  Diseases and pests cause huge economic loss to the apple industry every year.
The identification of various apple diseases is challenging for the farmers as
the symptoms produced by different diseases may be very similar, and may be
present simultaneously. This paper is an attempt to provide the timely and
accurate detection and identification of apple diseases. In this study, we
propose a deep learning based approach for identification and classification of
apple diseases. The first part of the study is dataset creation which includes
data collection and data labelling. Next, we train a Convolutional Neural
Network (CNN) model on the prepared dataset for automatic classification of
apple diseases. CNNs are end-to-end learning algorithms which perform automatic
feature extraction and learn complex features directly from raw images, making
them suitable for wide variety of tasks like image classification, object
detection, segmentation etc. We applied transfer learning to initialize the
parameters of the proposed deep model. Data augmentation techniques like
rotation, translation, reflection and scaling were also applied to prevent
overfitting. The proposed CNN model obtained encouraging results, reaching
around 97.18% of accuracy on our prepared dataset. The results validate that
the proposed method is effective in classifying various types of apple diseases
and can be used as a practical tool by farmers.
