PosNet: Estimating lettuce fresh weight in plant factory based on oblique image

Abstract

Fresh weight is a crucial indicator for assessing crop growth in plant factory. To date, the majority of non-destructive techniques employed for estimating crop fresh weight rely on the top view images. Nevertheless, these approaches are limited in performance due to the constrained and non-open environment of plant factories. In this paper, we propose a novel position-guided network (PosNet) to estimate the fresh weight of crop using oblique view images. Precisely, we first build the crop (i.e., lettuce) dataset by positioning the camera vertically in an oblique angle, with the Mask R-CNN framework trained for individual lettuces segmentation from the background. The segmented lettuce images are then fed into the PosNet for network training and testing purpose. By integrating the shallow feature extraction module and the position information extraction module, the proposed PosNet attains superior performance on assessing the lettuce fresh weight from oblique view images against other models. We further conducted the ablation studies and generalization testing to verify the efficacy and robustness of the proposed network model. Moreover, by comparing three variations (i.e., position, growth stage and posture orientation) of the lettuce separately and the sensitivity analysis of oblique shooting angles, our method demonstrates plausible adaptability for lettuce fresh weight estimation. Taking the accuracy, robustness, generalization capability, and adaptability into account, the integration of PosNet with oblique images not only enjoys great potentials in assessing the fresh weight, but also provides a practical support for agronomic management of crops cultivated in plant factories.