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Artificial Neural Network Based Apple Yield Prediction Using Morphological Characters

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Title Artificial Neural Network Based Apple Yield Prediction Using Morphological Characters
Not Available
 
Creator Bharti
Pankaj Das
Rahul Banerjee
Tauqueer Ahmad
Sarita Devi
Geeta Verma
 
Subject apple fruit crop
morphological characters
yield prediction
machine learning
principal component analysis (PCA)
artificial neural network (ANN)
 
Description Not Available
The yield of the crop is a complex function of a number of dependent traits, which
makes yield prediction a statistically difficult task. A number of work on yield prediction using
morphological characters already exists in the literature. Most of the work used statistical techniques
such as linear regression and crop yield models, which assume a linear relationship between yield
and the morphological traits; in actual practice, such a linear relationship is seldom achieved. With
the advancement in the field of machine learning techniques, these methods can provide a viable
alternative for dealing with nonlinear relationships for yield prediction. Globally, apples are the most
consumed fruit. In this paper, attempts have been made to predict the yield of the apple crop using
morphological traits. PCA was used for selection of the significant variables. These variables were
later used as input variables in the ANN model with different hidden layers for predicting crop yield.
The predictive performance of the model was evaluated using standard statistical tests. Sensitivity
analysis was performed to find out the individual effects of each character on the apple yield. The
study contributes to a better understanding of the complex relationships between crop yield and
morphological traits.
Not Available
 
Date 2023-04-03T03:31:48Z
2023-04-03T03:31:48Z
2023-03-28
 
Type Article
 
Identifier Bharti; Das, P.; Banerjee, R.; Ahmad, T.; Devi, S.; Verma, G. Artificial Neural Network Based Apple Yield Prediction Using Morphological Characters. Horticulturae 2022, 9, 436. https:// doi.org/10.3390/horticulturae9040436
2311-7524
http://krishi.icar.gov.in/jspui/handle/123456789/76645
 
Language English
 
Relation Not Available;
 
Publisher MDPI