Optimizing Linseed ( Linum usitatissimum L.) Seed Yield through Agronomic Parameter Modeling via Artificial Neural Networks

Aliakbar Mohammadi Mirik, Mahdieh Parsaeian, Abbas Rohani () and Shaneka Lawson
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Aliakbar Mohammadi Mirik: Department of Plant Genetics and Production, Vali-e-Asr University of Rafsanjan, Rafsanjan 7718897111, Iran
Mahdieh Parsaeian: Department of Agronomy and Plant Breeding, Shahrood University of Technology, Shahrood P.O. Box 316-36155, Iran
Abbas Rohani: Department of Biosystem Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
Shaneka Lawson: USDA Forest Service, Northern Research Station, Hardwood Tree Improvement and Regeneration Center (HTIRC), PFEN226C, Department of Forestry and Natural Resources, Purdue University, 715 West State Street, West Lafayette, IN 47907, USA

Agriculture, 2023, vol. 14, issue 1, 1-21

Abstract: Linseed ( Linum usitatissimum L.), a globally cultivated oilseed crop in high demand, is the focal point of our efforts aimed at improving yield production. The achievement of robust yield outcomes relies on the intricate interplay of various agronomic traits. This study, conducted over two years at a research farm in Iran, presents a comprehensive analysis evaluating diverse agronomic characteristics inherent to different linseed cultivars and hybrids. Essential parameters, including days to emergence, days to flowering, plant height, number of branches, number of capsules per plant, number of seeds per capsule, 1000-seed weight, and seed yield per plant, were examined. For predictive insights into seed yield, machine learning techniques, specifically multilayer perceptron (MLP) and multiple linear regression (MLR), were employed. The analysis of contribution percentages for each agronomic variable to linseed seed yield revealed that the number of capsules per plant emerged as the most influential factor, contributing 30.7% among the considered variables. The results indicated the superiority of MLP over MLR, with RMSE and MAPE values equaling 0.062 g/plant and 3.585%, respectively. Additionally, R 2 values for training, validation, and test phases exceeded 0.97. Consequently, MLP served as a merit function in the genetic algorithm (GA), targeting the identification of optimal trait levels to maximize linseed yield. The optimization outcomes demonstrated the potential achievement of a yield of 4.40 g/plant. To attain this performance, a set of agronomic characteristic values was proposed by GA, initiating a discussion on genetic modification possibilities. The findings of this study highlight the remarkable efficacy of machine learning tools, particularly neural networks, when paired with evolutionary optimization techniques such as genetic algorithms. These methodologies prove to be invaluable assets in aiding biotechnologists as they strive to enhance the genetic makeup of products for various applications, providing unwavering reliability and invaluable guidance in the pursuit of genetic modification endeavors.

Keywords: artificial neural network; genetic algorithm; seed yield; linseed (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: 2023
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