Matches in SemOpenAlex for { <https://semopenalex.org/work/W4207038069> ?p ?o ?g. }
Showing items 1 to 94 of
94
with 100 items per page.
- W4207038069 abstract "Predicting crop yield is a complex task since it depends on multiple factors. Although many models have been developed so far in the literature, the performance of current models is not satisfactory, and hence, they must be improved. In this study, we developed deep learning-based models to evaluate how the underlying algorithms perform with respect to different performance criteria. The algorithms evaluated in our study are the XGBoost machine learning (ML) algorithm, Convolutional Neural Networks (CNN)-Deep Neural Networks (DNN), CNN-XGBoost, CNN-Recurrent Neural Networks (RNN), and CNN-Long Short Term Memory (LSTM). For the case study, we performed experiments on a public soybean dataset that consists of 395 features including weather and soil parameters and 25,345 samples. The results showed that the hybrid CNN-DNN model outperforms other models, having an RMSE equal to 0.266, an MSE of 0.071, and an MAE of 0.199. The predictions of the model fit with an R2 of 0.87. The second-best result was achieved by the XGBoost model, which required less time to execute compared to the other DL-based models." @default.
- W4207038069 created "2022-01-26" @default.
- W4207038069 creator A5038073619 @default.
- W4207038069 creator A5047358343 @default.
- W4207038069 creator A5060643121 @default.
- W4207038069 date "2022-01-22" @default.
- W4207038069 modified "2023-10-17" @default.
- W4207038069 title "Hybrid Deep Learning-based Models for Crop Yield Prediction" @default.
- W4207038069 cites W2072128103 @default.
- W4207038069 cites W2543665758 @default.
- W4207038069 cites W2567297430 @default.
- W4207038069 cites W2604645045 @default.
- W4207038069 cites W2805142011 @default.
- W4207038069 cites W2896107488 @default.
- W4207038069 cites W2902487926 @default.
- W4207038069 cites W2910729503 @default.
- W4207038069 cites W2914941211 @default.
- W4207038069 cites W2919115771 @default.
- W4207038069 cites W2922128523 @default.
- W4207038069 cites W2945600159 @default.
- W4207038069 cites W2979666105 @default.
- W4207038069 cites W2982418982 @default.
- W4207038069 cites W2987573763 @default.
- W4207038069 cites W2992839153 @default.
- W4207038069 cites W2996041315 @default.
- W4207038069 cites W2997552745 @default.
- W4207038069 cites W3007597990 @default.
- W4207038069 cites W3009587349 @default.
- W4207038069 cites W3011178959 @default.
- W4207038069 cites W3014134514 @default.
- W4207038069 cites W3015117847 @default.
- W4207038069 cites W3015527879 @default.
- W4207038069 cites W3017079270 @default.
- W4207038069 cites W3020885311 @default.
- W4207038069 cites W3029014910 @default.
- W4207038069 cites W3034067006 @default.
- W4207038069 cites W3042706118 @default.
- W4207038069 cites W3043115696 @default.
- W4207038069 cites W3079760979 @default.
- W4207038069 cites W3103444592 @default.
- W4207038069 cites W3123407900 @default.
- W4207038069 cites W4255816075 @default.
- W4207038069 doi "https://doi.org/10.1080/08839514.2022.2031823" @default.
- W4207038069 hasPublicationYear "2022" @default.
- W4207038069 type Work @default.
- W4207038069 citedByCount "23" @default.
- W4207038069 countsByYear W42070380692022 @default.
- W4207038069 countsByYear W42070380692023 @default.
- W4207038069 crossrefType "journal-article" @default.
- W4207038069 hasAuthorship W4207038069A5038073619 @default.
- W4207038069 hasAuthorship W4207038069A5047358343 @default.
- W4207038069 hasAuthorship W4207038069A5060643121 @default.
- W4207038069 hasBestOaLocation W42070380691 @default.
- W4207038069 hasConcept C108583219 @default.
- W4207038069 hasConcept C119857082 @default.
- W4207038069 hasConcept C147168706 @default.
- W4207038069 hasConcept C153180895 @default.
- W4207038069 hasConcept C154945302 @default.
- W4207038069 hasConcept C162324750 @default.
- W4207038069 hasConcept C187736073 @default.
- W4207038069 hasConcept C2780451532 @default.
- W4207038069 hasConcept C41008148 @default.
- W4207038069 hasConcept C50644808 @default.
- W4207038069 hasConcept C81363708 @default.
- W4207038069 hasConceptScore W4207038069C108583219 @default.
- W4207038069 hasConceptScore W4207038069C119857082 @default.
- W4207038069 hasConceptScore W4207038069C147168706 @default.
- W4207038069 hasConceptScore W4207038069C153180895 @default.
- W4207038069 hasConceptScore W4207038069C154945302 @default.
- W4207038069 hasConceptScore W4207038069C162324750 @default.
- W4207038069 hasConceptScore W4207038069C187736073 @default.
- W4207038069 hasConceptScore W4207038069C2780451532 @default.
- W4207038069 hasConceptScore W4207038069C41008148 @default.
- W4207038069 hasConceptScore W4207038069C50644808 @default.
- W4207038069 hasConceptScore W4207038069C81363708 @default.
- W4207038069 hasIssue "1" @default.
- W4207038069 hasLocation W42070380691 @default.
- W4207038069 hasLocation W42070380692 @default.
- W4207038069 hasOpenAccess W4207038069 @default.
- W4207038069 hasPrimaryLocation W42070380691 @default.
- W4207038069 hasRelatedWork W2731899572 @default.
- W4207038069 hasRelatedWork W2999805992 @default.
- W4207038069 hasRelatedWork W3116150086 @default.
- W4207038069 hasRelatedWork W3133861977 @default.
- W4207038069 hasRelatedWork W4200173597 @default.
- W4207038069 hasRelatedWork W4223943233 @default.
- W4207038069 hasRelatedWork W4291897433 @default.
- W4207038069 hasRelatedWork W4312417841 @default.
- W4207038069 hasRelatedWork W4321369474 @default.
- W4207038069 hasRelatedWork W4380075502 @default.
- W4207038069 hasVolume "36" @default.
- W4207038069 isParatext "false" @default.
- W4207038069 isRetracted "false" @default.
- W4207038069 workType "article" @default.