Predicting Cone Production in Clonal Seed Orchard of Anatolian Black Pine With Artificial Neural Network

Gemici, Ercan; Yücedağ, Cengiz; Özel, H. B.; İmren, Erol

doi:10.15666/aeer/1702_22672273

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…Similarly to the present study, the high performance of ANN in obtaining agronomic estimates has been confirmed in several studies (Binoti et al 2013, Soares et al 2014, Soares et al 2015, Aquino et al 2016a, Aquino et al 2016b, Miguel et al 2016, Gemici et al 2019, Vitor et al 2019. Among the reasons associated with the efficiency of the ANN models, there is the average predictive error of less The predictive capacity of mathematical models based on morphological characteristics by ANN have been attested in several crops, such as Tropical banana (Soares et al 2014), corn (Soares et al 2015) and cactus pear (Guimarães et al 2018), with agreement indexes (relationship between the estimated and observed values) similar to the adjusted models for 'Prata-Anã' and 'BRS Platina' (Figure 1), equal to 0.91; 1.0; and 0.87, respectively.…”

Section: Resultssupporting

confidence: 88%

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Yield prediction of ‘Prata Anã’ and ‘BRS Platina’ banana plants by artificial neural networks1

Guimarães

Donato

Aspiazú

et al. 2021

Pesqui. Agropecu. Trop.

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Prediction models may contribute to data analysis and decision-making in the management of a crop. This study aimed to evaluate the feasibility of predicting the yield of ‘Prata-Anã’ and ‘BRS Platina’ banana plants by means of artificial neural networks, as well as to determine the most important morphological descriptors for this purpose. The following characteristics were measured: plant height; perimeter of the pseudostem at the ground level, at 30 cm and 100 cm; number of live leaves at harvest; stalk mass, length and diameter; number of hands and fruits; bunches and hands masses; hands average mass; and ratio between the stalk and bunch masses. The data were submitted to artificial neural networks analysis using the R software. The best adjustments were obtained with two and three neurons at the intermediate layer, respectively for ‘Prata-Anã’ and ‘BRS Platina’. These models presented the lowest mean square errors, which correspond to the higher proximity between the predicted and the real data, and, therefore, a higher efficiency of the networks in the yield prediction. By the coefficient of determination, the best adjustments were found for ‘Prata-Anã’ (R² = 0.99 for all the network compositions), while, for ‘BRS Platina’, the data adjustment enabled an R² with values between 0.97 and 1.00, approximately. Yield predictions for ‘Prata-Anã’ and ‘BRS Platina’ were obtained with high efficiency by using artificial neural networks.

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Section: Resultssupporting

confidence: 88%

“…The selection of robust tools has been a priority and, therefore, several studies have proposed alternatives to describe plant growth and behavior, especially with complex characteristics that are difficult to measure in the field (Azevedo et al 2015, Guimarães et al 2018, Gemici et al 2019.…”

Section: Introductionmentioning

confidence: 99%

Yield prediction of ‘Prata Anã’ and ‘BRS Platina’ banana plants by artificial neural networks1

Guimarães

Donato

Aspiazú

et al. 2021

Pesqui. Agropecu. Trop.

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“…Küçükönder, et al [42] pointed out that the ANN model can be used as an alternative method in estimating the leaf area of tomato plant. Also in a previous research, the amount of cone in clonal seed orchards of Anatolian black pine was predicted with high efficiency through artificial neural networks [43].…”

Section: Discussionmentioning

confidence: 98%

Selection of Superior Clones by the Multi-Dimensional Decision-Making Techniques in Scots Pine Seed Orchard

et al. 2021

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The living conditions of living beings are becoming ever more difficult due to the climate change caused by industrialization. Forests, which have a great importance in terms of natural resources, are one of the main elements which prevent this situation. Therefore, it is important to ensure the sustainability of forests and to increase their genetic and structural quality. Appropriate farms and clonal seed orchards should be established with the purpose of achieving this genetic diversity. This way, quantitative traits of clones, which are located in these seed orchards, depending on their growth performance, the cone yield can be determined. In this study, the best clones in terms of cone yield were determined through MAUT and WASPAS methods, which are some of the multiple criteria decision-making techniques. This was done by using the height and diameter measurements of 30 Scots pine (Pinus sylvestris L.) clones selected according to random sampling method in 3 different blocks in Erzurum region. Based on the sum product assessment and multi-attribute utility theory model results, clones 22 and 29 were determined as superior and prospective for further breeding procedures in terms of seedling height and root collar diameter. According to the entropy method, the maximum weights for seedling height and root collar diameter were obtained in Block-3 with 0.580175 and in Block-1 with 0.590017, respectively. Contribution/Originality:This study plays important role in selecting the best clones in terms of cone yield through MAUT and WASPAS methods, which are some of the multiple criteria decision-making techniques.

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“…The image processing for the defect detection of the samples is performed using four different median filters (Adaptive Median Filter, 2D Hybrid Median Filter, 2D Adaptive Log Gabor, 2D Adaptive Anisotropic Bilateral Diffusion Filter). The images for the image processing technique were procured from our previous study pertaining to the (LW) and (TIG) welding of (SS 304) [48].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of machine learning techniques for the Nd: YAG Laser & TIG welded stainless steel 304

Kumar,

Krishna,

Hasanabadi

et al. 2024

FME Transactions

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Nd: YAG Laser and Tungsten Inert Gas (TIG) welding processes are the most promising joining techniques used for stainless steel (SS) alloys due to their significant weld characteristics. In this study, the effect of two process parameters (weld power and travel speed) on the mechanical properties (ultimate tensile strength and microhardness) of the weldment is investigated. Two different machine learning techniques, namely Adaptive Neuro-Fuzzy Inference System (ANFIS) and Unified Convolutional Neural Network (UCNN) are also evaluated for prediction of mechanical properties and defect detection through the image processing technique, respectively. A correlation has been performed between these two machine learning approaches with the experimental values. The training data sets are developed for the machine learning techniques, and the obtained results of (ANFIS) and (UCNN) models are related to the actual experimental values. The output of both developed models (ANFIS & UCNN) showed a good agreement with the actual experimental test results. The predicted tensile and microhardness values from the (ANFIS) model were found to greatly agree with the Peak Signal-to-Noise Ratio (PSNR) values from the (UCNN) model. However, owing to the increase in the applications of welding processes in industries, the utilization of machine learning techniques would be more efficient when compared with the other traditional methods that are being adopted.

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Predicting Cone Production in Clonal Seed Orchard of Anatolian Black Pine With Artificial Neural Network

Cited by 5 publications

References 10 publications

Yield prediction of ‘Prata Anã’ and ‘BRS Platina’ banana plants by artificial neural networks1

Yield prediction of ‘Prata Anã’ and ‘BRS Platina’ banana plants by artificial neural networks1

Selection of Superior Clones by the Multi-Dimensional Decision-Making Techniques in Scots Pine Seed Orchard

Evaluation of machine learning techniques for the Nd: YAG Laser & TIG welded stainless steel 304

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