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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Natalia Angeline
Universitas Multimedia Nusantara

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Faster region-based convolutional neural network for plant-parasitic and non-parasitic nematode detection Natalia Angeline; Nabila Husna Shabrina; Siwi Indarti
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp316-324

Abstract

Nematodes represent very abundant and the largest species diversity in the world. Nematodes, which live in a soil environment, possess several functions in agricultural systems. There are two huge groups of soil nematodes, a non-parasitic nematode, which contributes positively to ecological processes, and a plant-parasitic nematode, which cause various disease and reduces yield losses in the agricultural system. Early detection and classification in the agricultural area infected with plant-parasitic nematode and interpreting the soil level condition in this area required a fast and reliable detection system. However, nematode identification is challenging and time-consuming due to their similar morphology. This study applied a pre-trained faster region-based convolutional neural network (RCNN) for plant-parasitic and non-parasitic nematodes detection. These deep learning-based object detection models gave satisfactory results as the accuracy reached 87.5%.
Co-Authors Shabrina, Nabila Husna Siwi Indarti