A Contactless Classification Method for Early Detection of Nematodes Using Deep Neural Networks (DNNs) and TensorFlow

Abstract

Soil-borne plant-parasitic nematodes are microscopic, eellike roundworms. The root-knot nematodes (Meloidogyne spp.) and root-lesion nematodes (Pratylenchus vulnus) are among the most damaging in California, which are difficult to control and can spread easily in soil on tools, boots, and infested plants. Root-knot nematodes can attack many different crops, including nut and fruit trees, usually cause unusual swellings, called galls, on affected plants’ roots. It is not easy to recognize the infestations of these nematodes. For instance, researchers need to dig up walnut trees with symptoms, wash or gently tap the soil from the roots, and examine the roots for galls. The nematode extraction procedures, identification, and enumeration under a microscope are tedious and time-consuming. Therefore, in this article, the authors proposed to use a low-cost contactless radio frequency tridimensional sensor “Walabot,” and Deep Neural Networks (DNNs), to perform the early detection of nematodes in a walnut site. Radiofrequency reflectance of walnut leaves from different nematode infestation levels was measured. The hypothesis was that waveforms generated from walnut leaves can estimate the damage caused by nematodes. DNNs with Tensor-Flow were used to train and test the proposed method. Results showed that the Walabot predicted nematode infestation levels with an accuracy of 82%, which showed great potentials for early detection of nematodes.