Data-driven Root Tuber Biomass Estimation via a Wireless Network

Below-ground biomass (BGB) of root tubers is an important phenotypic trait in crop monitoring and other agricultural applications. This paper proposes a novel tuber biomass sensing (TBS) framework that uses internet of things (IoT) devices to enable non-destructive estimation of below-ground root tuber biomass. Specifically, we perform extensive experiments to build a new BGB dataset with more than 700,000 received signal strength (RSS) measurements collected by our low-cost wireless network. Then, we propose a novel data-driven model that integrates convolution neural networks, residual connections, and attention mechanisms to facilitate discriminative feature extraction from RSS data and achieve state-of-the-art (SOTA) performance in biomass estimation. In addition, to mitigate performance degradation caused by imbalanced training data, we propose a contrastive learning method that aligns feature representations of samples with similar biomass values while increasing the separation between those with significantly different values. This method reduces estimation bias toward high-frequency biomass labels, thereby improving the performance and generalizability of the data-driven model. Experimental results demonstrate the efficacy of the proposed TBS framework. Our dataset and pre-trained models are publicly available on https://zenodo.org/records/15000852.