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. 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. 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.
New airborne LiDAR (Light Detection and Ranging) measurement systems, like the FLI-MAP 400 System,
make it possible to obtain high density data containing far more information about single objects, like trees,
than traditional airborne laser systems. Therefore, it becomes feasible to analyze geometric properties of trees
on the individual object level. In this paper a new 3-step strategy is presented to calculate the stem diameter of
individual natural trees at 1.3m height, the so-called breast height diameter, which is an important parameter
for forest inventory and flooding simulations. Currently, breast height diameter estimates are not obtained from
direct measurements, but are derived using species dependent allometric constraints. Our strategy involves three
independent steps: 1. Delineation of the individual trees as represented by the LiDAR data, 2. Skeletonization
of the single trees, and 3. Determination of the breast height diameter computing the distance of a suited subset
of LiDAR points to the local skeleton. The use of a recently developed skeletonization algorithm based on
graph-reduction is the key to the breast height measurement. A set of four relevant test cases is presented and
validated against hand measurements. It is shown that the new 3-step approach automatically derives breast
height diameters deviating only 10% from hand measurements in four test cases. The potential of the introduced
method in practice is demonstrated on the fully automatic analysis of a LiDAR data set representing a patch of
forest consisting of 49 individual trees.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.