Pocket-sized micro-spectrometer for early detection of nematode infestation in walnut trees
A low-cost, pocket-sized near-infrared micro-spectrometer (Scio) combined with machine learning algorithms for early detection of nematode infestation levels in walnut trees. The MESA Lab at Texas A&M has achieved 72% classification accuracy using Neural Networks, demonstrating a strong relationship between leaf near-infrared reflectance and nematode infection levels.
Overview
This solution leverages a pocket-sized, low-cost near-infrared micro-spectrometer (Scio) as a novel proximate sensor for the early detection of nematode infestation in walnut trees. By measuring the near-infrared reflectance of walnut leaves and applying machine learning algorithms, the technology enables rapid, non-destructive field assessment of nematode infection levels. Early detection is critical for management decision support in the walnut industry, allowing growers to take timely action to protect crop health and yield.
Technical specifications
Core technology:
- Scio pocket-sized micro-spectrometer for near-infrared reflectance measurement
- Programmable sensor capable of capturing spectral data from walnut leaves
Machine learning models evaluated:
- Neural Networks
- Support Vector Machines
- Random Forest
- PCA (Principal Component Analysis)
Validation methodology:
- 30 sampling trees across 6 different blocks measured in 2018
- Each tree measured three times to reduce errors and anomalous results
- Trees classified into three infestation levels based on root-lesion nematode counts
- Data trained using scikit-learn algorithms
- Highest classification accuracy of 72% achieved with PCA and Neural Networks models
Technology readiness level
The technology is currently at an early-to-mid stage of development. Initial field validation has demonstrated proof of concept with 72% classification accuracy for distinguishing nematode infestation levels. The MESA Lab at Texas A&M University plans to conduct additional field experiments over a one-year period, collecting more training data at UC Kearny to improve model accuracy. Further validation and larger datasets are needed to advance the technology toward broader commercial deployment.
About Texas A&M University, College Station
Texas A&M University in College Station is a comprehensive public research university and the flagship of The Texas A&M University System, combining broad academic strengths with a strong applied‑research culture. Industry collaborates on the Texas A&M‑RELLIS campus—an integrated education, research and testing environment that supports large‑scale experimentation and proving grounds—and through the Texas A&M Transportation Institute’s facilities in Bryan‑College Station. A statewide extension network connects university expertise to companies and communities across all Texas counties, enabling rapid piloting and deployment. Research is supported by competitive federal funding from agencies such as NSF, NIH, DOE, USDA and DoD, alongside state and industry sponsorship. Texas A&M Innovation provides IP management, licensing and commercialization pathways across the system.
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