Details

Autor(en) / Beteiligte

• Goncharov, Pavel
• Uzhinskiy, Alexander
• Ososkov, Gennady
• Nechaevskiy, Andrey
• Zudikhina, Julia
• Adam, Gh
• Buša, J.
• Hnatič, M.

Titel

Deep Siamese Networks for Plant Disease Detection

Ist Teil von

• EPJ Web of Conferences, 2020, Vol.226, p.3010

Ort / Verlag

Les Ulis: EDP Sciences

Erscheinungsjahr

2020

Quelle

Elektronische Zeitschriftenbibliothek - Freely accessible e-journals

Beschreibungen/Notizen

• Crop losses are a major threat to the wellbeing of rural families, to the economy and governments, and to food security worldwide. The goal of our research is to develop a multi-functional platform to help the farming community to tilt against plant diseases. In our previous works, we reported about the creation of a special database of healthy and diseased plants’ leaves consisting of five sets of grapes images and proposed a special classification model based on a deep siamese network followed by k -nearest neighbors (KNN) classifier. Then we extended our database to five sets of images for grape, corn, and wheat – 611 images in total. Since after this extension the classification accuracy decreased to 86 %, we propose in this paper a novel architecture with a deep siamese network as feature extractor and a single-layer perceptron as a classifier that results in a significant gain of accuracy, up to 96 %.