Rowland, Diane L. | Search NASA

DOE OSTI2021

Entrepreneurial talent building for 21st century agricultural innovation.

Agricultural innovation is a key component of the global economy and enhances food security, health, and nutrition. Current innovation efforts focus mainly on supporting the transition to sustainable food systems, which is expected to harness technological advances across a range of fields. In this Nano Focus, we discuss how such efforts would benefit from not only supporting farmer participation in deciding transition pathways but also in fostering the interdisciplinary training and development of entrepreneurial-minded farmers, whom we term "AgTech Pioneers", to participate in cross-sector agricultural innovation ecosystems as cocreators and informed users of developing and future technologies. Toward this goal, we discuss possible strategies based on talent development, cross-disciplinary educational and training programs, and innovation clusters to build an AgTech Pioneer ecosystem, which can help to reinvigorate interest in farming careers and to identify and address challenges and opportunities in agriculture by accelerating and applying advances in nanoscience, nanotechnology, and related fields.

Yoon, Bo Kyeong↗

DOE OSTI2020

Overcoming small minirhizotron datasets using transfer learning

Minirhizotron technology is widely used to study root growth and development. Yet, standard approaches for tracing roots in minirhiztron imagery is extremely tedious and time consuming. Machine learning approaches can help to automate this task. However, lack of enough annotated training data is a major limitation for the application of machine learning methods. Transfer learning is a useful technique to help with training when available datasets are limited. In this paper, we investigated the effect of pre-trained features from the massives-cale, irrelevant ImageNet dataset and a relatively moderate-scale, but relevant peanut root dataset on switchgrass root imagery segmentation applications. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. Deep neural networks based on the U-net architecture were used with different pre-trained features as initialization for automated, precise pixel-wise root segmentation in minirhizotron imagery. We observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset were more effective than features pre-trained on the large but unrelated ImageNet dataset. Here, we achieved high quality segmentation on peanut root dataset with 99.04% accuracy at the pixel-level and overcame errors in human-labeled ground truth masks. By applying transfer learning technique on limited switchgrass dataset with features pre-trained on peanut dataset, we obtained 99% segmentation accuracy in switchgrass imagery using only 21 images for training (fine tuning). Furthermore, the peanut pre-trained features can help the model converge faster and have much more stable performance.

59 BASIC BIOLOGICAL SCIENCES↗