Abstract
INTRODUCTION: Automatic fruit classification is a challenging task. The types, shapes, and colors of fruits are all essential factors affecting classification. OBJECTIVES: This paper aimed to use deep learning methods to improve the overall accuracy of fruit classification, thereby improving
Highlights
Automatic fruit classification is a challenging task
Some researchers proposed some classic methods for the problem of fruit classification
The main contribution of the research is that the recognition system can identify images of different kinds of fruits mixed and further improve the accuracy of fruit classification
Summary
The traditional fruit sorting methods, which rely on manpower, consume a lot of time and labor. Some researchers proposed some classic methods for the problem of fruit classification. S [6] proposed a sixlayer (6L) convolutional neural network for fruit classification. The methods above have achieved good results, they still have several flaws They need to collect features in advance and preprocess the images, which will waste lots of time. The main contribution of the research is that the recognition system can identify images of different kinds of fruits mixed and further improve the accuracy of fruit classification. It reduces the classification time, thereby reducing the cost of classification. The experimental dataset of this paper comes from the following three channels:
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