Apple scab detection using CNN and Transfer Learning
¹Rezekne Academy of Technologies, Faculty of Engineering, Institute of Engineering, Atbrivoshanas Str. 115, LV-4601 Rezekne, Latvia
²Institute of Horticulture, Graudu Str. 1, LV-3701 Ceriņi, Krimūnu pagasts, Dobeles novads, Latvia
*Correspondence: sergejs.kodors@rta.lv
Abstract:
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.
Key words:
agriculture., artificial intelligence, deep learning, fungus, machine learning, Malus, pathogen, precise horticulture, Venturia