Artificial Neural Network Model for Predicting Carrot Root Yield Loss in Relation to Mechanical Heading

Piotr Rybacki (), Przemysław Przygodziński, Andrzej Osuch, Ewa Osuch and Ireneusz Kowalik
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Piotr Rybacki: Department of Agronomy, Faculty of Agronomy, Horticulture and Biotechnology, Poznan University of Life Sciences, Dojazd 11, 60-632 Poznan, Poland
Przemysław Przygodziński: Department of Agronomy, Faculty of Agronomy, Horticulture and Biotechnology, Poznan University of Life Sciences, Dojazd 11, 60-632 Poznan, Poland
Andrzej Osuch: Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-637 Poznan, Poland
Ewa Osuch: Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-637 Poznan, Poland
Ireneusz Kowalik: Department of Agronomy, Faculty of Agronomy, Horticulture and Biotechnology, Poznan University of Life Sciences, Dojazd 11, 60-632 Poznan, Poland

Agriculture, 2024, vol. 14, issue 10, 1-18

Abstract: Modelling and predicting agricultural production processes have high cognitive and practical values. Plant growth, the threat of pathogens and pests, and the structure of agricultural machinery treatments are mostly non-linear, measurable processes that can be described mathematically. In this paper, a multiple regression analysis was carried out in the first step to check the non-linearity of the data and yielded a coefficient of determination of R 2 = 0.9741 and the coefficient of determination corrected for degrees of freedom was R 2 adj = 0.9739. An artificial neural network model, called CH-NET, is then presented to predict the yield loss of carrot roots by leaving root mass in the field during harvest at the mechanical heading stage. The proposed network model has an architecture consisting of an input layer, three hidden layers with 12 neurons each, and an output layer with one neuron. Twelve input criteria were defined for the analysis and testing of the network, eight of which related to carrot root parameters and four to the heading machine. The training, testing, and validation database of the CH-NET network consisted of the results of field trials and tests of the operation of the patented (P.242097) root heading machine. The proposed CH-NET neural network model achieved global error (GE) values of 0.0931 t·ha −1 for predicting carrot root yield losses for all twelve criteria adopted. However, when the number of criteria is reduced to eight, the error increased to 0.0991 t·ha −1 . That is, the prediction was realised with an accuracy of 90.69%. The developed CH-NET model allows the prediction of economic losses associated with root mass left in the field or contamination of the raw material with undercut leaves. The simulations carried out showed that minimum root losses (0.263 t·ha −1 ) occur at an average root head projection height of 38 mm and a heading height of 20 mm from the ridge surface.

Keywords: yield loss prediction; carrot roots; neural networks; artificial intelligence; mechanical heading (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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