Open AccessCereal fungal diseases detection using autoencoders
Author(s) -
Irina Arinicheva,
Igor V. Arinichev,
Zhenny D. Darmilova
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/949/1/012048
Subject(s) - identification (biology) , autoencoder , disease , agriculture , artificial intelligence , computer science , task (project management) , pipeline (software) , plant disease , artificial neural network , pattern recognition (psychology) , microbiology and biotechnology , medicine , biology , engineering , pathology , ecology , systems engineering , programming language
According to the data of the Food and Agriculture Organization of the United Nations (FAO), diseases and pests destroy 20-40% of the world’s agricultural crops. At the same time, fungal diseases cause enormous economic damage. Farmers suffer significant financial losses every year due to fungal diseases. It is very important accurately and at an early stage to identify the symptoms of the disease in order to take the necessary measures to combat it in a timely manner. Symptoms of fungal diseases often appear in the form of spots around the infected areas, so the initial detection of the disease is reduced to the analysis of these spots. At present, farmers mainly rely on their own experience, disease-identifying atlases or involve expert agronomists. However, the identification is complicated by the fact that different diseases can have similar types of spots and vice versa, the same disease can manifest itself differently in different crops varieties and depending on growing conditions. This research shows that modern neural network approaches can be used instead of conventional colour and brightness filters to detect fungal infections in cereal crops. In particular, the use of autoencoders can significantly simplify the task of automatically detecting diseased areas. The authors prove that after complementing the pipeline of image processing using filters, usual for such tasks, with a neural network autoencoder, in some cases it is possible to explicitly highlight the affected area.