Prediction of Pea (Pisum sativum L.) Seeds Yield Using Artificial Neural Networks

Piekutowska, Magdalena; Niedbała, Gniewko

doi:10.3390/agriculture13030661

Cited by 8 publications

(1 citation statement)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rosado et al (2020) stated that using the ANN-MLP neural network for bean genetic prediction using phenotypic and genetic traits allowed model accuracy to reach higher by 90%, and also stated that the Neuroscience Network provided the ability to use quantitative features to approximate the prediction to the true value [39]. Also, the results of studies on different crop plants show the e ciency of using neural network for crop performance [40,41].…”

Section: Model Performancementioning

confidence: 99%

Investigation Of Genetic Diversity Of Different Rapeseed (brassica napus l.) Genotypes And Yield Prediction Using Machine Learning Models

Norouzi

Ahangar

Payghamzadeh

et al. 2023

Preprint

View full text Add to dashboard Cite

Background Seed yield is controlled by additive and non-additive effects of genes, so predicting seed yield is one of the most important goals of rapeseed breeding in agricultural research. However, there is less information about the yield estimation of canola using neural network. In this research, three models of Multi-Layer Perceptron (MLP) neural network, Radial Basis Function (RBF) neural network and Support Vector Machine (SVM) were used to predict rapeseed yield. Network training was performed using phenological, morphological, yield and yield components, as well as data obtained from molecular markers of 8 genotypes and 56 hybrids. Results The obtained from the comparison of the efficiency of the models showed that the MLP model was able to predict the hybrid yield with the RMSE, MAE and R2 equal to 226, 183 and 92% and the use of phenotypic data as model inputs in direct crosses with the highest accuracy. In the genetic evaluation section, according to the indicators obtained, it was found that molecular study is a powerful tool that can provide valuable information to the breeder. The results showed that among the 40 primers investigated, the ISJ10 primer had more resolving power than the other primers. Conclusions The use of molecular and phenotypic data as input data in the model showed that the MLP model had a lower error value in terms of RMSE and MAE and a higher R2 than direct crosses in predicting the performance of reciprocal crosses. The proposed neural network model makes it possible to estimate the performance of each of the hybrids of the parents studied before crossing, which helps the breeder to focus on the best possible hybrids.

show abstract

Section: Model Performancementioning

confidence: 99%

Investigation Of Genetic Diversity Of Different Rapeseed (brassica napus l.) Genotypes And Yield Prediction Using Machine Learning Models

Norouzi

Ahangar

Payghamzadeh

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Estimation and Classification of Physical Parameters Pumpkins (Cucurbita pepo L.) Crop S by Soft Computing Tecniques

Yildirim,

Yesiloglu Cevher,

Gurdil Gürkan

2024

BIO Web Conf.

View full text Add to dashboard Cite

Determining the seed type is very important for the correct indentification of genetic material. Some plant seeds can not be classified based on their visual diversity or small size by experts. Therefore, in this study was to develop a simple, accurate and rapid using different soft computing tecniques that estimates physical parameters for pumpkin seeds. The current investigation was devoted to determining some properties, such as physical dimensions, surface area, sphericity, density, rupture energy of pumpkin seeds. The methods using in this study are; (1) Multilayer perceptron (MLP); (2) Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Different statistic parameters such as coffecient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) are used to evaluate performance of the methods. These selected the best models predicted for plant seeds which can be used in the soft computing tecniques determined alternative approach to estimating the physical properties of estimation and clasification pumpkin seeds.

show abstract

Digital Innovations in Agriculture

Niedbała,

Kujawa

2023

Agriculture

View full text Add to dashboard Cite

show abstract

Prediction of Pea (Pisum sativum L.) Seeds Yield Using Artificial Neural Networks

Cited by 8 publications

References 73 publications

Investigation Of Genetic Diversity Of Different Rapeseed (brassica napus l.) Genotypes And Yield Prediction Using Machine Learning Models

Investigation Of Genetic Diversity Of Different Rapeseed (brassica napus l.) Genotypes And Yield Prediction Using Machine Learning Models

Estimation and Classification of Physical Parameters Pumpkins (Cucurbita pepo L.) Crop S by Soft Computing Tecniques

Digital Innovations in Agriculture

Contact Info

Product

Resources

About