Classification of Apple Disease Based on Non-Linear Deep Features

Hamail Ayaz,Diego Oliva,Erick Rodríguez-Esparza,Muhammad Ahmad,Marco Pérez-Cisneros,Ram Sarkar

doi:10.3390/app11146422

Abstract

Diseases in apple orchards (rot, scab, and blotch) worldwide cause a substantial loss in the agricultural industry. Traditional hand picking methods are subjective to human efforts. Conventional machine learning methods for apple disease classification depend on hand-crafted features that are not robust and are complex. Advanced artificial methods such as Convolutional Neural Networks (CNN’s) have become a promising way for achieving higher accuracy although they need a high volume of samples. This work investigates different Deep CNN (DCNN) applications to apple disease classification using deep generative images to obtain higher accuracy. In order to achieve this, our work progressively modifies a baseline model by using an end-to-end trained DCNN model that has fewer parameters, better recognition accuracy than existing models (i.e., ResNet, SqeezeNet, and MiniVGGNet). We have performed a comparative study with state-of-the-art CNN as well as conventional methods proposed in the literature, and comparative results confirm the superiority of our proposed model.

Highlights

The apple is known as one of the most important tree fruits, due to its second place in world fruit production [1,2]
There are many apple diseases according to a phytopathology datasheet [5,6], but the most common diseases are Blotch, Rot, and Scab
This section discusses the deep learning architecture used for the classification of apple diseases

Summary

Introduction

The apple is known as one of the most important tree fruits, due to its second place in world fruit production [1,2]. In the year 2017, the annual production of apples worldwide reached 83.1 million tons and consumed heavily around the world [1,3]. It is estimated that approximately 33% of apples produced worldwide are processed to make juices, ciders, applesauce, alcoholic beverages, and dried apples, among other products [4]. The production of the apple industry is facing significant loss due to diseases that cause poor quality of the product. Minimal observation through a naked eye can distinguish and identify the diseased apple from the rest. An accurate and timely diagnosis of diseases is a fundamental and extremely critical process to avoid future losses. There are many apple diseases according to a phytopathology datasheet [5,6], but the most common diseases are Blotch, Rot, and Scab

Methods

Results

Conclusion