Parasitic nematodes can cause up to 20 percent of annual yield losses for walnut growers. But Haoyu Niu is aiming to curb those losses through the early detection of nematode infestations with Scio, combining cutting-edge field devices with machine learning algorithms. View Halo Profile >>
Tell us about your research
My research focuses on assessing the performances of Scio in a classification model for nematodes infection levels, which has shown improved classification accuracy for early detection of those levels. By using the Neural Networks model, it can classify nematode infection levels with an accuracy of 72 percent to this point. The results show that there might be a strong relationship between the near-infrared reflectance and nematode infection levels.
Can you explain that to a non-scientist?
A low-cost, pocket-sized, cutting-edge micro-spectrometer, Scio, serves as a novel proximate sensor on early detection of nematode infestation levels with machine learning algorithms, such as Neural Networks, Support Vector Machines, Random Forest, and so on. The programmable Scio will be used to measure the reflectance of the near-infrared band of the walnut leaves. Nematode infection levels will be analyzed based on the reflectance from walnut leaves.
A low-cost, pocket-sized, cutting-edge micro-spectrometer, Scio, serves as a novel proximate sensor on early detection of nematode infestation levels with machine learning algorithms
Why did you choose this area of research?
Soil-borne plant-parasitic nematodes exist in many soils. Some of them can cause up to 15 to 20 percent annual yield losses. Walnut has high economic value, and most edible walnuts in the US are produced in the fertile soils of the California Central Valley. Soil-dwelling nematode parasites are a significant threat and cause severe root damage and affect the walnut yields. Early detection of plant-parasitic nematodes is critical to design management strategies.
Soil-dwelling nematode parasites are a significant threat and cause severe root damage and affect the walnut yields. Early detection of plant-parasitic nematodes is critical to design management strategies.
How could your Grants4Ag project someday impact #healthforall #hungerfornone?
Early detection of nematode infestation is important in the walnut industry for management decision support. To further assess the role of Scio sensor and machine learning algorithms for early detection of nematodes levels, we will design more field experiments. Being able to detect the nematodes will enable growers to manage walnut production more sustainably. Less pesticide will be used, which will make the walnut industry more environmentally friendly.