A Novel Approach for Plant Root Hair Counting and its Improvement via Image Super-Resolution
Root hair counting is a specialized aspect of plant biology and agronomy research that offers valuable insights into plant health, nutrient uptake, and overall growth potential. Root hairs are tiny extensions from the root epidermis that significantly increase the surface area and constitute roughly 70% of the total root area of a plant root system, enhancing the ability of plants to absorb water and nutrients from the soil. Understanding the importance of root hair counting involves looking at various aspects of plant physiology and soil–plant interactions. Despite these benefits, counting root hairs, especially manually, can be tedious, time-consuming, and, more often, inaccurate due to differences in the perception of individuals. Therefore, we have proposed a novel method for root hair counting and further observed an improvement in root hair count measurements when utilizing image super-resolution as a preprocessing step. Our approach of counting root hairs can tackle real-world challenges and be able to count overlapping hairs as well. By visualizing the rhizosphere in binary space, we can see a considerable increase in root hair count from 37 to 68 when counting manually to our approach for Bell pepper, and from 44 to 88, when counting manually to our method for Arabidopsis root images. To the best of the authors’ knowledge, this research study is specifically designed for root hair counting and measurement improvement using super-resolution, is the first of its kind, and has yet to be acknowledged.
Authors
- Sharon Chemweno (ORCID: https://orcid.org/0000-0001-9940-0554)
- Divya Mishra (ORCID: https://orcid.org/0000-0002-7332-4538)
- Ofer Hadar (ORCID: https://orcid.org/0000-0002-6089-8401)
- Naftali Lazarovitch (ORCID: https://orcid.org/0000-0002-3630-5696)
- Jhonathan E. Ephrath (ORCID: https://orcid.org/0000-0001-5583-1969)
- Ofek Nourian (ORCID: https://orcid.org/0009-0008-2900-6365)
Institutions
- Ben-Gurion University of the Negev (IL)
- University of Petroleum and Energy Studies (IN)
Publication Details
- Journal
- IEEE Transactions on AgriFood Electronics
- Published
- 2025-06-11
- DOI
- https://doi.org/10.1109/tafe.2025.3565690
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00