Non-destructive underground fiber Bragg grating sensing system with ResNet prediction for root phenotyping

Abstract

The quantitative evaluation of plant organs in a non-destructive and continuous fashion is the technological bottleneck to meet the food, fuel, and fiber needs for the 10 billion people on earth by 2050 [1]. Quantifying crop root architecture paves promising ways to improve resource uptake in the face of the resource limitations in the degraded soils of future climates [2]. Current root measurement methods either have low resolution or involve uprooting the plant. In all cases, the measurement methods do not provide any prediction on how well the plant is growing. We propose the usage of three fiber Bragg gratings (FBG) embedded within soil to measure underground strain change due to pseudo-root growth and a Residual Neural Network (ResNet) to predict its characteristics in a non-destructive fashion. To generate large amounts of sensor data similar to that of a growing root, we developed an automated robot that inserts pseudo-roots of 1mm and 5mm in diameter to 15cm below the soil's surface over the span of 11 minutes. We used 2,582 and 240 samples in training of the diameter and depth models, while testing was performed using 646 and 60 samples. The models were able to achieve accuracy of 92% and 93% for diameter and depth prediction, respectively. Through transfer learning, our base models will be expanded so that real time prediction on actual plant roots diameter and depth can be achieved.