Apple scab detection using CNN and Transfer Learning
Laen...
Kuupäev
2021
Kättesaadav alates
Ajakirja pealkiri
Ajakirja ISSN
Köite pealkiri
Kirjastaja
Abstrakt
The goal of smart and precise horticulture is to increase yield and product quality by
simultaneous reduction of pesticide application, thereby promoting the improvement of food
security. The scope of this research is apple scab detection in the early stage of development using
mobile phones and artificial intelligence based on convolutional neural network (CNN)
applications. The research considers data acquisition and CNN training. Two datasets were
collected - with images of scab infected fruits and leaves of an apple tree. However, data
acquisition is a time-consuming process and scab appearance has a probability factor. Therefore,
transfer learning is an appropriate training methodology. The goal of this research was to select
the most suitable dataset for transfer learning for the apple scab detection domain and to evaluate
the transfer learning impact comparing it with learning from scratch. The statistical analysis
confirmed the positive effect of transfer learning on CNN performance with significance
level 0.05.
Kirjeldus
Received: January 11th, 2021 ; Accepted: April 10th, 2021 ; Published: April 22nd, 2021 ; Correspondence: sergejs.kodors@rta.lv
Märksõnad
agriculture, artificial intelligence, deep learning, fungus, machine learning, Malus, pathogen, precise horticulture, Venturia, articles