Machine-Learning Approach to Non-Destructive Biomass and Relative Growth Rate Estimation in Aeroponic Cultivation

Oskar Åström, Henrik Hedlund and Alexandros Sopasakis ()
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Oskar Åström: Department of Mathematics, Faculty of Science, Lund University, 221 00 Lund, Sweden
Henrik Hedlund: Alovivum AB, Göingegatan 6, 222 41 Lund, Sweden
Alexandros Sopasakis: Department of Mathematics, Faculty of Science, Lund University, 221 00 Lund, Sweden

Agriculture, 2023, vol. 13, issue 4, 1-13

Abstract: We train and compare the performance of two machine learning methods, a multi-variate regression network and a ResNet-50-based neural network, to learn and forecast plant biomass as well as the relative growth rate from a short sequence of temporal images from plants in aeroponic cultivation. The training dataset consists of images of 57 plants taken from two different angles every hour during a 5-day period. The results show that images taken from a top-down perspective produce better results for the multi-variate regression network, while images taken from the side are better for the ResNet-50 neural network. In addition, using images from both cameras improves the biomass estimates from the ResNet-50 network, but not those from the multi-variatemultivariate regression. However, all relative growth rate estimates were improved by using images from both cameras. We found that the best biomass estimates are produced from the multi-variate regression model trained on top camera images using a moving average filter resulting in a root mean square error of 0.0466 g. The best relative growth rate estimates were produced from the ResNet-50 network training on images from both cameras resulting in a root mean square error of 0.1767 g/(g·day).

Keywords: machine learning; aeroponics; hydroculture; neural network; regression; biomass; fresh weight; relative growth rate; image analysis (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
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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