Increased drought tolerance and improved nutrient absorption are two ways to improve agricultural energy efficiency and reduce the demand for energy intensive fertilizer production. Both of these features are directly related to the root system architecture - the shape and arrangement of roots in soil and thus can be improved by plant breeding for improved root systems. However, accurately measuring the roots of different plants is a major challenge. Laboratory methods limit the number of plants that can be easily studied, the size to which the plants can grow, and the type of environments to which the plants are exposed. Alternatively, field-deployable methods have the drawback that root measuring techniques require digging up the plant, which is labor intensive and prevents multiple measurements of the same plant. New methods to measure root system architecture in the field at different times in the growth cycle will help researchers identify which root structures lead to greater agricultural efficiency in biomass production for biofuels.
Project Innovation + Advantages:
Hi Fidelity Genetics will develop a low-cost device to measure the characteristics of plant roots and the environmental conditions that affect their development. Their device, called the "RootTracker," is a cylindrical, cage-like structure equipped with sensors on the rings of the cage. Before a seed is planted, farmers can push or twist the RootTracker directly into the soil. A seed is then planted at the top of the cage, allowing the plant to grow naturally while sensors accurately measure root density, growth angles, and growth rates, while having minimal impact on the growth of the plant. The prototype includes additional sensors attached to a removable, reusable rod to monitor environmental conditions. Data gathered by the device can be transmitted wirelessly or recorded internally using a low-cost microcontroller charged by solar power. The main technical challenge is automatically adjusting the calibration of the sensors, which are affected by soil type, soil moisture, and other environmental conditions that can disrupt the signal produced by the sensor. Another challenge is to distinguish between different types of biological matter. The team will also develop software for processing the data generated by the device and conduct laboratory and field tests to assess the performance of the prototype. Data collected by the device will help breeders further optimize root system architecture, which should lead to more energy-efficient crop varieties.