Development of a Deep Learning‐Assisted Mobile Application for the Identification of Nematodes Through Microscopic Images

Abstract

Nematodes are microscopic metazoans, some species of which can be used as biological insecticides, while some other species annually damage 10.0%–20.0% of crops globally. Accurate identification of nematodes is crucial for their effective utilisation or control. Current methods of nematode identification are labour‐intensive, time‐consuming, and prone to false positives, thus necessitating the development of an intelligent system for their identification from microscopic images without technical assistance. In this study, a novel approach was investigated for the identification of nematodes from microscopic images. A novel lightweight convolutional neural network (CNN) was developed to identify the nematodes belonging to different trophic groups (Heterorhabditis indica, Meloidogyne incognita, Helicotylenchus, Anguina tritici, and Xiphinema). The CNN model was trained for 75 epochs using 70.0% of the nematode dataset, with validation on 20.0% of the dataset. To ensure unbiased evaluation, 30 images from each class were randomly selected from the remaining 10.0% of the dataset for testing the classification performance of the trained model. The trained models achieved an average classification accuracy, precision, recall, and F1‐score values of 98.52%, 95.66%, 95.56%, and 95.56%, respectively. The proposed CNN was found to be four times faster and five times lighter against a marginal (< 1.0%) decrease in accuracy when compared with the existing state‐of‐the‐art CNNs. The classification accuracy of the developed mobile application was validated by nematode specialists on freshly captured data and found to be greater than 98.0%. Hence, the developed mobile application can be effectively applied for the identification of targeted nematodes and eliminate the necessity of specialists.