByYear W30141870792022 @default.
• W3014187079 countsByYear W30141870792023 @default.
• W3014187079 crossrefType "journal-article" @default.
• W3014187079 hasAuthorship W3014187079A5010618467 @default.
• W3014187079 hasAuthorship W3014187079A5012213238 @default.
• W3014187079 hasAuthorship W3014187079A5048083064 @default.
• W3014187079 hasAuthorship W3014187079A5054130629 @default.
• W3014187079 hasAuthorship W3014187079A5075358407 @default.
• W3014187079 hasAuthorship W3014187079A5083137292 @default.
• W3014187079 hasBestOaLocation W30141870791 @default.
• W3014187079 hasConcept C123552892 @default.
• W3014187079 hasConcept C134121241 @default.
• W3014187079 hasConcept C137580998 @default.
• W3014187079 hasConcept C144027150 @default.
• W3014187079 hasConcept C154945302 @default.
• W3014187079 hasConcept C155032097 @default.
• W3014187079 hasConcept C168741863 @default.

• next