Mass Estimation of Soft Fruit via Oscillatory Plant Dynamics

Nikolaus Wagner, Grzegorz Cielniak

Abstract: Yield forecasting is an essential task in modern agriculture, as it enables farmers and food economists to manage crop and its distribution precisely and effectively. Traditionally, most methods for yield forecasting are based on historical data and yield estimates from manually collected samples. More modern approaches often rely on computer vision-based fruit counting algorithms, which do not take individual crop weights into account.In this paper, we propose a novel, non-destructive method to estimate the mass of individual pieces of fruit by exploiting the dynamic properties of plants. By observing short-term oscillatory plant motion through RGB-D video data, we formulate an approach for mass estimation based on determining the parameters of a damped harmonic oscillator model.We test the proposed algorithm by collecting a dataset of around 300 video samples of strawberries on a real strawberry farm and apply our method. With a semi-automated toolchain, capable of inferring the key parameters from video data and calculating the mass of individual berries from those, we were able to estimate the mass of all berries in our dataset with a median error of 1.16g, outperforming a baseline utilising vision-based volume estimation to infer the mass. These insights hold valuable improvements for the development of yield forecasting systems and selective harvesters, which help to address the sustainability of food production and labour shortages.