دورية أكاديمية
Artificial neural network approach for predicting the sesame ( Sesamum indicum L.) leaf area: A non-destructive and accurate method.
| العنوان: | Artificial neural network approach for predicting the sesame ( Sesamum indicum L.) leaf area: A non-destructive and accurate method. |
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| المؤلفون: | da Silva Ribeiro JE; Federal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil., Dos Santos Coêlho E; Federal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil., de Oliveira AKS; Federal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil., Correia da Silva AG; Federal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil., de Araújo Rangel Lopes W; Federal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil., de Almeida Oliveira PH; Federal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil., Freire da Silva E; Federal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil., Barros Júnior AP; Federal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil., Maria da Silveira L; Federal Rural University of the Semi-Arid, Mossoró, Rio Grande do Norte, Brazil. |
| المصدر: | Heliyon [Heliyon] 2023 Jul 11; Vol. 9 (7), pp. e17834. Date of Electronic Publication: 2023 Jul 11 (Print Publication: 2023). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Ltd Country of Publication: England NLM ID: 101672560 Publication Model: eCollection Cited Medium: Print ISSN: 2405-8440 (Print) Linking ISSN: 24058440 NLM ISO Abbreviation: Heliyon Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: London : Elsevier Ltd, [2015]- |
| مستخلص: | The estimative of the leaf area using a nondestructive method is paramount for successive evaluations in the same plant with precision and speed, not requiring high-cost equipment. Thus, the objective of this work was to construct models to estimate leaf area using artificial neural network models (ANN) and regression and to compare which model is the most effective model for predicting leaf area in sesame culture. A total of 11,000 leaves of four sesame cultivars were collected. Then, the length (L) and leaf width (W), and the actual leaf area (LA) were quantified. For the ANN model, the parameters of the length and width of the leaf were used as input variables of the network, with hidden layers and leaf area as the desired output parameter. For the linear regression models, leaf dimensions were considered independent variables, and the actual leaf area was the dependent variable. The criteria for choosing the best models were: the lowest root of the mean squared error (RMSE), mean absolute error (MAE), and absolute mean percentage error (MAPE), and higher coefficients of determination (R 2 ). Among the linear regression models, the equation y ˆ = 0.515 + 0.584 * L W was considered the most indicated to estimate the leaf area of the sesame. In modeling with ANNs, the best results were found for model 2-3-1, with two input variables (L and W), three hidden variables, and an output variable (LA). The ANN model was more accurate than the regression models, recording the lowest errors and higher R 2 in the training phase (RMSE: 0.0040; MAE: 0.0027; MAPE: 0.0587; and R 2 : 0.9834) and in the test phase (RMSE: 0.0106; MAE: 0.0029; MAPE: 0.0611; and R 2 : 0.9828). Thus, the ANN method is the most indicated and accurate for predicting the leaf area of the sesame. Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Joao Everthon da Silva Ribeiro reports administrative support, article publishing charges, equipment, drugs, or supplies, statistical analysis, travel, and writing assistance were provided by 10.13039/100017134Federal Rural University of the Semi-Arid. There was no financial interest. (© 2023 The Author(s).) |
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| فهرسة مساهمة: | Keywords: Leaf length; Leaf width; Machine learning; Multilayer perceptrons; Sesamum indicum L.; Simple linear regression |
| تواريخ الأحداث: | Date Created: 20230728 Latest Revision: 20230729 |
| رمز التحديث: | 20230729 |
| مُعرف محوري في PubMed: | PMC10368775 |
| DOI: | 10.1016/j.heliyon.2023.e17834 |
| PMID: | 37501953 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2405-8440 |
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| DOI: | 10.1016/j.heliyon.2023.e17834 |